Mercurial > repos > fubar > differential_count_models

--- a/rgToolFactory.py	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,638 +0,0 @@
-# rgToolFactory.py
-# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
-#
-# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012
-#
-# all rights reserved
-# Licensed under the LGPL
-# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
-#
-# march 2014
-# added ghostscript and graphicsmagick as dependencies
-# fixed a wierd problem where gs was trying to use the new_files_path from universe (database/tmp) as ./database/tmp
-# errors ensued
-#
-# august 2013
-# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn
-#
-# july 2013
-# added ability to combine images and individual log files into html output
-# just make sure there's a log file foo.log and it will be output
-# together with all images named like "foo_*.pdf
-# otherwise old format for html
-#
-# January 2013
-# problem pointed out by Carlos Borroto
-# added escaping for <>$ - thought I did that ages ago...
-#
-# August 11 2012
-# changed to use shell=False and cl as a sequence
-
-# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye.
-# It also serves as the wrapper for the new tool.
-#
-# you paste and run your script
-# Only works for simple scripts that read one input from the history.
-# Optionally can write one new history dataset,
-# and optionally collect any number of outputs into links on an autogenerated HTML page.
-
-# DO NOT install on a public or important site - please.
-
-# installed generated tools are fine if the script is safe.
-# They just run normally and their user cannot do anything unusually insecure
-# but please, practice safe toolshed.
-# Read the fucking code before you install any tool
-# especially this one
-
-# After you get the script working on some test data, you can
-# optionally generate a toolshed compatible gzip file
-# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for
-# safe and largely automated installation in a production Galaxy.
-
-# If you opt for an HTML output, you get all the script outputs arranged
-# as a single Html history item - all output files are linked, thumbnails for all the pdfs.
-# Ugly but really inexpensive.
-#
-# Patches appreciated please.
-#
-#
-# long route to June 2012 product
-# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them
-# derived from an integrated script model
-# called rgBaseScriptWrapper.py
-# Note to the unwary:
-#   This tool allows arbitrary scripting on your Galaxy as the Galaxy user
-#   There is nothing stopping a malicious user doing whatever they choose
-#   Extremely dangerous!!
-#   Totally insecure. So, trusted users only
-#
-# preferred model is a developer using their throw away workstation instance - ie a private site.
-# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool.
-#
-
-import sys
-import shutil
-import subprocess
-import os
-import time
-import tempfile
-import optparse
-import tarfile
-import re
-import shutil
-import math
-
-progname = os.path.split(sys.argv[0])[1]
-myversion = 'V001.1 March 2014'
-verbose = False
-debug = False
-toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory'
-
-def timenow():
-    """return current time as a string
-    """
-    return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time()))
-
-html_escape_table = {
-     "&": "&amp;",
-     ">": "&gt;",
-     "<": "&lt;",
-     "$": "\$"
-     }
-
-def html_escape(text):
-     """Produce entities within text."""
-     return "".join(html_escape_table.get(c,c) for c in text)
-
-def cmd_exists(cmd):
-     return subprocess.call("type " + cmd, shell=True,
-           stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0
-
-
-class ScriptRunner:
-    """class is a wrapper for an arbitrary script
-    """
-
-    def __init__(self,opts=None,treatbashSpecial=True):
-        """
-        cleanup inputs, setup some outputs
-
-        """
-        self.useGM = cmd_exists('gm')
-        self.useIM = cmd_exists('convert')
-        self.useGS = cmd_exists('gs')
-        self.temp_warned = False # we want only one warning if $TMP not set
-        self.treatbashSpecial = treatbashSpecial
-        if opts.output_dir: # simplify for the tool tarball
-            os.chdir(opts.output_dir)
-        self.thumbformat = 'png'
-        self.opts = opts
-        self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but..
-        self.toolid = self.toolname
-        self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later
-        self.pyfile = self.myname # crude but efficient - the cruft won't hurt much
-        self.xmlfile = '%s.xml' % self.toolname
-        s = open(self.opts.script_path,'r').readlines()
-        s = [x.rstrip() for x in s] # remove pesky dos line endings if needed
-        self.script = '\n'.join(s)
-        fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter))
-        tscript = open(self.sfile,'w') # use self.sfile as script source for Popen
-        tscript.write(self.script)
-        tscript.close()
-        self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help
-        self.escapedScript = '\n'.join([html_escape(x) for x in s])
-        self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname)
-        if opts.output_dir: # may not want these complexities
-            self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname)
-            art = '%s.%s' % (self.toolname,opts.interpreter)
-            artpath = os.path.join(self.opts.output_dir,art) # need full path
-            artifact = open(artpath,'w') # use self.sfile as script source for Popen
-            artifact.write(self.script)
-            artifact.close()
-        self.cl = []
-        self.html = []
-        a = self.cl.append
-        a(opts.interpreter)
-        if self.treatbashSpecial and opts.interpreter in ['bash','sh']:
-            a(self.sfile)
-        else:
-            a('-') # stdin
-        a(opts.input_tab)
-        a(opts.output_tab)
-        self.outFormats = 'tabular' # TODO make this an option at tool generation time
-        self.inputFormats = 'tabular' # TODO make this an option at tool generation time
-        self.test1Input = '%s_test1_input.xls' % self.toolname
-        self.test1Output = '%s_test1_output.xls' % self.toolname
-        self.test1HTML = '%s_test1_output.html' % self.toolname
-
-    def makeXML(self):
-        """
-        Create a Galaxy xml tool wrapper for the new script as a string to write out
-        fixme - use templating or something less fugly than this example of what we produce
-
-        <tool id="reverse" name="reverse" version="0.01">
-            <description>a tabular file</description>
-            <command interpreter="python">
-            reverse.py --script_path "$runMe" --interpreter "python"
-            --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file"
-            </command>
-            <inputs>
-            <param name="input1"  type="data" format="tabular" label="Select a suitable input file from your history"/><param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="reverse"/>
-
-            </inputs>
-            <outputs>
-            <data format="tabular" name="tab_file" label="${job_name}"/>
-
-            </outputs>
-            <help>
-
-**What it Does**
-
-Reverse the columns in a tabular file
-
-            </help>
-            <configfiles>
-            <configfile name="runMe">
-
-# reverse order of columns in a tabular file
-import sys
-inp = sys.argv[1]
-outp = sys.argv[2]
-i = open(inp,'r')
-o = open(outp,'w')
-for row in i:
-     rs = row.rstrip().split('\t')
-     rs.reverse()
-     o.write('\t'.join(rs))
-     o.write('\n')
-i.close()
-o.close()
-
-
-            </configfile>
-            </configfiles>
-            </tool>
-
-        """
-        newXML="""<tool id="%(toolid)s" name="%(toolname)s" version="%(tool_version)s">
-            %(tooldesc)s
-            %(command)s
-            <inputs>
-            %(inputs)s
-            </inputs>
-            <outputs>
-            %(outputs)s
-            </outputs>
-            <configfiles>
-            <configfile name="runMe">
-            %(script)s
-            </configfile>
-            </configfiles>
-            %(tooltests)s
-            <help>
-            %(help)s
-            </help>
-            </tool>""" # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto
-
-        newCommand="""<command interpreter="python">
-            %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s"
-            --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s
-            </command>""" # may NOT be an input or htmlout
-        tooltestsTabOnly = """<tests><test>
-        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
-        <param name="job_name" value="test1"/>
-        <param name="runMe" value="$runMe"/>
-        <output name="tab_file" file="%(test1Output)s" ftype="tabular"/>
-        </test></tests>"""
-        tooltestsHTMLOnly = """<tests><test>
-        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
-        <param name="job_name" value="test1"/>
-        <param name="runMe" value="$runMe"/>
-        <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="5"/>
-        </test></tests>"""
-        tooltestsBoth = """<tests><test>
-        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
-        <param name="job_name" value="test1"/>
-        <param name="runMe" value="$runMe"/>
-        <output name="tab_file" file="%(test1Output)s" ftype="tabular" />
-        <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="10"/>
-        </test></tests>"""
-        xdict = {}
-        xdict['tool_version'] = self.opts.tool_version
-        xdict['test1Input'] = self.test1Input
-        xdict['test1HTML'] = self.test1HTML
-        xdict['test1Output'] = self.test1Output
-        if self.opts.make_HTML and self.opts.output_tab <> 'None':
-            xdict['tooltests'] = tooltestsBoth % xdict
-        elif self.opts.make_HTML:
-            xdict['tooltests'] = tooltestsHTMLOnly % xdict
-        else:
-            xdict['tooltests'] = tooltestsTabOnly % xdict
-        xdict['script'] = self.escapedScript
-        # configfile is least painful way to embed script to avoid external dependencies
-        # but requires escaping of <, > and $ to avoid Mako parsing
-        if self.opts.help_text:
-            xdict['help'] = open(self.opts.help_text,'r').read()
-        else:
-            xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation'
-        coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::']
-        coda.append(self.indentedScript)
-        coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow()))
-        coda.append('See %s for details of that project' % (toolFactoryURL))
-        coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ')
-        coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573')
-        xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda))
-        if self.opts.tool_desc:
-            xdict['tooldesc'] = '<description>%s</description>' % self.opts.tool_desc
-        else:
-            xdict['tooldesc'] = ''
-        xdict['command_outputs'] = ''
-        xdict['outputs'] = ''
-        if self.opts.input_tab <> 'None':
-            xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something
-            xdict['inputs'] = '<param name="input1"  type="data" format="%s" label="Select a suitable input file from your history"/> \n' % self.inputFormats
-        else:
-            xdict['command_inputs'] = '' # assume no input - eg a random data generator
-            xdict['inputs'] = ''
-        xdict['inputs'] += '<param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="%s"/> \n' % self.toolname
-        xdict['toolname'] = self.toolname
-        xdict['toolid'] = self.toolid
-        xdict['interpreter'] = self.opts.interpreter
-        xdict['scriptname'] = self.sfile
-        if self.opts.make_HTML:
-            xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" '
-            xdict['outputs'] +=  ' <data format="html" name="html_file" label="${job_name}.html"/>\n'
-        if self.opts.output_tab <> 'None':
-            xdict['command_outputs'] += ' --output_tab "$tab_file"'
-            xdict['outputs'] += ' <data format="%s" name="tab_file" label="${job_name}"/>\n' % self.outFormats
-        xdict['command'] = newCommand % xdict
-        xmls = newXML % xdict
-        xf = open(self.xmlfile,'w')
-        xf.write(xmls)
-        xf.write('\n')
-        xf.close()
-        # ready for the tarball
-
-
-    def makeTooltar(self):
-        """
-        a tool is a gz tarball with eg
-        /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ...
-        """
-        retval = self.run()
-        if retval:
-            print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry'
-            sys.exit(1)
-        self.makeXML()
-        tdir = self.toolname
-        os.mkdir(tdir)
-        if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe..
-            testdir = os.path.join(tdir,'test-data')
-            os.mkdir(testdir) # make tests directory
-            shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input))
-            if self.opts.output_tab <> 'None':
-                shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output))
-            if self.opts.make_HTML:
-                shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML))
-            if self.opts.output_dir:
-                shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log'))
-        op = '%s.py' % self.toolname # new name
-        outpiname = os.path.join(tdir,op) # path for the tool tarball
-        pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM)
-        notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),]
-        notes.append('# to make a new Galaxy tool called %s\n' % self.toolname)
-        notes.append('# User %s at %s\n' % (self.opts.user_email,timenow()))
-        pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm
-        notes += pi
-        outpi = open(outpiname,'w')
-        outpi.write(''.join(notes))
-        outpi.write('\n')
-        outpi.close()
-        stname = os.path.join(tdir,self.sfile)
-        if not os.path.exists(stname):
-            shutil.copyfile(self.sfile, stname)
-        xtname = os.path.join(tdir,self.xmlfile)
-        if not os.path.exists(xtname):
-            shutil.copyfile(self.xmlfile,xtname)
-        tarpath = "%s.gz" % self.toolname
-        tar = tarfile.open(tarpath, "w:gz")
-        tar.add(tdir,arcname=self.toolname)
-        tar.close()
-        shutil.copyfile(tarpath,self.opts.new_tool)
-        shutil.rmtree(tdir)
-        ## TODO: replace with optional direct upload to local toolshed?
-        return retval
-
-
-    def compressPDF(self,inpdf=None,thumbformat='png'):
-        """need absolute path to pdf
-           note that GS gets confoozled if no $TMP or $TEMP
-           so we set it
-        """
-        assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName)
-        hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf))
-        sto = open(hlog,'a')
-        our_env = os.environ.copy()
-        our_tmp = our_env.get('TMP',None)
-        if not our_tmp:
-            our_tmp = our_env.get('TEMP',None)
-        if not (our_tmp and os.path.exists(our_tmp)):
-            newtmp = os.path.join(self.opts.output_dir,'tmp')
-            try:
-                os.mkdir(newtmp)
-            except:
-                sto.write('## WARNING - cannot make %s - it may exist or permissions need fixing\n' % newtmp)
-            our_env['TEMP'] = newtmp
-            if not self.temp_warned:
-               sto.write('## WARNING - no $TMP or $TEMP!!! Please fix - using %s temporarily\n' % newtmp)
-               self.temp_warned = True
-        outpdf = '%s_compressed' % inpdf
-        cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dUseCIEColor", "-dBATCH","-dPDFSETTINGS=/printer", "-sOutputFile=%s" % outpdf,inpdf]
-        x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
-        retval1 = x.wait()
-        sto.close()
-        if retval1 == 0:
-            os.unlink(inpdf)
-            shutil.move(outpdf,inpdf)
-            os.unlink(hlog)
-        hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf))
-        sto = open(hlog,'w')
-        outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat)
-        if self.useGM:
-            cl2 = ['gm', 'convert', inpdf, outpng]
-        else: # assume imagemagick
-            cl2 = ['convert', inpdf, outpng]
-        x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
-        retval2 = x.wait()
-        sto.close()
-        if retval2 == 0:
-             os.unlink(hlog)
-        retval = retval1 or retval2
-        return retval
-
-
-    def getfSize(self,fpath,outpath):
-        """
-        format a nice file size string
-        """
-        size = ''
-        fp = os.path.join(outpath,fpath)
-        if os.path.isfile(fp):
-            size = '0 B'
-            n = float(os.path.getsize(fp))
-            if n > 2**20:
-                size = '%1.1f MB' % (n/2**20)
-            elif n > 2**10:
-                size = '%1.1f KB' % (n/2**10)
-            elif n > 0:
-                size = '%d B' % (int(n))
-        return size
-
-    def makeHtml(self):
-        """ Create an HTML file content to list all the artifacts found in the output_dir
-        """
-
-        galhtmlprefix = """<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
-        <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-        <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-        <meta name="generator" content="Galaxy %s tool output - see http://getgalaxy.org/" />
-        <title></title>
-        <link rel="stylesheet" href="/static/style/base.css" type="text/css" />
-        </head>
-        <body>
-        <div class="toolFormBody">
-        """
-        galhtmlattr = """<hr/><div class="infomessage">This tool (%s) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>"""
-        galhtmlpostfix = """</div></body></html>\n"""
-
-        flist = os.listdir(self.opts.output_dir)
-        flist = [x for x in flist if x <> 'Rplots.pdf']
-        flist.sort()
-        html = []
-        html.append(galhtmlprefix % progname)
-        html.append('<div class="infomessage">Galaxy Tool "%s" run at %s</div><br/>' % (self.toolname,timenow()))
-        fhtml = []
-        if len(flist) > 0:
-            logfiles = [x for x in flist if x.lower().endswith('.log')] # log file names determine sections
-            logfiles.sort()
-            logfiles = [x for x in logfiles if os.path.abspath(x) <> os.path.abspath(self.tlog)]
-            logfiles.append(os.path.abspath(self.tlog)) # make it the last one
-            pdflist = []
-            npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf'])
-            for rownum,fname in enumerate(flist):
-                dname,e = os.path.splitext(fname)
-                sfsize = self.getfSize(fname,self.opts.output_dir)
-                if e.lower() == '.pdf' : # compress and make a thumbnail
-                    thumb = '%s.%s' % (dname,self.thumbformat)
-                    pdff = os.path.join(self.opts.output_dir,fname)
-                    retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat)
-                    if retval == 0:
-                        pdflist.append((fname,thumb))
-                    else:
-                        pdflist.append((fname,fname))
-                if (rownum+1) % 2 == 0:
-                    fhtml.append('<tr class="odd_row"><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize))
-                else:
-                    fhtml.append('<tr><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize))
-            for logfname in logfiles: # expect at least tlog - if more
-                if os.path.abspath(logfname) == os.path.abspath(self.tlog): # handled later
-                    sectionname = 'All tool run'
-                    if (len(logfiles) > 1):
-                        sectionname = 'Other'
-                    ourpdfs = pdflist
-                else:
-                    realname = os.path.basename(logfname)
-                    sectionname = os.path.splitext(realname)[0].split('_')[0] # break in case _ added to log
-                    ourpdfs = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] == sectionname]
-                    pdflist = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] <> sectionname] # remove
-                nacross = 1
-                npdf = len(ourpdfs)
-
-                if npdf > 0:
-                    nacross = math.sqrt(npdf) ## int(round(math.log(npdf,2)))
-                    if int(nacross)**2 != npdf:
-                        nacross += 1
-                    nacross = int(nacross)
-                    width = min(400,int(1200/nacross))
-                    html.append('<div class="toolFormTitle">%s images and outputs</div>' % sectionname)
-                    html.append('(Click on a thumbnail image to download the corresponding original PDF image)<br/>')
-                    ntogo = nacross # counter for table row padding with empty cells
-                    html.append('<div><table class="simple" cellpadding="2" cellspacing="2">\n<tr>')
-                    for i,paths in enumerate(ourpdfs):
-                        fname,thumb = paths
-                        s= """<td><a href="%s"><img src="%s" title="Click to download a PDF of %s" hspace="5" width="%d"
-                           alt="Image called %s"/></a></td>\n""" % (fname,thumb,fname,width,fname)
-                        if ((i+1) % nacross == 0):
-                            s += '</tr>\n'
-                            ntogo = 0
-                            if i < (npdf - 1): # more to come
-                               s += '<tr>'
-                               ntogo = nacross
-                        else:
-                            ntogo -= 1
-                        html.append(s)
-                    if html[-1].strip().endswith('</tr>'):
-                        html.append('</table></div>\n')
-                    else:
-                        if ntogo > 0: # pad
-                           html.append('<td>&nbsp;</td>'*ntogo)
-                        html.append('</tr></table></div>\n')
-                logt = open(logfname,'r').readlines()
-                logtext = [x for x in logt if x.strip() > '']
-                html.append('<div class="toolFormTitle">%s log output</div>' % sectionname)
-                if len(logtext) > 1:
-                    html.append('\n<pre>\n')
-                    html += logtext
-                    html.append('\n</pre>\n')
-                else:
-                    html.append('%s is empty<br/>' % logfname)
-        if len(fhtml) > 0:
-           fhtml.insert(0,'<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>\n')
-           fhtml.append('</table></div><br/>')
-           html.append('<div class="toolFormTitle">All output files available for downloading</div>\n')
-           html += fhtml # add all non-pdf files to the end of the display
-        else:
-            html.append('<div class="warningmessagelarge">### Error - %s returned no files - please confirm that parameters are sane</div>' % self.opts.interpreter)
-        html.append(galhtmlpostfix)
-        htmlf = file(self.opts.output_html,'w')
-        htmlf.write('\n'.join(html))
-        htmlf.write('\n')
-        htmlf.close()
-        self.html = html
-
-
-    def run(self):
-        """
-        scripts must be small enough not to fill the pipe!
-        """
-        if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']:
-          retval = self.runBash()
-        else:
-            if self.opts.output_dir:
-                ste = open(self.elog,'w')
-                sto = open(self.tlog,'w')
-                sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl))
-                sto.flush()
-                p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir)
-            else:
-                p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE)
-            p.stdin.write(self.script)
-            p.stdin.close()
-            retval = p.wait()
-            if self.opts.output_dir:
-                sto.close()
-                ste.close()
-                err = open(self.elog,'r').read()
-                if retval <> 0 and err: # problem
-                    print >> sys.stderr,err
-            if self.opts.make_HTML:
-                self.makeHtml()
-        return retval
-
-    def runBash(self):
-        """
-        cannot use - for bash so use self.sfile
-        """
-        if self.opts.output_dir:
-            s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl)
-            sto = open(self.tlog,'w')
-            sto.write(s)
-            sto.flush()
-            p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir)
-        else:
-            p = subprocess.Popen(self.cl,shell=False)
-        retval = p.wait()
-        if self.opts.output_dir:
-            sto.close()
-        if self.opts.make_HTML:
-            self.makeHtml()
-        return retval
-
-
-def main():
-    u = """
-    This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as:
-    <command interpreter="python">rgToolFactory.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript"
-    </command>
-    """
-    op = optparse.OptionParser()
-    a = op.add_option
-    a('--script_path',default=None)
-    a('--tool_name',default=None)
-    a('--interpreter',default=None)
-    a('--output_dir',default=None)
-    a('--output_html',default=None)
-    a('--input_tab',default="None")
-    a('--output_tab',default="None")
-    a('--user_email',default='Unknown')
-    a('--bad_user',default=None)
-    a('--make_Tool',default=None)
-    a('--make_HTML',default=None)
-    a('--help_text',default=None)
-    a('--tool_desc',default=None)
-    a('--new_tool',default=None)
-    a('--tool_version',default=None)
-    opts, args = op.parse_args()
-    assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user)
-    assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq'
-    assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript'
-    assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R'
-    if opts.output_dir:
-        try:
-            os.makedirs(opts.output_dir)
-        except:
-            pass
-    r = ScriptRunner(opts)
-    if opts.make_Tool:
-        retcode = r.makeTooltar()
-    else:
-        retcode = r.run()
-    os.unlink(r.sfile)
-    if retcode:
-        sys.exit(retcode) # indicate failure to job runner
-
-
-if __name__ == "__main__":
-    main()
-
-
--- a/rgedgeRpaired.xml.camera	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1084 +0,0 @@
-<tool id="rgDifferentialCount" name="Differential_Count" version="0.30">
-  <description>models using BioConductor packages</description>
-  <requirements>
-      <requirement type="package" version="2.14">biocbasics</requirement>
-      <requirement type="package" version="3.0.2">r302</requirement>
-      <requirement type="package" version="1.3.18">graphicsmagick</requirement>
-      <requirement type="package" version="9.10">ghostscript</requirement>
-  </requirements>
-
-  <command interpreter="python">
-     rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts"
-    --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
-  </command>
-  <inputs>
-    <param name="input1"  type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"
-       help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>
-    <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs"
-           help="Supply a meaningful name here to remind you what the outputs contain">
-      <sanitizer invalid_char="">
-        <valid initial="string.letters,string.digits"><add value="_" /> </valid>
-      </sanitizer>
-    </param>
-    <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>
-    <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True"
-         multiple="true" use_header_names="true" size="120" display="checkboxes">
-        <validator type="no_options" message="Please select at least one column."/>
-    </param>
-    <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>
-    <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True"
-         multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true">
-    </param>
-    <param name="subjectids" type="text" optional="true" size="120" value = ""
-       label="IF SUBJECTS NOT ALL INDEPENDENT! Enter comma separated strings to indicate sample labels for (eg) pairing - must be one for every column in input"
-       help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter 'A99,C21,A99,C21'">
-      <sanitizer>
-        <valid initial="string.letters,string.digits"><add value="," /> </valid>
-      </sanitizer>
-    </param>
-    <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs"
-     help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/>
-    <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1"
-              label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"
-     help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/>
-
-    <conditional name="edgeR">
-        <param name="doedgeR" type="select"
-           label="Run this model using edgeR"
-           help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates">
-          <option value="F">Do not run edgeR</option>
-          <option value="T" selected="true">Run edgeR</option>
-         </param>
-         <when value="T">
-          <param name="edgeR_priordf" type="integer" value="20" size="3"
-           label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance. Replaces prior.n  and prior.df = prior.n * residual.df"
-           help="0 = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/>
-         </when>
-         <when value="F"></when>
-    </conditional>
-    <conditional name="DESeq2">
-    <param name="doDESeq2" type="select"
-       label="Run the same model with DESeq2 and compare findings"
-       help="DESeq2 is an update to the DESeq package. It uses different assumptions and methods to edgeR">
-      <option value="F" selected="true">Do not run DESeq2</option>
-      <option value="T">Run DESeq2</option>
-     </param>
-     <when value="T">
-         <param name="DESeq_fitType" type="select">
-            <option value="parametric" selected="true">Parametric (default) fit for dispersions</option>
-            <option value="local">Local fit - this will automagically be used if parametric fit fails</option>
-            <option value="mean">Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation</option>
-         </param>
-     </when>
-     <when value="F"> </when>
-    </conditional>
-    <param name="doVoom" type="select"
-       label="Run the same model with Voom/limma and compare findings"
-       help="Voom uses counts per million and a precise transformation of variance so count data can be analysed using limma">
-      <option value="F" selected="true">Do not run VOOM</option>
-      <option value="T">Run VOOM</option>
-     </param>
-    <!--
-    <conditional name="camera">
-        <param name="doCamera" type="select" label="Run the edgeR implementation of Camera GSEA for up/down gene sets"
-            help="If yes, you can choose a set of genesets to test and/or supply a gmt format geneset collection from your history">
-        <option value="F" selected="true">Do not run GSEA tests with the Camera algorithm</option>
-        <option value="T">Run GSEA tests with the Camera algorithm</option>
-        </param>
-         <when value="T">
-         <conditional name="gmtSource">
-          <param name="refgmtSource" type="select"
-             label="Use a gene set (.gmt) from your history and/or use a built-in (MSigDB etc) gene set">
-            <option value="indexed" selected="true">Use a built-in gene set</option>
-            <option value="history">Use a gene set from my history</option>
-            <option value="both">Add a gene set from my history to a built in gene set</option>
-          </param>
-          <when value="indexed">
-            <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis">
-              <options from_data_table="gseaGMT_3.1">
-                <filter type="sort_by" column="2" />
-                <validator type="no_options" message="No GMT v3.1 files are available - please install them"/>
-              </options>
-            </param>
-          </when>
-          <when value="history">
-            <param name="ownGMT" type="data" format="gmt" label="Select a Gene Set from your history" />
-          </when>
-          <when value="both">
-            <param name="ownGMT" type="data" format="gseagmt" label="Select a Gene Set from your history" />
-            <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis">
-              <options from_data_table="gseaGMT_4">
-                <filter type="sort_by" column="2" />
-                <validator type="no_options" message="No GMT v4 files are available - please fix tool_data_table and loc files"/>
-              </options>
-            </param>
-           </when>
-         </conditional>
-         </when>
-         <when value="F">
-         </when>
-    </conditional>
-    -->
-    <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"
-     help="Conventional default value of 0.05 recommended"/>
-    <param name="fdrtype" type="select" label="FDR (Type II error) control method"
-         help="Use fdr or bh typically to control for the number of tests in a reliable way">
-            <option value="fdr" selected="true">fdr</option>
-            <option value="BH">Benjamini Hochberg</option>
-            <option value="BY">Benjamini Yukateli</option>
-            <option value="bonferroni">Bonferroni</option>
-            <option value="hochberg">Hochberg</option>
-            <option value="holm">Holm</option>
-            <option value="hommel">Hommel</option>
-            <option value="none">no control for multiple tests</option>
-    </param>
-  </inputs>
-  <outputs>
-    <data format="tabular" name="out_edgeR" label="${title}_topTable_edgeR.xls">
-         <filter>edgeR['doedgeR'] == "T"</filter>
-    </data>
-    <data format="tabular" name="out_DESeq2" label="${title}_topTable_DESeq2.xls">
-         <filter>DESeq2['doDESeq2'] == "T"</filter>
-    </data>
-    <data format="tabular" name="out_VOOM" label="${title}_topTable_VOOM.xls">
-         <filter>doVoom == "T"</filter>
-    </data>
-    <data format="html" name="html_file" label="${title}.html"/>
-  </outputs>
- <stdio>
-     <exit_code range="4"   level="fatal"   description="Number of subject ids must match total number of samples in the input matrix" />
- </stdio>
- <tests>
-<test>
-<param name='input1' value='test_bams2mx.xls' ftype='tabular' />
- <param name='treatment_name' value='liver' />
- <param name='title' value='edgeRtest' />
- <param name='useNDF' value='' />
- <param name='doedgeR' value='T' />
- <param name='doVoom' value='T' />
- <param name='doDESeq2' value='T' />
- <param name='fdrtype' value='fdr' />
- <param name='edgeR_priordf' value="8" />
- <param name='fdrthresh' value="0.05" />
- <param name='control_name' value='heart' />
- <param name='subjectids' value='' />
- <param name='Control_cols' value='3,4,5,9' />
- <param name='Treat_cols' value='2,6,7,8' />
- <output name='out_edgeR' file='edgeRtest1out.xls' compare='diff' />
- <output name='html_file' file='edgeRtest1out.html'  compare='diff' lines_diff='20' />
-</test>
-</tests>
-
-<configfiles>
-<configfile name="runme">
-<![CDATA[
-#
-# edgeR.Rscript
-# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross
-# Performs DGE on a count table containing n replicates of two conditions
-#
-# Parameters
-#
-# 1 - Output Dir
-
-# Original edgeR code by: S.Lunke and A.Kaspi
-reallybig = log10(.Machine\$double.xmax)
-reallysmall = log10(.Machine\$double.xmin)
-library('stringr')
-library('gplots')
-library('edgeR')
-hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here')
-{
-# Perform clustering for significant pvalues after controlling FWER
-    samples = colnames(cmat)
-    gu = unique(group)
-    gn = rownames(cmat)
-    if (length(gu) == 2) {
-        col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"}
-        pcols = unlist(lapply(group,col.map))
-        } else {
-        colours = rainbow(length(gu),start=0,end=4/6)
-        pcols = colours[match(group,gu)]        }
-    dm = cmat[(! is.na(gn)),]
-    # remove unlabelled hm rows
-    nprobes = nrow(dm)
-    # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance')
-    if (nprobes > nsamp) {
-      dm =dm[1:nsamp,]
-      #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total')
-    }
-    newcolnames = substr(colnames(dm),1,20)
-    colnames(dm) = newcolnames
-    pdf(outpdfname)
-    heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none',
-         Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5)
-    dev.off()
-}
-
-hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here")
-{
-    # for 2 groups only was
-    #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"}
-    #pcols = unlist(lapply(group,col.map))
-    gu = unique(group)
-    colours = rainbow(length(gu),start=0.3,end=0.6)
-    pcols = colours[match(group,gu)]
-    nrows = nrow(cmat)
-    mtitle = paste(myTitle,'Heatmap: n contigs =',nrows)
-    if (nrows > nsamp)  {
-               cmat = cmat[c(1:nsamp),]
-               mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='')
-          }
-    newcolnames = substr(colnames(cmat),1,20)
-    colnames(cmat) = newcolnames
-    pdf(outpdfname)
-    heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols)
-    dev.off()
-}
-
-qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...)
-# stolen from https://gist.github.com/703512
-{
-    o = -log10(sort(pvector,decreasing=F))
-    e = -log10( 1:length(o)/length(o) )
-    o[o==-Inf] = reallysmall
-    o[o==Inf] = reallybig
-    maint = descr
-    pdf(outpdf)
-    plot(e,o,pch=19,cex=1, main=maint, ...,
-        xlab=expression(Expected~~-log[10](italic(p))),
-        ylab=expression(Observed~~-log[10](italic(p))),
-        xlim=c(0,max(e)), ylim=c(0,max(o)))
-    lines(e,e,col="red")
-    grid(col = "lightgray", lty = "dotted")
-    dev.off()
-}
-
-smearPlot = function(DGEList,deTags, outSmear, outMain)
-        {
-        pdf(outSmear)
-        plotSmear(DGEList,de.tags=deTags,main=outMain)
-        grid(col="lightgray", lty="dotted")
-        dev.off()
-        }
-
-boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname)
-{   #
-        nc = ncol(rawrs)
-        for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA}
-        fullnames = colnames(rawrs)
-        newcolnames = substr(colnames(rawrs),1,20)
-        colnames(rawrs) = newcolnames
-        newcolnames = substr(colnames(cleanrs),1,20)
-        colnames(cleanrs) = newcolnames
-        defpar = par(no.readonly=T)
-        print.noquote('raw contig counts by sample:')
-        print.noquote(summary(rawrs))
-        print.noquote('normalised contig counts by sample:')
-        print.noquote(summary(cleanrs))
-        pdf(pdfname)
-        par(mfrow=c(1,2))
-        boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint))
-        grid(col="lightgray",lty="dotted")
-        boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint))
-        grid(col="lightgray",lty="dotted")
-        dev.off()
-        pdfname = "sample_counts_histogram.pdf"
-        nc = ncol(rawrs)
-        print.noquote(paste('Using ncol rawrs=',nc))
-        ncroot = round(sqrt(nc))
-        if (ncroot*ncroot < nc) { ncroot = ncroot + 1 }
-        m = c()
-        for (i in c(1:nc)) {
-              rhist = hist(rawrs[,i],breaks=100,plot=F)
-              m = append(m,max(rhist\$counts))
-             }
-        ymax = max(m)
-        ncols = length(fullnames)
-        if (ncols > 20)
-        {
-           scale = 7*ncols/20
-           pdf(pdfname,width=scale,height=scale)
-        } else {
-           pdf(pdfname)
-        }
-        par(mfrow=c(ncroot,ncroot))
-        for (i in c(1:nc)) {
-                 hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon",
-                 breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax))
-             }
-        dev.off()
-        par(defpar)
-
-}
-
-cumPlot = function(rawrs,cleanrs,maint,myTitle)
-{   # updated to use ecdf
-        pdfname = "Filtering_rowsum_bar_charts.pdf"
-        defpar = par(no.readonly=T)
-        lrs = log(rawrs,10)
-        lim = max(lrs)
-        pdf(pdfname)
-        par(mfrow=c(2,1))
-        hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)",
-             ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1)
-        grid(col="lightgray", lty="dotted")
-        lrs = log(cleanrs,10)
-        hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)",
-             ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1)
-        grid(col="lightgray", lty="dotted")
-        dev.off()
-        par(defpar)
-}
-
-cumPlot1 = function(rawrs,cleanrs,maint,myTitle)
-{   # updated to use ecdf
-        pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
-        pdf(pdfname)
-        par(mfrow=c(2,1))
-        lastx = max(rawrs)
-        rawe = knots(ecdf(rawrs))
-        cleane = knots(ecdf(cleanrs))
-        cy = 1:length(cleane)/length(cleane)
-        ry = 1:length(rawe)/length(rawe)
-        plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads",
-             ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
-        grid(col="blue")
-        plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads",
-             ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
-        grid(col="blue")
-        dev.off()
-}
-
-
-
-doGSEAold = function(y=NULL,design=NULL,histgmt="",
-                  bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
-                  ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
-{
-  sink('Camera.log')
-  genesets = c()
-  if (bigmt > "")
-  {
-    bigenesets = readLines(bigmt)
-    genesets = bigenesets
-  }
-  if (histgmt > "")
-  {
-    hgenesets = readLines(histgmt)
-    if (bigmt > "") {
-      genesets = rbind(genesets,hgenesets)
-    } else {
-      genesets = hgenesets
-    } # use only history if no bi
-  }
-  print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
-  genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
-  outf = outfname
-  head=paste(myTitle,'edgeR GSEA')
-  write(head,file=outfname,append=F)
-  ntest=length(genesets)
-  urownames = toupper(rownames(y))
-  upcam = c()
-  downcam = c()
-  for (i in 1:ntest) {
-    gs = unlist(genesets[i])
-    g = gs[1] # geneset_id
-    u = gs[2]
-    if (u > "") { u = paste("<a href=\'",u,"\'>",u,"</a>",sep="") }
-    glist = gs[3:length(gs)] # member gene symbols
-    glist = toupper(glist)
-    inglist = urownames %in% glist
-    nin = sum(inglist)
-    if ((nin > minnin) && (nin < maxnin)) {
-      ### print(paste('@@found',sum(inglist),'genes in glist'))
-      camres = camera(y=y,index=inglist,design=design)
-      if (! is.null(camres)) {
-      rownames(camres) = g # gene set name
-      camres = cbind(GeneSet=g,URL=u,camres)
-      if (camres\$Direction == "Up")
-        {
-        upcam = rbind(upcam,camres) } else {
-          downcam = rbind(downcam,camres)
-        }
-      }
-   }
-  }
-  uscam = upcam[order(upcam\$PValue),]
-  unadjp = uscam\$PValue
-  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
-  dscam = downcam[order(downcam\$PValue),]
-  unadjp = dscam\$PValue
-  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
-  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
-  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
-  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
-  sink()
-}
-
-
-
-
-doGSEA = function(y=NULL,design=NULL,histgmt="",
-                  bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
-                  ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
-{
-  sink('Camera.log')
-  genesets = c()
-  if (bigmt > "")
-  {
-    bigenesets = readLines(bigmt)
-    genesets = bigenesets
-  }
-  if (histgmt > "")
-  {
-    hgenesets = readLines(histgmt)
-    if (bigmt > "") {
-      genesets = rbind(genesets,hgenesets)
-    } else {
-      genesets = hgenesets
-    } # use only history if no bi
-  }
-  print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
-  genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
-  outf = outfname
-  head=paste(myTitle,'edgeR GSEA')
-  write(head,file=outfname,append=F)
-  ntest=length(genesets)
-  urownames = toupper(rownames(y))
-  upcam = c()
-  downcam = c()
-  incam = c()
-  urls = c()
-  gsids = c()
-  for (i in 1:ntest) {
-    gs = unlist(genesets[i])
-    gsid = gs[1] # geneset_id
-    url = gs[2]
-    if (url > "") { url = paste("<a href=\'",url,"\'>",url,"</a>",sep="") }
-    glist = gs[3:length(gs)] # member gene symbols
-    glist = toupper(glist)
-    inglist = urownames %in% glist
-    nin = sum(inglist)
-    if ((nin > minnin) && (nin < maxnin)) {
-      incam = c(incam,inglist)
-      gsids = c(gsids,gsid)
-      urls = c(urls,url)
-    }
-  }
-  incam = as.list(incam)
-  names(incam) = gsids
-  allcam = camera(y=y,index=incam,design=design)
-  allcamres = cbind(geneset=gsids,allcam,URL=urls)
-  for (i in 1:ntest) {
-    camres = allcamres[i]
-    res = try(test = (camres\$Direction == "Up"))
-    if ("try-error" %in% class(res)) {
-      cat("test failed, camres = :")
-      print.noquote(camres)
-    } else  { if (camres\$Direction == "Up")
-    {  upcam = rbind(upcam,camres)
-    } else { downcam = rbind(downcam,camres)
-    }
-
-    }
-  }
-  uscam = upcam[order(upcam\$PValue),]
-  unadjp = uscam\$PValue
-  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
-  dscam = downcam[order(downcam\$PValue),]
-  unadjp = dscam\$PValue
-  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
-  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
-  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
-  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
-  sink()
-  }
-
-
-edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5,
-        fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F,
-        filterquantile=0.2, subjects=c(),mydesign=NULL,
-        doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19',
-        histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
-        doCook=F,DESeq_fitType="parameteric")
-{
-  # Error handling
-  if (length(unique(group))!=2){
-    print("Number of conditions identified in experiment does not equal 2")
-    q()
-    }
-  require(edgeR)
-  options(width = 512)
-  mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ")
-  allN = nrow(Count_Matrix)
-  nscut = round(ncol(Count_Matrix)/2)
-  colTotmillionreads = colSums(Count_Matrix)/1e6
-  counts.dataframe = as.data.frame(c())
-  rawrs = rowSums(Count_Matrix)
-  nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes
-  nzN = nrow(nonzerod)
-  nzrs = rowSums(nonzerod)
-  zN = allN - nzN
-  print('# Quantiles for non-zero row counts:',quote=F)
-  print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F)
-  if (useNDF == T)
-  {
-    gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut
-    lo = colSums(Count_Matrix[!gt1rpin3,])
-    workCM = Count_Matrix[gt1rpin3,]
-    cleanrs = rowSums(workCM)
-    cleanN = length(cleanrs)
-    meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="")
-    print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F)
-    maint = paste('Filter >=1/million reads in >=',nscut,'samples')
-  }   else {
-    useme = (nzrs > quantile(nzrs,filterquantile))
-    workCM = nonzerod[useme,]
-    lo = colSums(nonzerod[!useme,])
-    cleanrs = rowSums(workCM)
-    cleanN = length(cleanrs)
-    meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="")
-    print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F)
-    maint = paste('Filter below',filterquantile,'quantile')
-  }
-  cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle)
-  allgenes = rownames(workCM)
-  reg = "^chr([0-9]+):([0-9]+)-([0-9]+)"
-  genecards="<a href=\'http://www.genecards.org/index.php?path=/Search/keyword/"
-  ucsc = paste("<a href=\'http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='')
-  testreg = str_match(allgenes,reg)
-  if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string
-  {
-    print("@@ using ucsc substitution for urls")
-    contigurls = paste0(ucsc,"&amp;position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"</a>")
-  } else {
-    print("@@ using genecards substitution for urls")
-    contigurls = paste0(genecards,allgenes,"\'>",allgenes,"</a>")
-  }
-  print.noquote("# urls")
-  print.noquote(head(contigurls))
-  print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F)
-  cmrowsums = rowSums(workCM)
-  TName=unique(group)[1]
-  CName=unique(group)[2]
-  if (is.null(mydesign)) {
-    if (length(subjects) == 0)
-    {
-      mydesign = model.matrix(~group)
-    }
-    else {
-      subjf = factor(subjects)
-      mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it
-    }
-  }
-  print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=',')))
-  print.noquote('Using design matrix:')
-  print.noquote(mydesign)
-  if (doedgeR) {
-  sink('edgeR.log')
-  #### Setup DGEList object
-  DGEList = DGEList(counts=workCM, group = group)
-  DGEList = calcNormFactors(DGEList)
-
-  DGEList = estimateGLMCommonDisp(DGEList,mydesign)
-  comdisp = DGEList\$common.dispersion
-  DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
-  if (edgeR_priordf > 0) {
-    print.noquote(paste("prior.df =",edgeR_priordf))
-    DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf)
-  } else {
-    DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
-  }
-  DGLM = glmFit(DGEList,design=mydesign)
-  DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed
-  efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
-  normData = (1e+06*DGEList\$counts/efflib)
-  uoutput = cbind(
-    Name=as.character(rownames(DGEList\$counts)),
-    DE\$table,
-    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
-    Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData,
-    DGEList\$counts
-  )
-  soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable
-  goodness = gof(DGLM, pcutoff=fdrthresh)
-  if (sum(goodness\$outlier) > 0) {
-    print.noquote('GLM outliers:')
-    print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F)
-    } else {
-      print('No GLM fit outlier genes found\n')
-    }
-  z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
-  pdf("edgeR_GoodnessofFit.pdf")
-  qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
-  abline(0,1,lwd=3)
-  points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon")
-  dev.off()
-  estpriorn = getPriorN(DGEList)
-  print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
-  efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
-  normData = (1e+06*DGEList\$counts/efflib)
-  uniqueg = unique(group)
-  #### Plot MDS
-  sample_colors =  match(group,levels(group))
-  sampleTypes = levels(factor(group))
-  print.noquote(sampleTypes)
-  pdf("edgeR_MDSplot.pdf")
-  plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors)
-  legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19)
-  grid(col="blue")
-  dev.off()
-  colnames(normData) = paste( colnames(normData),'N',sep="_")
-  print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=',')))
-  nzd = data.frame(log(nonzerod + 1e-2,10))
-  try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") )
-  write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F)
-  tt = cbind(
-    Name=as.character(rownames(DGEList\$counts)),
-    DE\$table,
-    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
-    Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums
-  )
-  print.noquote("# edgeR Top tags\n")
-  tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely
-  tt = tt[order(DE\$table\$PValue),]
-  print.noquote(tt[1:50,])
-  deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
-  nsig = length(deTags)
-  print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F)
-  deColours = ifelse(deTags,'red','black')
-  pdf("edgeR_BCV_vs_abundance.pdf")
-  plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance")
-  dev.off()
-  dg = DGEList[order(DE\$table\$PValue),]
-  #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg)))
-  efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors
-  normData = (1e+06*dg\$counts/efflib)
-  outpdfname="edgeR_top_100_heatmap.pdf"
-  hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle))
-  outSmear = "edgeR_smearplot.pdf"
-  outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
-  smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
-  qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf')
-  norm.factor = DGEList\$samples\$norm.factors
-  topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ]
-  edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR))
-  edgeRcounts = rep(0, length(allgenes))
-  edgeRcounts[edgeRcountsindex] = 1  # Create venn diagram of hits
-  sink()
-  } ### doedgeR
-  if (doDESeq2 == T)
-  {
-    sink("DESeq2.log")
-    # DESeq2
-    require('DESeq2')
-    library('RColorBrewer')
-    if (length(subjects) == 0)
-        {
-        pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM))
-        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ Rx))
-        } else {
-        pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM))
-        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ subjects + Rx))
-        }
-    #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype)
-    #rDESeq = results(DESeq2)
-    #newCountDataSet(workCM, group)
-    deSeqDatsizefac = estimateSizeFactors(deSEQds)
-    deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType)
-    resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype)
-    rDESeq = as.data.frame(results(resDESeq))
-    rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
-    srDESeq = rDESeq[order(rDESeq\$pvalue),]
-    qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf')
-    cat("# DESeq top 50\n")
-    print.noquote(srDESeq[1:50,])
-    write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
-    topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ]
-    DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq))
-    DESeqcounts = rep(0, length(allgenes))
-    DESeqcounts[DESeqcountsindex] = 1
-    pdf("DESeq2_dispersion_estimates.pdf")
-    plotDispEsts(resDESeq)
-    dev.off()
-    ysmall = abs(min(rDESeq\$log2FoldChange))
-    ybig = abs(max(rDESeq\$log2FoldChange))
-    ylimit = min(4,ysmall,ybig)
-    pdf("DESeq2_MA_plot.pdf")
-    plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit))
-    dev.off()
-    rlogres = rlogTransformation(resDESeq)
-    sampledists = dist( t( assay(rlogres) ) )
-    sdmat = as.matrix(sampledists)
-    pdf("DESeq2_sample_distance_plot.pdf")
-    heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"),
-         col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255))
-    dev.off()
-    ###outpdfname="DESeq2_top50_heatmap.pdf"
-    ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle))
-    sink()
-    result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) )
-    if ("try-error" %in% class(result)) {
-         print.noquote('DESeq2 plotPCA failed.')
-         } else {
-         pdf("DESeq2_PCA_plot.pdf")
-         #### wtf - print? Seems needed to get this to work
-         print(ppca)
-         dev.off()
-        }
-  }
-
-  if (doVoom == T) {
-      sink('VOOM.log')
-      if (doedgeR == F) {
-         #### Setup DGEList object
-         DGEList = DGEList(counts=workCM, group = group)
-         DGEList = calcNormFactors(DGEList)
-         DGEList = estimateGLMCommonDisp(DGEList,mydesign)
-         DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
-         DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
-         DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
-         norm.factor = DGEList\$samples\$norm.factors
-         }
-      pdf("VOOM_mean_variance_plot.pdf")
-      dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor)
-      dev.off()
-      # Use limma to fit data
-      fit = lmFit(dat.voomed, mydesign)
-      fit = eBayes(fit)
-      rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none")
-      qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf')
-      rownames(rvoom) = rownames(workCM)
-      rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls)
-      srvoom = rvoom[order(rvoom\$P.Value),]
-      cat("# VOOM top 50\n")
-      print(srvoom[1:50,])
-      write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F)
-      # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma
-      topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
-      voomcountsindex = which(allgenes %in% topresults.voom\$ID)
-      voomcounts = rep(0, length(allgenes))
-      voomcounts[voomcountsindex] = 1
-      sink()
-  }
-
-  if (doCamera) {
-  doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle,
-    outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype)
-  }
-
-  if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) {
-    if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) {
-        vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh)
-        counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts,
-                                       VOOM_limma = voomcounts, row.names = allgenes)
-       } else if ((doDESeq2==T) && (doedgeR==T))  {
-         vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh)
-         counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes)
-       } else if ((doVoom==T) && (doedgeR==T)) {
-        vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
-        counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes)
-       }
-
-    if (nrow(counts.dataframe > 1)) {
-      counts.venn = vennCounts(counts.dataframe)
-      vennf = "Venn_significant_genes_overlap.pdf"
-      pdf(vennf)
-      vennDiagram(counts.venn,main=vennmain,col="maroon")
-      dev.off()
-    }
-  } #### doDESeq2 or doVoom
-
-}
-#### Done
-
-###sink(stdout(),append=T,type="message")
-builtin_gmt = ""
-history_gmt = ""
-history_gmt_name = ""
-out_edgeR = F
-out_DESeq2 = F
-out_VOOM = "$out_VOOM"
-doDESeq2 = $DESeq2.doDESeq2 # make these T or F
-doVoom = $doVoom
-doCamera = F
-doedgeR = $edgeR.doedgeR
-edgeR_priordf = 0
-
-
-#if $doVoom == "T":
-  out_VOOM = "$out_VOOM"
-#end if
-
-#if $DESeq2.doDESeq2 == "T":
-  out_DESeq2 = "$out_DESeq2"
-  DESeq_fitType = "$DESeq2.DESeq_fitType"
-#end if
-
-#if $edgeR.doedgeR == "T":
-  out_edgeR = "$out_edgeR"
-  edgeR_priordf = $edgeR.edgeR_priordf
-#end if
-
-<!--
-#if $camera.doCamera == 'T'
- doCamera = $camera.doCamera
- #if $camera.gmtSource.refgmtSource == "indexed" or $camera.gmtSource.refgmtSource == "both":
-  builtin_gmt = "${camera.gmtSource.builtinGMT.fields.path}"
- #end if
- #if $camera.gmtSource.refgmtSource == "history" or $camera.gmtSource.refgmtSource == "both":
-    history_gmt = "${camera.gmtSource.ownGMT}"
-    history_gmt_name = "${camera.gmtSource.ownGMT.name}"
-  #end if
-#end if
--->
-
-if (sum(c(doedgeR,doVoom,doDESeq2)) == 0)
-{
-write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr())
-quit(save="no",status=2)
-}
-
-Out_Dir = "$html_file.files_path"
-Input =  "$input1"
-TreatmentName = "$treatment_name"
-TreatmentCols = "$Treat_cols"
-ControlName = "$control_name"
-ControlCols= "$Control_cols"
-org = "$input1.dbkey"
-if (org == "") { org = "hg19"}
-fdrtype = "$fdrtype"
-fdrthresh = $fdrthresh
-useNDF = $useNDF
-fQ = $fQ # non-differential centile cutoff
-myTitle = "$title"
-sids = strsplit("$subjectids",',')
-subjects = unlist(sids)
-nsubj = length(subjects)
-TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1
-CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1
-cat('Got TCols=')
-cat(TCols)
-cat('; CCols=')
-cat(CCols)
-cat('\n')
-useCols = c(TCols,CCols)
-if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
-Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header
-snames = colnames(Count_Matrix)
-nsamples = length(snames)
-if (nsubj >  0 & nsubj != nsamples) {
-options("show.error.messages"=T)
-mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),
-   'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=','))
-write(mess, stderr())
-quit(save="no",status=4)
-}
-if (length(subjects) != 0) {subjects = subjects[useCols]}
-Count_Matrix = Count_Matrix[,useCols] ### reorder columns
-rn = rownames(Count_Matrix)
-islib = rn %in% c('librarySize','NotInBedRegions')
-LibSizes = Count_Matrix[subset(rn,islib),][1] # take first
-Count_Matrix = Count_Matrix[subset(rn,! islib),]
-group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) )             #Build a group descriptor
-group = factor(group, levels=c(ControlName,TreatmentName))
-colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_")                   #Relable columns
-results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2,
-                 fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.',
-                 myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects,
-                 doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org,
-                 histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType)
-sessionInfo()
-]]>
-</configfile>
-</configfiles>
-<help>
-
-**What it does**
-
-Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes.
-Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design.
-
-**Input**
-
-Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper
-and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the
-non-negative integer count of reads from one sample overlapping the feature.
-The matrix must have a header row uniquely identifying the source samples, and unique row names in
-the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods.
-
-**Specifying comparisons**
-
-This is basically dumbed down for two factors - case vs control.
-
-More complex interfaces are possible but painful at present.
-Probably need to specify a phenotype file to do this better.
-Work in progress. Send code.
-
-If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples),
-put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or
-A list of integers, one for each subject or an empty string if samples are all independent.
-If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.
-Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.
-
-So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones
-eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use
-8,9,1,1,2,2
-as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6
-
-**Methods available**
-
-You can run 3 popular Bioconductor packages available for count data.
-
-edgeR - see edgeR_ for details
-
-VOOM/limma - see limma_VOOM_ for details
-
-DESeq2 - see DESeq2_ for details
-
-and optionally camera in edgeR which works better if MSigDB is installed.
-
-**Outputs**
-
-Some helpful plots and analysis results. Note that most of these are produced using R code
-suggested by the excellent documentation and vignettes for the Bioconductor
-packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy.
-
-**Note on Voom**
-
-The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method.
-
-This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
-
-voom is an acronym for mean-variance modelling at the observational level.
-The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
-Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
-This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
-The weights are then used in the linear modelling process to adjust for heteroscedasticity.
-
-In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess.
-The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag.
-The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag.
-Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays.
-Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation.
-This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.
-
-
-Author(s)
-
-Charity Law and Gordon Smyth
-
-References
-
-Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia.
-
-Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts.
-Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia.
-http://www.statsci.org/smyth/pubs/VoomPreprint.pdf
-
-See Also
-
-A voom case study is given in the edgeR User's Guide.
-
-vooma is a similar function but for microarrays instead of RNA-seq.
-
-
-***old rant on changes to Bioconductor package variable names between versions***
-
-The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue)
-breaking this and all other code that assumed the old name for this variable,
-between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing).
-This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing
-to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly
-when their old scripts break. This tool currently now works with 2.4.6.
-
-**Note on prior.N**
-
-http://seqanswers.com/forums/showthread.php?t=5591 says:
-
-*prior.n*
-
-The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion.
-You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood
-in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your
-tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the
-common likelihood the weight of one observation.
-
-In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value,
-or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that
-you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation
-(squeezing) of the tagwise dispersions. How many samples do you have in your experiment?
-What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10.
-If you have more samples, then the tagwise dispersion estimates will be more reliable,
-so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5.
-
-
-From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:
-
-Dear Dorota,
-
-The important settings are prior.df and trend.
-
-prior.n and prior.df are related through prior.df = prior.n * residual.df,
-and your experiment has residual.df = 36 - 12 = 24.  So the old setting of
-prior.n=10 is equivalent for your data to prior.df = 240, a very large
-value.  Going the other way, the new setting of prior.df=10 is equivalent
-to prior.n=10/24.
-
-To recover old results with the current software you would use
-
-  estimateTagwiseDisp(object, prior.df=240, trend="none")
-
-To get the new default from old software you would use
-
-  estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)
-
-Actually the old trend method is equivalent to trend="loess" in the new
-software. You should use plotBCV(object) to see whether a trend is
-required.
-
-Note you could also use
-
-  prior.n = getPriorN(object, prior.df=10)
-
-to map between prior.df and prior.n.
-
-----
-
-**Attributions**
-
-edgeR - edgeR_
-
-VOOM/limma - limma_VOOM_
-
-DESeq2 - DESeq2_ for details
-
-See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package.
-
-Galaxy_ (that's what you are using right now!) for gluing everything together
-
-Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is
-licensed to you under the LGPL_ like other rgenetics artefacts
-
-.. _LGPL: http://www.gnu.org/copyleft/lesser.html
-.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html
-.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
-.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
-.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html
-.. _Galaxy: http://getgalaxy.org
-</help>
-
-</tool>
-
-
--- a/rgedgeRpaired_nocamera.xml	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1069 +0,0 @@
-<tool id="rgdifferentialcount" name="Differential_Count" version="0.27">
-  <description>models using BioConductor packages</description>
-  <requirements>
-      <requirement type="package" version="3.1.1">R_3_1_1</requirement>
-      <requirement type="package" version="1.3.18">graphicsmagick</requirement>
-      <requirement type="package" version="9.10">ghostscript</requirement>
-      <requirement type="package" version="2.14">biocbasics</requirement>
-  </requirements>
-
-  <command interpreter="python">
-     rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "Differential_Counts"
-    --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
-  </command>
-  <inputs>
-    <param name="input1"  type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"
-       help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>
-    <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs"
-           help="Supply a meaningful name here to remind you what the outputs contain">
-      <sanitizer invalid_char="">
-        <valid initial="string.letters,string.digits"><add value="_" /> </valid>
-      </sanitizer>
-    </param>
-    <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>
-    <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True"
-         multiple="true" use_header_names="true" size="120" display="checkboxes"  force_select="True">
-        <validator type="no_options" message="Please select at least one column."/>
-    </param>
-    <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>
-    <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True"
-         multiple="true" use_header_names="true" size="120" display="checkboxes"  force_select="True">
-    </param>
-    <param name="subjectids" type="text" optional="true" size="120" value = ""
-       label="IF SUBJECTS NOT ALL INDEPENDENT! Enter comma separated strings to indicate sample labels for (eg) pairing - must be one for every column in input"
-       help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter 'A99,C21,A99,C21'">
-      <sanitizer>
-        <valid initial="string.letters,string.digits"><add value="," /> </valid>
-      </sanitizer>
-    </param>
-    <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs"
-     help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/>
-    <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1"
-              label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"
-     help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/>
-
-    <conditional name="edgeR">
-        <param name="doedgeR" type="select"
-           label="Run this model using edgeR"
-           help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates">
-          <option value="F">Do not run edgeR</option>
-          <option value="T" selected="true">Run edgeR</option>
-         </param>
-         <when value="T">
-          <param name="edgeR_priordf" type="integer" value="10" size="3"
-           label="prior.df for tagwise dispersion - larger value = more squeezing of tag dispersions to common dispersion. Replaces prior.n  and prior.df = prior.n * residual.df"
-           help="10 = edgeR default. Use a larger value to 'smooth' small samples. See edgeR docs and note below"/>
-          <param name="edgeR_robust_method" type="select" value="20" size="3"
-           label="Use robust dispersion method"
-           help="Use ordinary, anscombe or deviance robust deviance estimates">
-             <option value="ordinary" selected="true">Use ordinary deviance estimates</option>
-             <option value="deviance">Use robust deviance estimates</option>
-             <option value="anscombe">use Anscombe robust deviance estimates</option>
-          </param>
-         </when>
-         <when value="F"></when>
-    </conditional>
-    <conditional name="DESeq2">
-    <param name="doDESeq2" type="select"
-       label="Run the same model with DESeq2 and compare findings"
-       help="DESeq2 is an update to the DESeq package. It uses different assumptions and methods to edgeR">
-      <option value="F" selected="true">Do not run DESeq2</option>
-      <option value="T">Run DESeq2</option>
-     </param>
-     <when value="T">
-         <param name="DESeq_fitType" type="select">
-            <option value="parametric" selected="true">Parametric (default) fit for dispersions</option>
-            <option value="local">Local fit - this will automagically be used if parametric fit fails</option>
-            <option value="mean">Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation</option>
-         </param>
-     </when>
-     <when value="F"> </when>
-    </conditional>
-    <param name="doVoom" type="select"
-       label="Run the same model with Voom/limma and compare findings"
-       help="Voom uses counts per million and a precise transformation of variance so count data can be analysed using limma">
-      <option value="F" selected="true">Do not run VOOM</option>
-      <option value="T">Run VOOM</option>
-     </param>
-    <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"
-     help="Conventional default value of 0.05 recommended"/>
-    <param name="fdrtype" type="select" label="FDR (Type II error) control method"
-         help="Use fdr or bh typically to control for the number of tests in a reliable way">
-            <option value="fdr" selected="true">fdr</option>
-            <option value="BH">Benjamini Hochberg</option>
-            <option value="BY">Benjamini Yukateli</option>
-            <option value="bonferroni">Bonferroni</option>
-            <option value="hochberg">Hochberg</option>
-            <option value="holm">Holm</option>
-            <option value="hommel">Hommel</option>
-            <option value="none">no control for multiple tests</option>
-    </param>
-  </inputs>
-  <outputs>
-    <data format="tabular" name="out_edgeR" label="${title}_topTable_edgeR.xls">
-         <filter>edgeR['doedgeR'] == "T"</filter>
-    </data>
-    <data format="tabular" name="out_DESeq2" label="${title}_topTable_DESeq2.xls">
-         <filter>DESeq2['doDESeq2'] == "T"</filter>
-    </data>
-    <data format="tabular" name="out_VOOM" label="${title}_topTable_VOOM.xls">
-         <filter>doVoom == "T"</filter>
-    </data>
-    <data format="html" name="html_file" label="${title}.html"/>
-  </outputs>
- <stdio>
-     <exit_code range="4"   level="fatal"   description="Number of subject ids must match total number of samples in the input matrix" />
- </stdio>
- <tests>
-<test>
-<param name='input1' value='test_bams2mx.xls' ftype='tabular' />
- <param name='treatment_name' value='liver' />
- <param name='title' value='edgeRtest' />
- <param name='useNDF' value='' />
- <param name='doedgeR' value='T' />
- <param name='doVoom' value='T' />
- <param name='doDESeq2' value='T' />
- <param name='fdrtype' value='fdr' />
- <param name='edgeR_priordf' value="8" />
- <param name='edgeR_robust' value="ordinary" />
- <param name='fdrthresh' value="0.05" />
- <param name='control_name' value='heart' />
- <param name='subjectids' value='' />
- <param name='Control_cols' value='3,4,5,9' />
- <param name='Treat_cols' value='2,6,7,8' />
- <output name='out_edgeR' file='edgeRtest1out.xls' compare='diff' />
- <output name='html_file' file='edgeRtest1out.html'  compare='diff' lines_diff='20' />
-</test>
-</tests>
-
-<configfiles>
-<configfile name="runme">
-<![CDATA[
-#
-# edgeR.Rscript
-# updated feb 2014 adding outlier-robust deviance estimate options by ross for R 3.0.2/bioc 2.13
-# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross
-# Performs DGE on a count table containing n replicates of two conditions
-#
-# Parameters
-#
-# 1 - Output Dir
-
-# Original edgeR code by: S.Lunke and A.Kaspi
-reallybig = log10(.Machine\$double.xmax)
-reallysmall = log10(.Machine\$double.xmin)
-library("stringr")
-library("gplots")
-library("edgeR")
-hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here')
-{
-# Perform clustering for significant pvalues after controlling FWER
-    samples = colnames(cmat)
-    gu = unique(group)
-    gn = rownames(cmat)
-    if (length(gu) == 2) {
-        col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"}
-        pcols = unlist(lapply(group,col.map))
-        } else {
-        colours = rainbow(length(gu),start=0,end=4/6)
-        pcols = colours[match(group,gu)]        }
-    dm = cmat[(! is.na(gn)),]
-    # remove unlabelled hm rows
-    nprobes = nrow(dm)
-    # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance')
-    if (nprobes > nsamp) {
-      dm =dm[1:nsamp,]
-      #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total')
-    }
-    newcolnames = substr(colnames(dm),1,20)
-    colnames(dm) = newcolnames
-    pdf(outpdfname)
-    heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none',
-         Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5)
-    dev.off()
-}
-
-hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here")
-{
-    # for 2 groups only was
-    #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"}
-    #pcols = unlist(lapply(group,col.map))
-    gu = unique(group)
-    colours = rainbow(length(gu),start=0.3,end=0.6)
-    pcols = colours[match(group,gu)]
-    nrows = nrow(cmat)
-    mtitle = paste(myTitle,'Heatmap: n contigs =',nrows)
-    if (nrows > nsamp)  {
-               cmat = cmat[c(1:nsamp),]
-               mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='')
-          }
-    newcolnames = substr(colnames(cmat),1,20)
-    colnames(cmat) = newcolnames
-    pdf(outpdfname)
-    heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols)
-    dev.off()
-}
-
-qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...)
-# stolen from https://gist.github.com/703512
-{
-    o = -log10(sort(pvector,decreasing=F))
-    e = -log10( 1:length(o)/length(o) )
-    o[o==-Inf] = reallysmall
-    o[o==Inf] = reallybig
-    maint = descr
-    pdf(outpdf)
-    plot(e,o,pch=19,cex=1, main=maint, ...,
-        xlab=expression(Expected~~-log[10](italic(p))),
-        ylab=expression(Observed~~-log[10](italic(p))),
-        xlim=c(0,max(e)), ylim=c(0,max(o)))
-    lines(e,e,col="red")
-    grid(col = "lightgray", lty = "dotted")
-    dev.off()
-}
-
-smearPlot = function(myDGEList,deTags, outSmear, outMain)
-        {
-        pdf(outSmear)
-        plotSmear(myDGEList,de.tags=deTags,main=outMain)
-        grid(col="lightgray", lty="dotted")
-        dev.off()
-        }
-
-boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname)
-{
-        nc = ncol(rawrs)
-        ##### for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA}
-        fullnames = colnames(rawrs)
-        newcolnames = substr(colnames(rawrs),1,20)
-        colnames(rawrs) = newcolnames
-        newcolnames = substr(colnames(cleanrs),1,20)
-        colnames(cleanrs) = newcolnames
-        defpar = par(no.readonly=T)
-        print.noquote('@@@ Raw contig counts by sample:')
-        print.noquote(summary(rawrs))
-        print.noquote('@@@ Library size contig counts by sample:')
-        print.noquote(summary(cleanrs))
-        pdf(pdfname)
-        par(mfrow=c(1,2))
-        boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main='log2 raw counts')
-        grid(col="lightgray",lty="dotted")
-        boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('log2 counts after ',maint))
-        grid(col="lightgray",lty="dotted")
-        dev.off()
-        pdfname = "sample_counts_histogram.pdf"
-        nc = ncol(rawrs)
-        print.noquote(paste('Using ncol rawrs=',nc))
-        ncroot = round(sqrt(nc))
-        if (ncroot*ncroot < nc) { ncroot = ncroot + 1 }
-        m = c()
-        for (i in c(1:nc)) {
-              rhist = hist(rawrs[,i],breaks=100,plot=F)
-              m = append(m,max(rhist\$counts))
-             }
-        ymax = max(m)
-        ncols = length(fullnames)
-        if (ncols > 20)
-        {
-           scale = 7*ncols/20
-           pdf(pdfname,width=scale,height=scale)
-        } else {
-           pdf(pdfname)
-        }
-        par(mfrow=c(ncroot,ncroot))
-        for (i in c(1:nc)) {
-                 hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon",
-                 breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax))
-             }
-        dev.off()
-        par(defpar)
-
-}
-
-cumPlot = function(rawrs,cleanrs,maint,myTitle)
-{   # updated to use ecdf
-        pdfname = "Differential_rowsum_bar_charts.pdf"
-        defpar = par(no.readonly=T)
-        lrs = log(rawrs,10)
-        lim = max(lrs)
-        pdf(pdfname)
-        par(mfrow=c(2,1))
-        hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)",
-             ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1)
-        grid(col="lightgray", lty="dotted")
-        lrs = log(cleanrs,10)
-        hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)",
-             ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1)
-        grid(col="lightgray", lty="dotted")
-        dev.off()
-        par(defpar)
-}
-
-cumPlot1 = function(rawrs,cleanrs,maint,myTitle)
-{   # updated to use ecdf
-        pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
-        pdf(pdfname)
-        par(mfrow=c(2,1))
-        lastx = max(rawrs)
-        rawe = knots(ecdf(rawrs))
-        cleane = knots(ecdf(cleanrs))
-        cy = 1:length(cleane)/length(cleane)
-        ry = 1:length(rawe)/length(rawe)
-        plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads",
-             ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
-        grid(col="blue")
-        plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads",
-             ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
-        grid(col="blue")
-        dev.off()
-}
-
-
-
-doGSEAold = function(y=NULL,design=NULL,histgmt="",
-                  bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
-                  ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
-{
-  sink('Camera.log')
-  genesets = c()
-  if (bigmt > "")
-  {
-    bigenesets = readLines(bigmt)
-    genesets = bigenesets
-  }
-  if (histgmt > "")
-  {
-    hgenesets = readLines(histgmt)
-    if (bigmt > "") {
-      genesets = rbind(genesets,hgenesets)
-    } else {
-      genesets = hgenesets
-    } # use only history if no bi
-  }
-  print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
-  genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
-  outf = outfname
-  head=paste(myTitle,'edgeR GSEA')
-  write(head,file=outfname,append=F)
-  ntest=length(genesets)
-  urownames = toupper(rownames(y))
-  upcam = c()
-  downcam = c()
-  for (i in 1:ntest) {
-    gs = unlist(genesets[i])
-    g = gs[1] # geneset_id
-    u = gs[2]
-    if (u > "") { u = paste("<a href=\'",u,"\'>",u,"</a>",sep="") }
-    glist = gs[3:length(gs)] # member gene symbols
-    glist = toupper(glist)
-    inglist = urownames %in% glist
-    nin = sum(inglist)
-    if ((nin > minnin) && (nin < maxnin)) {
-      ### print(paste('@@found',sum(inglist),'genes in glist'))
-      camres = camera(y=y,index=inglist,design=design)
-      if (! is.null(camres)) {
-      rownames(camres) = g # gene set name
-      camres = cbind(GeneSet=g,URL=u,camres)
-      if (camres\$Direction == "Up")
-        {
-        upcam = rbind(upcam,camres) } else {
-          downcam = rbind(downcam,camres)
-        }
-      }
-   }
-  }
-  uscam = upcam[order(upcam\$PValue),]
-  unadjp = uscam\$PValue
-  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
-  dscam = downcam[order(downcam\$PValue),]
-  unadjp = dscam\$PValue
-  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
-  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
-  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
-  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
-  sink()
-}
-
-
-
-
-doGSEA = function(y=NULL,design=NULL,histgmt="",
-                  bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
-                  ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
-{
-  sink('Camera.log')
-  genesets = c()
-  if (bigmt > "")
-  {
-    bigenesets = readLines(bigmt)
-    genesets = bigenesets
-  }
-  if (histgmt > "")
-  {
-    hgenesets = readLines(histgmt)
-    if (bigmt > "") {
-      genesets = rbind(genesets,hgenesets)
-    } else {
-      genesets = hgenesets
-    } # use only history if no bi
-  }
-  print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
-  genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
-  outf = outfname
-  head=paste(myTitle,'edgeR GSEA')
-  write(head,file=outfname,append=F)
-  ntest=length(genesets)
-  urownames = toupper(rownames(y))
-  upcam = c()
-  downcam = c()
-  incam = c()
-  urls = c()
-  gsids = c()
-  for (i in 1:ntest) {
-    gs = unlist(genesets[i])
-    gsid = gs[1] # geneset_id
-    url = gs[2]
-    if (url > "") { url = paste("<a href=\'",url,"\'>",url,"</a>",sep="") }
-    glist = gs[3:length(gs)] # member gene symbols
-    glist = toupper(glist)
-    inglist = urownames %in% glist
-    nin = sum(inglist)
-    if ((nin > minnin) && (nin < maxnin)) {
-      incam = c(incam,inglist)
-      gsids = c(gsids,gsid)
-      urls = c(urls,url)
-    }
-  }
-  incam = as.list(incam)
-  names(incam) = gsids
-  allcam = camera(y=y,index=incam,design=design)
-  allcamres = cbind(geneset=gsids,allcam,URL=urls)
-  for (i in 1:ntest) {
-    camres = allcamres[i]
-    res = try(test = (camres\$Direction == "Up"))
-    if ("try-error" %in% class(res)) {
-      cat("test failed, camres = :")
-      print.noquote(camres)
-    } else  { if (camres\$Direction == "Up")
-    {  upcam = rbind(upcam,camres)
-    } else { downcam = rbind(downcam,camres)
-    }
-
-    }
-  }
-  uscam = upcam[order(upcam\$PValue),]
-  unadjp = uscam\$PValue
-  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
-  dscam = downcam[order(downcam\$PValue),]
-  unadjp = dscam\$PValue
-  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
-  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
-  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
-  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
-  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
-  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
-  sink()
-  }
-
-
-edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_Voom=F,out_DESeq2=F,fdrtype='fdr',priordf=5,
-        fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F,
-        filterquantile=0.2, subjects=c(),TreatmentName="Rx",ControlName="Ctrl",mydesign=NULL,
-        doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19',
-        histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
-        doCook=F,DESeq_fitType="parameteric",robust_meth='ordinary')
-{
-
-logf = file('Differential.log', open = "a")
-sink(logf,type = c("output", "message"))
-
-
-run_edgeR = function(workCM,pdata,subjects,group,priordf,robust_meth,mydesign,mt,cmrowsums,out_edgeR,nonzerod)
-{
-  logf = file('edgeR.log', open = "a")
-  sink(logf,type = c("output", "message"))
-  #### Setup myDGEList object
-  myDGEList = DGEList(counts=workCM, group = group)
-  myDGEList = calcNormFactors(myDGEList)
-  if (robust_meth == 'ordinary') {
-       myDGEList = estimateGLMCommonDisp(myDGEList,mydesign)
-       myDGEList = estimateGLMTrendedDisp(myDGEList,mydesign)
-       if (priordf > 0) {  myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign,prior.df = priordf)
-       } else { myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign) }
-       comdisp = myDGEList\$common.dispersion
-       estpriorn = getPriorN(myDGEList)
-       print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
-     } else {
-       myDGEList = estimateGLMRobustDisp(myDGEList,design=mydesign, prior.df = priordf, maxit = 6, residual.type = robust_meth)
-          }
-
-
-  DGLM = glmFit(myDGEList,design=mydesign)
-  DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed
-  normData = cpm(myDGEList)
-  uoutput = cbind(
-    Name=as.character(rownames(myDGEList\$counts)),
-    DE\$table,
-    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
-    Dispersion=myDGEList\$tagwise.dispersion,totreads=cmrowsums,normData,
-    myDGEList\$counts
-  )
-  soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable
-  goodness = gof(DGLM, pcutoff=fdrthresh)
-  if (sum(goodness\$outlier) > 0) {
-    print.noquote('GLM outliers:')
-    print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F)
-    } else {
-      print('No GLM fit outlier genes found\n')
-    }
-  z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
-  pdf(paste("edgeR",mt,"GoodnessofFit.pdf",sep='_'))
-  qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
-  abline(0,1,lwd=3)
-  points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon")
-  dev.off()
-  uniqueg = unique(group)
-  write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F)
-  tt = cbind(
-    Name=as.character(rownames(myDGEList)),
-    DE\$table,
-    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
-    Dispersion=myDGEList\$tagwise.dispersion,totreads=cmrowsums
-  )
-  tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely
-  stt = tt[order(DE\$table\$PValue),]
-  print.noquote("@@ edgeR Top tags\n")
-  print.noquote(stt[1:50,])
-  deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
-  nsig = length(deTags)
-  print.noquote(paste('@@',nsig,'tags significant at adj p=',fdrthresh))
-  deColours = ifelse(deTags,'red','black')
-  pdf(paste("edgeR",mt,"BCV_vs_abundance.pdf",sep="_"))
-  plotBCV(myDGEList, cex=0.3, main="Biological CV vs abundance")
-  dev.off()
-  dg = myDGEList[order(DE\$table\$PValue),]
-  outpdfname= paste("edgeR",mt,"top_100_heatmap.pdf",sep="_")
-  ocpm = normData[order(DE\$table\$PValue),]
-  ocpm = ocpm[c(1:100),]
-  hmap2(ocpm,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste(myTitle,'Heatmap'))
-  outSmear = paste("edgeR",mt,"smearplot.pdf",sep="_")
-  outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
-  smearPlot(myDGEList=myDGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
-  qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf=paste('edgeR',mt,'qqplot.pdf',sep='_'))
-  topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ]
-  edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR))
-  edgeRcounts = rep(0, length(allgenes))
-  edgeRcounts[edgeRcountsindex] = 1  # Create venn diagram of hits
-  sink()
-  return(list(myDGEList=myDGEList,edgeRcounts=edgeRcounts))
-} ### run_edgeR
-
-
-run_DESeq2 = function(workCM,pdata,subjects,group,out_DESeq2,mt,DESeq_fitType)
-
- {
-    logf = file("DESeq2.log", open = "a")
-    sink(logf,type = c("output", "message"))
-    # DESeq2
-    require('DESeq2')
-    library('RColorBrewer')
-    if (length(subjects) == 0)
-        {
-        pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM))
-        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ Rx))
-        } else {
-        pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM))
-        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ subjects + Rx))
-        }
-    deSeqDatsizefac = estimateSizeFactors(deSEQds)
-    deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType)
-    resDESeq = nbinomWaldTest(deSeqDatdisp)
-    rDESeq = as.data.frame(results(resDESeq))
-    rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
-    srDESeq = rDESeq[order(rDESeq\$pvalue),]
-    qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf=paste('DESeq2',mt,'qqplot.pdf',sep="_"))
-    cat("# DESeq top 50\n")
-    print.noquote(srDESeq[1:50,])
-    write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
-    topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ]
-    DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq))
-    DESeqcounts = rep(0, length(allgenes))
-    DESeqcounts[DESeqcountsindex] = 1
-    pdf(paste("DESeq2",mt,"dispersion_estimates.pdf",sep='_'))
-    plotDispEsts(resDESeq)
-    dev.off()
-    ysmall = abs(min(rDESeq\$log2FoldChange))
-    ybig = abs(max(rDESeq\$log2FoldChange))
-    ylimit = min(4,ysmall,ybig)
-    pdf(paste("DESeq2",mt,"MA_plot.pdf",sep="_"))
-    plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit))
-    dev.off()
-    rlogres = rlogTransformation(resDESeq)
-    sampledists = dist( t( assay(rlogres) ) )
-    sdmat = as.matrix(sampledists)
-    pdf(paste("DESeq2",mt,"sample_distance_plot.pdf",sep="_"))
-    heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"),
-         col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255))
-    dev.off()
-    result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) )
-    if ("try-error" %in% class(result)) {
-         print.noquote('DESeq2 plotPCA failed.')
-         } else {
-         pdf(paste("DESeq2",mt,"PCA_plot.pdf",sep="_"))
-         #### wtf - print? Seems needed to get this to work
-         print(ppca)
-         dev.off()
-        }
-    sink()
-    return(DESeqcounts)
-  }
-
-
-run_Voom = function(workCM,pdata,subjects,group,mydesign,mt,out_Voom)
-  {
-    logf = file('VOOM.log', open = "a")
-    sink(logf,type = c("output", "message"))
-    if (doedgeR == F) {
-        #### Setup myDGEList object
-        myDGEList = DGEList(counts=workCM, group = group)
-        myDGEList = calcNormFactors(myDGEList)
-        myDGEList = estimateGLMCommonDisp(myDGEList,mydesign)
-        myDGEList = estimateGLMTrendedDisp(myDGEList,mydesign)
-        myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign)
-    }
-    pdf(paste("VOOM",mt,"mean_variance_plot.pdf",sep='_'))
-    dat.voomed <- voom(myDGEList, mydesign, plot = TRUE, normalize.method="quantil", lib.size = NULL)
-    dev.off()
-    # Use limma to fit data
-    fit = lmFit(dat.voomed, mydesign)
-    fit = eBayes(fit)
-    rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none")
-    qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf=paste('VOOM',mt,'qqplot.pdf',sep='_'))
-    rownames(rvoom) = rownames(workCM)
-    rvoom = cbind(Contig=rownames(workCM),rvoom,NReads=cmrowsums,URL=contigurls)
-    srvoom = rvoom[order(rvoom\$P.Value),]
-    cat("# VOOM top 50\n")
-    print(srvoom[1:50,])
-    write.table(srvoom,file=out_Voom, quote=FALSE, sep="\t",row.names=F)
-    # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma
-    topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
-    voomcountsindex <- which(allgenes %in% rownames(topresults.voom))
-    voomcounts = rep(0, length(allgenes))
-    voomcounts[voomcountsindex] = 1
-    sink()
-    return(voomcounts)
-    }
-
-
-#### data cleaning and analsis control starts here
-
-
-  # Error handling
-  nugroup = length(unique(group))
-  if (nugroup!=2){
-    print("Number of conditions identified in experiment does not equal 2")
-    q()
-    }
-  require(edgeR)
-  options(width = 512)
-  mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ")
-  allN = nrow(Count_Matrix)
-  nscut = round(ncol(Count_Matrix)/2) # half samples
-  colTotmillionreads = colSums(Count_Matrix)/1e6
-  counts.dataframe = as.data.frame(c())
-  rawrs = rowSums(Count_Matrix)
-  nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes
-  nzN = nrow(nonzerod)
-  nzrs = rowSums(nonzerod)
-  zN = allN - nzN
-  print('@@@ Quantiles for non-zero row counts:',quote=F)
-  print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F)
-  if (useNDF == T)
-  {
-    gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut
-    lo = colSums(Count_Matrix[!gt1rpin3,])
-    workCM = Count_Matrix[gt1rpin3,]
-    cleanrs = rowSums(workCM)
-    cleanN = length(cleanrs)
-    meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="")
-    print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F)
-    maint = paste('Filter >=1/million reads in >=',nscut,'samples')
-  }   else {
-    useme = (nzrs > quantile(nzrs,filterquantile))
-    workCM = nonzerod[useme,]
-    lo = colSums(nonzerod[!useme,])
-    cleanrs = rowSums(workCM)
-    cleanN = length(cleanrs)
-    meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="")
-    print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F)
-    maint = paste('Filter below',filterquantile,'quantile')
-  }
-  cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle)
-  allgenes = rownames(workCM)
-  reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" # ucsc chr:start-end regexp
-  genecards="<a href=\'http://www.genecards.org/index.php?path=/Search/keyword/"
-  ucsc = paste("<a href=\'http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='')
-  testreg = str_match(allgenes,reg)
-  if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string
-  {
-    print("@@ using ucsc substitution for urls")
-    contigurls = paste0(ucsc,"&amp;position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"</a>")
-  } else {
-    print("@@ using genecards substitution for urls")
-    contigurls = paste0(genecards,allgenes,"\'>",allgenes,"</a>")
-  }
-  print.noquote(paste("@@ Total low count contigs per sample = ",paste(table(lo),collapse=',')))
-  cmrowsums = rowSums(workCM)
-  TName=unique(group)[1]
-  CName=unique(group)[2]
-  if (is.null(mydesign)) {
-    if (length(subjects) == 0)
-    {
-      mydesign = model.matrix(~group)
-    }
-    else {
-      subjf = factor(subjects)
-      mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it
-    }
-  }
-  print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=',')))
-  print.noquote('Using design matrix:')
-  print.noquote(mydesign)
-  normData = cpm(workCM)*1e6
-  colnames(normData) = paste( colnames(workCM),'N',sep="_")
-  print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=',')))
-
-  if (doedgeR == T) {
-      eres = run_edgeR(workCM,pdata,subjects,group,priordf,robust_meth,mydesign,mt,cmrowsums,out_edgeR,nonzerod)
-      myDGEList = eres\$myDGEList
-      edgeRcounts = eres\$edgeRcounts
-      #### Plot MDS
-      sample_colors =  match(group,levels(group))
-      sampleTypes = levels(factor(group))
-      print.noquote(sampleTypes)
-      pdf(paste("edgeR",mt,"MDSplot.pdf",sep='_'))
-      plotMDS.DGEList(myDGEList,main=paste("MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors)
-      legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19)
-      grid(col="blue")
-      dev.off()
-      scale <- myDGEList\$samples\$lib.size*myDGEList\$samples\$norm.factors
-      normCounts <- round(t(t(myDGEList\$counts)/scale)*mean(scale))
-      try({boxPlot(rawrs=nzd,cleanrs=log2(normCounts+1),maint='Effects of TMM size normalisation',myTitle=myTitle,pdfname=paste("edgeR",mt,"raw_norm_counts_box.pdf",sep='_'))},T)
-   }
-  if (doDESeq2 == T) {  DESeqcounts = run_DESeq2(workCM,pdata,subjects,group,out_DESeq2,mt,DESeq_fitType) }
-  if (doVoom == T) { voomcounts = run_Voom(workCM,pdata,subjects,group,mydesign,mt,out_Voom) }
-
-
-  if (doCamera) {
-  doGSEA(y=myDGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle,
-    outfname=paste("GSEA_Camera",mt,"table.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype)
-  }
-  counts.dataframe = c()
-  vennmain = 'no venn'
-  if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) {
-    if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) {
-        vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh)
-        counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts,
-                                       VOOM_limma = voomcounts, row.names = allgenes)
-       } else if ((doDESeq2==T) && (doedgeR==T))  {
-         vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh)
-         counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes)
-       } else if ((doVoom==T) && (doedgeR==T)) {
-        vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
-        counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes)
-       }
-
-    if (nrow(counts.dataframe > 1)) {
-      counts.venn = vennCounts(counts.dataframe)
-      vennf = paste("Differential_venn",mt,"significant_genes_overlap.pdf",sep="_")
-      pdf(vennf)
-      vennDiagram(counts.venn,main=vennmain,col="maroon")
-      dev.off()
-    }
-  } #### doDESeq2 or doVoom
-sink()
-}
-#### Done
-]]>
-builtin_gmt = ""
-history_gmt = ""
-history_gmt_name = ""
-out_edgeR = F
-out_DESeq2 = F
-out_Voom = "$out_VOOM"
-edgeR_robust_meth = "ordinary"
-doDESeq2 = $DESeq2.doDESeq2
-doVoom = $doVoom
-doCamera = F
-doedgeR = $edgeR.doedgeR
-edgeR_priordf = 10
-
-
-#if $doVoom == "T":
-  out_Voom = "$out_VOOM"
-#end if
-
-#if $DESeq2.doDESeq2 == "T":
-  out_DESeq2 = "$out_DESeq2"
-  doDESeq2 = T
-  DESeq_fitType = "$DESeq2.DESeq_fitType"
-#end if
-
-#if $edgeR.doedgeR == "T":
-  out_edgeR = "$out_edgeR"
-  edgeR_priordf = $edgeR.edgeR_priordf
-  edgeR_robust_meth = "$edgeR.edgeR_robust_method"
-#end if
-
-
-if (sum(c(doedgeR,doVoom,doDESeq2)) == 0)
-{
-write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr())
-quit(save="no",status=2)
-}
-
-Out_Dir = "$html_file.files_path"
-Input =  "$input1"
-TreatmentName = "$treatment_name"
-TreatmentCols = "$Treat_cols"
-ControlName = "$control_name"
-ControlCols= "$Control_cols"
-org = "$input1.dbkey"
-if (org == "") { org = "hg19"}
-fdrtype = "$fdrtype"
-fdrthresh = $fdrthresh
-useNDF = $useNDF
-fQ = $fQ # non-differential centile cutoff
-myTitle = "$title"
-sids = strsplit("$subjectids",',')
-subjects = unlist(sids)
-nsubj = length(subjects)
-TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1
-CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1
-cat('Got TCols=')
-cat(TCols)
-cat('; CCols=')
-cat(CCols)
-cat('\n')
-<![CDATA[
-useCols = c(TCols,CCols)
-if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
-Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t')
-snames = colnames(Count_Matrix)
-nsamples = length(snames)
-if (nsubj >  0 & nsubj != nsamples) {
-options("show.error.messages"=T)
-mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),
-   'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=','))
-write(mess, stderr())
-quit(save="no",status=4)
-}
-if (length(subjects) != 0) {subjects = subjects[useCols]}
-Count_Matrix = Count_Matrix[,useCols] ### reorder columns
-rn = rownames(Count_Matrix)
-islib = rn %in% c('librarySize','NotInBedRegions')
-LibSizes = Count_Matrix[subset(rn,islib),][1] # take first
-Count_Matrix = Count_Matrix[subset(rn,! islib),]
-group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) )
-group = factor(group, levels=c(ControlName,TreatmentName))
-colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_")
-results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_Voom=out_Voom, out_DESeq2=out_DESeq2,
-                 fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.',
-                 myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects,TreatmentName=TreatmentName,ControlName=ControlName,
-                 doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org,
-                 histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType,robust_meth=edgeR_robust_meth)
-sessionInfo()
-
-sink()
-]]>
-</configfile>
-</configfiles>
-<help>
-
-**What it does**
-
-Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes.
-Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design.
-
-**Input**
-
-Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper
-and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the
-non-negative integer count of reads from one sample overlapping the feature.
-
-The matrix must have a header row uniquely identifying the source samples, and unique row names in
-the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods.
-They must be unique and R names or they will be mangled - please read the fine R docs for the rules on identifiers.
-
-**Specifying comparisons**
-
-This is basically dumbed down for two factors - case vs control.
-
-More complex interfaces are possible but painful at present.
-Probably need to specify a phenotype file to do this better.
-Work in progress. Send code.
-
-If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples),
-put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or
-A list of integers, one for each subject or an empty string if samples are all independent.
-If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.
-Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.
-
-So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones
-eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use
-8,9,1,1,2,2
-as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6
-
-**Methods available**
-
-You can run 3 popular Bioconductor packages available for count data.
-
-edgeR - see edgeR_ for details
-
-VOOM/limma - see limma_VOOM_ for details
-
-DESeq2 - see DESeq2_ for details
-
-and optionally camera in edgeR which works better if MSigDB is installed.
-
-**Outputs**
-
-Some helpful plots and analysis results. Note that most of these are produced using R code
-suggested by the excellent documentation and vignettes for the Bioconductor
-packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy.
-
-**Note on Voom**
-
-The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method.
-
-This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
-
-voom is an acronym for mean-variance modelling at the observational level.
-The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
-Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
-This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
-The weights are then used in the linear modelling process to adjust for heteroscedasticity.
-
-In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess.
-The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag.
-The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag.
-Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays.
-Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation.
-This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.
-
-
-Author(s)
-
-Charity Law and Gordon Smyth
-
-References
-
-Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia.
-
-Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts.
-Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia.
-http://www.statsci.org/smyth/pubs/VoomPreprint.pdf
-
-See Also
-
-A voom case study is given in the edgeR User's Guide.
-
-vooma is a similar function but for microarrays instead of RNA-seq.
-
-
-***old rant on changes to Bioconductor package variable names between versions***
-
-The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue)
-breaking this and all other code that assumed the old name for this variable,
-between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing).
-This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing
-to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly
-when their old scripts break. This tool currently now works with 2.4.6.
-
-**Note on prior.N**
-
-http://seqanswers.com/forums/showthread.php?t=5591 says:
-
-*prior.n*
-
-The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion.
-You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood
-in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your
-tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the
-common likelihood the weight of one observation.
-
-In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value,
-or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that
-you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation
-(squeezing) of the tagwise dispersions. How many samples do you have in your experiment?
-What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10.
-If you have more samples, then the tagwise dispersion estimates will be more reliable,
-so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5.
-
-
-From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:
-
-Dear Dorota,
-
-The important settings are prior.df and trend.
-
-prior.n and prior.df are related through prior.df = prior.n * residual.df,
-and your experiment has residual.df = 36 - 12 = 24.  So the old setting of
-prior.n=10 is equivalent for your data to prior.df = 240, a very large
-value.  Going the other way, the new setting of prior.df=10 is equivalent
-to prior.n=10/24.
-
-To recover old results with the current software you would use
-
-  estimateTagwiseDisp(object, prior.df=240, trend="none")
-
-To get the new default from old software you would use
-
-  estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)
-
-Actually the old trend method is equivalent to trend="loess" in the new
-software. You should use plotBCV(object) to see whether a trend is
-required.
-
-Note you could also use
-
-  prior.n = getPriorN(object, prior.df=10)
-
-to map between prior.df and prior.n.
-
-----
-
-**Attributions**
-
-edgeR - edgeR_
-
-VOOM/limma - limma_VOOM_
-
-DESeq2 - DESeq2_ for details
-
-See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package.
-
-Galaxy_ (that's what you are using right now!) for gluing everything together
-
-Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is
-licensed to you under the LGPL_ like other rgenetics artefacts
-
-.. _LGPL: http://www.gnu.org/copyleft/lesser.html
-.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html
-.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
-.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
-.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html
-.. _Galaxy: http://getgalaxy.org
-</help>
-
-</tool>
-
-
--- a/test-data/edgeRtest1out.html	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,621 +0,0 @@
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
-        <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
-        <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-        <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://getgalaxy.org/" />
-        <title></title>
-        <link rel="stylesheet" href="/static/style/base.css" type="text/css" />
-        </head>
-        <body>
-        <div class="toolFormBody">
-
-<div class="infomessage">Galaxy Tool "Differential_Counts" run at 28/12/2014 21:02:37</div><br/>
-<div class="toolFormTitle">DESeq2 images and outputs</div>
-(Click on a thumbnail image to download the corresponding original PDF image)<br/>
-<div><table class="simple" cellpadding="2" cellspacing="2">
-<tr>
-<td><a href="DESeq2_edgeRtest_MA_plot.pdf"><img src="DESeq2_edgeRtest_MA_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_MA_plot.pdf" hspace="5" width="400"
-                           alt="Image called DESeq2_edgeRtest_MA_plot.pdf"/></a></td>
-
-<td><a href="DESeq2_edgeRtest_PCA_plot.pdf"><img src="DESeq2_edgeRtest_PCA_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_PCA_plot.pdf" hspace="5" width="400"
-                           alt="Image called DESeq2_edgeRtest_PCA_plot.pdf"/></a></td>
-
-<td><a href="DESeq2_edgeRtest_dispersion_estimates.pdf"><img src="DESeq2_edgeRtest_dispersion_estimates.png" title="Click to download a PDF of DESeq2_edgeRtest_dispersion_estimates.pdf" hspace="5" width="400"
-                           alt="Image called DESeq2_edgeRtest_dispersion_estimates.pdf"/></a></td>
-</tr>
-<tr>
-<td><a href="DESeq2_edgeRtest_qqplot.pdf"><img src="DESeq2_edgeRtest_qqplot.png" title="Click to download a PDF of DESeq2_edgeRtest_qqplot.pdf" hspace="5" width="400"
-                           alt="Image called DESeq2_edgeRtest_qqplot.pdf"/></a></td>
-
-<td><a href="DESeq2_edgeRtest_sample_distance_plot.pdf"><img src="DESeq2_edgeRtest_sample_distance_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_sample_distance_plot.pdf" hspace="5" width="400"
-                           alt="Image called DESeq2_edgeRtest_sample_distance_plot.pdf"/></a></td>
-
-<td>&nbsp;</td>
-</tr></table></div>
-
-<div class="toolFormTitle">DESeq2 log output</div>
-
-<pre>
-
-# DESeq top 50
-
-                     Contig     baseMean log2FoldChange     lfcSE       stat        pvalue          padj  NReads                                                                                               URL
-
-Mir192               Mir192 271352.97636       6.965264 0.2150593  32.387646 4.096935e-230 3.818343e-227 2325567               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a>
-
-Mir122a             Mir122a  10112.31117      10.312083 0.3292695  31.318061 2.649329e-215 1.234587e-212   90428             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a>
-
-Mir149               Mir149    810.35429      -6.911118 0.2341392 -29.517132 1.735536e-191 5.391733e-189    6164               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a>
-
-Mir23a               Mir23a   1289.18043      -3.104086 0.1191688 -26.047815 1.424245e-149 3.318491e-147   10118               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a>
-
-Mir181d             Mir181d    275.22797      -3.581172 0.1778187 -20.139461  3.329373e-90  6.205952e-88    2139             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a>
-
-Mir204               Mir204    347.57397      -7.284200 0.3771119 -19.315751  3.959346e-83  6.150183e-81    2601               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a>
-
-Mir23b               Mir23b   2028.55377      -2.065110 0.1085802 -19.019217  1.182361e-80  1.574229e-78   16387               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23b'>Mir23b</a>
-
-Mir27a               Mir27a   2788.72629      -3.016676 0.1688167 -17.869539  2.036708e-71  2.372765e-69   21886               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a>
-
-Mir195               Mir195    519.86200      -3.152795 0.1784796 -17.664734  7.838131e-70  8.116820e-68    3962               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a>
-
-Mir194-2           Mir194-2    391.65678       5.222911 0.3099275  16.852045  1.013492e-63  9.445744e-62    3570           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a>
-
-Mir208b             Mir208b   1649.77924     -11.396172 0.6771238 -16.830264  1.464482e-63  1.240816e-61   14756             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a>
-
-Mir10b               Mir10b  27820.40551      -5.071453 0.3044884 -16.655656  2.753110e-62  2.138249e-60  197340               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a>
-
-Mir181c             Mir181c   2765.96510      -3.660964 0.2275711 -16.087120  3.141152e-58  2.251965e-56   23605             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a>
-
-Mir208a             Mir208a    616.76981     -10.356524 0.6559218 -15.789267  3.688391e-56  2.455415e-54    4638             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a>
-
-Mir490               Mir490    220.99790      -8.059660 0.5142876 -15.671504  2.369067e-55  1.471980e-53    1741               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a>
-
-Mir203               Mir203    772.92882       1.990849 0.1274099  15.625546  4.877239e-55  2.840992e-53    6739               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a>
-
-Mir215               Mir215    152.78082      -3.004380 0.1939090 -15.493765  3.822341e-54  2.095542e-52    1182               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'>Mir215</a>
-
-Dnm3os               Dnm3os    179.61643      -3.278392 0.2166491 -15.132265  9.922045e-52  5.137415e-50    1401               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'>Dnm3os</a>
-
-Mir214               Mir214    134.69038      -3.216444 0.2154916 -14.926074  2.230149e-50  1.093947e-48    1048               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'>Mir214</a>
-
-Mir21                 Mir21  26121.31011       2.963903 0.2008617  14.755939  2.817433e-49  1.312924e-47  229120                 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a>
-
-Mir1948             Mir1948    263.89527       7.074045 0.4867226  14.534039  7.374076e-48  3.272685e-46    2404             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a>
-
-Mir27b               Mir27b  76478.05753      -1.904653 0.1312889 -14.507339  1.088626e-47  4.611815e-46  625308               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27b'>Mir27b</a>
-
-Rabggtb             Rabggtb   2257.19195       1.988368 0.1401741  14.184987  1.134862e-45  4.598659e-44   19535             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a>
-
-Mir499               Mir499    712.45950     -10.577061 0.7528467 -14.049423  7.766426e-45  3.015962e-43    6527               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a>
-
-Mir101b             Mir101b   6846.19683       3.791681 0.2809666  13.495132  1.670548e-41  6.227801e-40   59019             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a>
-
-Mir132               Mir132    106.46062      -2.797928 0.2083376 -13.429779  4.046171e-41  1.450397e-39     857               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'>Mir132</a>
-
-Mir143hg           Mir143hg 180217.77425      -2.169143 0.1685614 -12.868566  6.764677e-38  2.335066e-36 1407364           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143hg'>Mir143hg</a>
-
-Mir143               Mir143 179219.35960      -2.170303 0.1696199 -12.795094  1.746402e-37  5.813025e-36 1399819               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143'>Mir143</a>
-
-Mir155               Mir155     57.66182      -3.788079 0.3056585 -12.393175  2.845516e-35  9.144898e-34     463               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a>
-
-Mir322               Mir322    899.53469      -3.126011 0.2622596 -11.919531  9.363380e-33  2.908890e-31    7074               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a>
-
-Mir378               Mir378    483.21548      -2.994300 0.2577321 -11.617876  3.343461e-31  1.005195e-29    4075               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a>
-
-Mir24-2             Mir24-2    424.48288      -2.712674 0.2361028 -11.489378  1.491830e-30  4.213289e-29    3470             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'>Mir24-2</a>
-
-Mir3074-2         Mir3074-2    424.48288      -2.712674 0.2361028 -11.489378  1.491830e-30  4.213289e-29    3470         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'>Mir3074-2</a>
-
-Mir199b             Mir199b     47.84725      -5.294373 0.4644474 -11.399295  4.215163e-30  1.155451e-28     370             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a>
-
-Mir802               Mir802    166.83414       8.816580 0.7782636  11.328527  9.478530e-30  2.523997e-28    1514               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a>
-
-Mir125b-2         Mir125b-2    493.08516      -2.919341 0.2631193 -11.095122  1.324798e-28  3.429754e-27    3837         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a>
-
-Mir301               Mir301    260.53406      -1.676984 0.1526772 -10.983852  4.570133e-28  1.151179e-26    2119               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir301'>Mir301</a>
-
-Snord104           Snord104   3851.90119       2.386573 0.2173857  10.978522  4.847915e-28  1.189015e-26   33458           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a>
-
-Mir150               Mir150    553.20599      -2.836881 0.2595088 -10.931734  8.127991e-28  1.942381e-26    4229               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir150'>Mir150</a>
-
-Mir148a             Mir148a 118994.46955       2.678852 0.2481801  10.793984  3.675045e-27  8.562855e-26 1002397             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'>Mir148a</a>
-
-5430416N02Rik 5430416N02Rik     62.15966       3.089960 0.2941123  10.506053  8.101331e-26  1.841571e-24     564 <a href='http://www.genecards.org/index.php?path=/Search/keyword/5430416N02Rik'>5430416N02Rik</a>
-
-Mir193               Mir193     45.70861       4.991530 0.4814098  10.368568  3.446495e-25  7.647936e-24     421               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'>Mir193</a>
-
-Mir3073             Mir3073     98.93199       8.208709 0.7944742  10.332254  5.036321e-25  1.091593e-23     904             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a>
-
-Mir125b-1         Mir125b-1     79.01988      -3.020660 0.2937360 -10.283590  8.355635e-25  1.769875e-23     609         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-1'>Mir125b-1</a>
-
-2610203C20Rik 2610203C20Rik     79.17666      -3.023491 0.2948614 -10.253939  1.136165e-24  2.353124e-23     610 <a href='http://www.genecards.org/index.php?path=/Search/keyword/2610203C20Rik'>2610203C20Rik</a>
-
-Mir181a-1         Mir181a-1     59.53826      -3.151487 0.3211628  -9.812740  9.923710e-23  2.010630e-21     506         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-1'>Mir181a-1</a>
-
-Mir184               Mir184     32.23796      -4.865023 0.4962776  -9.803028  1.092606e-22  2.166615e-21     247               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a>
-
-Mir199a-2         Mir199a-2     44.84878      -3.422216 0.3545647  -9.651880  4.826276e-22  9.371019e-21     352         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199a-2'>Mir199a-2</a>
-
-Mir182               Mir182    886.79583       4.919630 0.5101689   9.643140  5.255515e-22  9.996204e-21    7189               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a>
-
-Snord91a           Snord91a    168.95251       2.700421 0.2835464   9.523738  1.670595e-21  3.113990e-20    1437           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'>Snord91a</a>
-
-
-</pre>
-
-<div class="toolFormTitle">Differential images and outputs</div>
-(Click on a thumbnail image to download the corresponding original PDF image)<br/>
-<div><table class="simple" cellpadding="2" cellspacing="2">
-<tr>
-<td><a href="Differential_rowsum_bar_charts.pdf"><img src="Differential_rowsum_bar_charts.png" title="Click to download a PDF of Differential_rowsum_bar_charts.pdf" hspace="5" width="400"
-                           alt="Image called Differential_rowsum_bar_charts.pdf"/></a></td>
-
-<td><a href="Differential_venn_edgeRtest_significant_genes_overlap.pdf"><img src="Differential_venn_edgeRtest_significant_genes_overlap.png" title="Click to download a PDF of Differential_venn_edgeRtest_significant_genes_overlap.pdf" hspace="5" width="400"
-                           alt="Image called Differential_venn_edgeRtest_significant_genes_overlap.pdf"/></a></td>
-</tr>
-
-</table></div>
-
-<div class="toolFormTitle">Differential log output</div>
-
-<pre>
-
-[1] @@@ Quantiles for non-zero row counts:
-
-       0%       10%       20%       30%       40%       50%       60%       70%       80%       90%      100%
-
-      1.0       1.0       2.0       3.0       4.0       8.0      13.0      24.0      86.6     753.0 2325567.0
-
-[1] Read 3242 contigs. Removed 1494 with no reads. After filtering at count quantile =0.3, there are 1141 contigs
-
-[1] "@@ using genecards substitution for urls"
-
-[1] @@ Total low count contigs per sample =  1,1,1,1,1,1,1,1
-
-[1] Using samples: liver_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam
-
-[1] Using design matrix:
-
-  (Intercept) groupliver
-
-1           1          1
-
-2           1          1
-
-3           1          1
-
-4           1          1
-
-5           1          0
-
-6           1          0
-
-7           1          0
-
-8           1          0
-
-attr(,"assign")
-
-[1] 0 1
-
-attr(,"contrasts")
-
-attr(,"contrasts")$group
-
-[1] contr.treatment
-
-[1] "Raw sample read totals 2443751,1644652,1682104,1806045,1440960,1341813,2888924,1428365"
-
-[1] heart liver
-
-
-</pre>
-
-<div class="toolFormTitle">Differential log output</div>
-
-<pre>
-
-Attaching package: ‘gplots’
-
-The following object is masked from ‘package:stats’:
-
-    lowess
-
-Loading required package: methods
-
-Loading required package: limma
-
-Loading required package: splines
-
-Loading required package: DESeq2
-
-Loading required package: GenomicRanges
-
-Loading required package: BiocGenerics
-
-Loading required package: parallel
-
-Attaching package: ‘BiocGenerics’
-
-The following objects are masked from ‘package:parallel’:
-
-    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
-
-The following object is masked from ‘package:limma’:
-
-    plotMA
-
-The following object is masked from ‘package:stats’:
-
-    xtabs
-
-The following objects are masked from ‘package:base’:
-
-    anyDuplicated, append, as.data.frame, as.vector, cbind, colnames, duplicated, eval, evalq, Filter, Find, get, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int, rownames, sapply, setdiff, sort, table, tapply, union, unique, unlist
-
-Loading required package: IRanges
-
-Attaching package: ‘IRanges’
-
-The following object is masked from ‘package:gplots’:
-
-    space
-
-Loading required package: XVector
-
-Loading required package: Rcpp
-
-Loading required package: RcppArmadillo
-
-gene-wise dispersion estimates
-
-mean-dispersion relationship
-
-final dispersion estimates
-
-you had estimated gene-wise dispersions, removing these
-
-you had estimated fitted dispersions, removing these
-
-you had estimated gene-wise dispersions, removing these
-
-you had estimated fitted dispersions, removing these
-
-Warning message:
-
-closing unused connection 4 (edgeR.log)
-
-Warning message:
-
-In sink() : no sink to remove
-
-
-</pre>
-
-<div class="toolFormTitle">VOOM images and outputs</div>
-(Click on a thumbnail image to download the corresponding original PDF image)<br/>
-<div><table class="simple" cellpadding="2" cellspacing="2">
-<tr>
-<td><a href="VOOM_edgeRtest_mean_variance_plot.pdf"><img src="VOOM_edgeRtest_mean_variance_plot.png" title="Click to download a PDF of VOOM_edgeRtest_mean_variance_plot.pdf" hspace="5" width="400"
-                           alt="Image called VOOM_edgeRtest_mean_variance_plot.pdf"/></a></td>
-
-<td><a href="VOOM_edgeRtest_qqplot.pdf"><img src="VOOM_edgeRtest_qqplot.png" title="Click to download a PDF of VOOM_edgeRtest_qqplot.pdf" hspace="5" width="400"
-                           alt="Image called VOOM_edgeRtest_qqplot.pdf"/></a></td>
-</tr>
-
-</table></div>
-
-<div class="toolFormTitle">VOOM log output</div>
-
-<pre>
-
-# VOOM top 50
-
-                     Contig      logFC    AveExpr          t      P.Value    adj.P.Val         B  NReads                                                                                               URL
-
-Mir192               Mir192   6.689950 14.4417888  50.335160 1.802287e-16 2.056409e-13 27.414844 2325567               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a>
-
-Mir208a             Mir208a -10.458438  3.8918506 -29.183545 2.249812e-13 1.283518e-10 19.141041    4638             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a>
-
-Mir3073             Mir3073   8.318578  2.6485638  25.821264 1.102217e-12 4.192097e-10 18.063600     904             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a>
-
-Mir802               Mir802   8.992449  2.9857711  25.195575 1.514327e-12 4.319618e-10 17.906674    1514               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a>
-
-Mir208b             Mir208b -12.256447  4.4678897 -22.360114 7.074494e-12 1.614400e-09 16.920424   14756             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a>
-
-Mir499               Mir499 -11.104485  3.8066799 -21.990054 8.769804e-12 1.667724e-09 16.728874    6527               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a>
-
-Mir10b               Mir10b  -4.775768 12.4173688 -21.487387 1.180685e-11 1.924516e-09 17.249348  197340               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a>
-
-Mir148a             Mir148a   2.751538 15.4237642  20.289553 2.464883e-11 3.515539e-09 16.455471 1002397             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'>Mir148a</a>
-
-Mir490               Mir490  -8.497742  3.6613221 -18.336110 8.980482e-11 1.138526e-08 13.923237    1741               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a>
-
-Mir122a             Mir122a  10.197963  8.1512374  17.467826 1.663427e-10 1.897970e-08 14.215445   90428             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a>
-
-Mir133b             Mir133b  -6.172367  1.3497975 -17.274094 1.916064e-10 1.987481e-08 13.840201     159             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'>Mir133b</a>
-
-Mir149               Mir149  -7.041176  6.0886889 -16.861286 2.602547e-10 2.474589e-08 13.714880    6164               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a>
-
-Mir101b             Mir101b   3.837883 10.6216725  15.443054 7.873164e-10 6.910215e-08 13.054350   59019             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a>
-
-Mir143               Mir143  -1.912927 16.0353646 -14.922755 1.209475e-09 9.857220e-08 12.374988 1399819               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143'>Mir143</a>
-
-Mir194-2           Mir194-2   5.534694  6.2627211  14.703097 1.455682e-09 1.107289e-07 12.316769    3570           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a>
-
-Mir23a               Mir23a  -2.905961  8.6431895 -14.558394 1.646894e-09 1.174441e-07 12.339306   10118               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a>
-
-Mir1983             Mir1983  -5.612359  1.1061384 -14.266537 2.119488e-09 1.422551e-07 11.743589     101             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1983'>Mir1983</a>
-
-Mir27a               Mir27a  -2.849084 10.0939084 -14.158498 2.329669e-09 1.476752e-07 11.960195   21886               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a>
-
-Cyp3a25             Cyp3a25   6.312461  1.6425308  13.845627 3.074630e-09 1.846396e-07 11.502713     226             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'>Cyp3a25</a>
-
-Mir200a             Mir200a   6.129125  1.8320913  13.226966 5.410979e-09 3.086963e-07 10.979834     264             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'>Mir200a</a>
-
-Mir181d             Mir181d  -3.405544  6.3702152 -13.064584 6.300369e-09 3.423201e-07 11.006301    2139             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a>
-
-Mir153               Mir153  -5.698257  1.5328802 -12.832092 7.856705e-09 3.829623e-07 10.583835     140               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir153'>Mir153</a>
-
-Mir204               Mir204  -7.718081  4.5031856 -12.808496 8.036265e-09 3.829623e-07 10.353229    2601               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a>
-
-Gm5441               Gm5441  -5.716851  1.5430406 -12.806028 8.055298e-09 3.829623e-07 10.562881     142               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm5441'>Gm5441</a>
-
-Rabggtb             Rabggtb   2.327908  9.9369857  12.760291 8.416902e-09 3.841474e-07 10.654006   19535             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a>
-
-Mir504               Mir504  -5.122304  0.8161671 -12.391521 1.205304e-08 5.289430e-07 10.144865      69               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir504'>Mir504</a>
-
-Mir133a-1         Mir133a-1  -4.912497  0.7297882 -12.335045 1.274466e-08 5.385801e-07 10.076395      60         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133a-1'>Mir133a-1</a>
-
-Mir195               Mir195  -2.954216  7.3530970 -12.081859 1.641098e-08 6.603731e-07 10.055879    3962               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a>
-
-Mir27b               Mir27b  -1.496991 14.9464877 -12.059553 1.678424e-08 6.603731e-07  9.674850  625308               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27b'>Mir27b</a>
-
-Snord52             Snord52   2.631712  9.7652181  11.922618 1.928407e-08 7.334374e-07  9.811774   18059             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord52'>Snord52</a>
-
-Mir322               Mir322  -3.029558  8.1188344 -11.736839 2.333148e-08 8.587488e-07  9.679815    7074               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a>
-
-Mir181c             Mir181c  -3.676262  9.6244506 -11.575598 2.758358e-08 9.835271e-07  9.453057   23605             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a>
-
-Mir1948             Mir1948   7.101780  4.7821564  11.471202 3.077353e-08 1.033009e-06  9.233632    2404             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a>
-
-0610031O16Rik 0610031O16Rik   4.519875  0.7388871  11.470939 3.078205e-08 1.033009e-06  9.284014      78 <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610031O16Rik'>0610031O16Rik</a>
-
-Mir201               Mir201  -4.964105  0.7490919 -11.289794 3.729283e-08 1.210650e-06  9.114028      63               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir201'>Mir201</a>
-
-Mir21                 Mir21   2.746616 13.2835800  11.267331 3.819755e-08 1.210650e-06  8.925217  229120                 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a>
-
-Mir184               Mir184  -5.569565  2.3521173 -11.190343 4.148052e-08 1.279170e-06  8.854599     247               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a>
-
-1810019D21Rik 1810019D21Rik   5.164581  1.0784751  11.082009 4.662057e-08 1.399844e-06  8.951908     117 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1810019D21Rik'>1810019D21Rik</a>
-
-Mir203               Mir203   2.216791  8.5426169  10.866758 5.896538e-08 1.725115e-06  8.715639    6739               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a>
-
-1110038B12Rik 1110038B12Rik   2.383720 10.6847245  10.832157 6.125623e-08 1.744094e-06  8.573067   37066 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1110038B12Rik'>1110038B12Rik</a>
-
-Snord104           Snord104   2.571210 10.4798167  10.811468 6.267120e-08 1.744094e-06  8.561110   33458           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a>
-
-Mir182               Mir182   5.196800  7.2088299  10.640454 7.579543e-08 2.021320e-06  8.545839    7189               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a>
-
-Mir547               Mir547  -4.542934  0.5799793 -10.635980 7.617593e-08 2.021320e-06  8.435473      42               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir547'>Mir547</a>
-
-Mir143hg           Mir143hg  -2.291921 16.3789153 -10.597275 7.955395e-08 2.062978e-06  7.952584 1407364           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143hg'>Mir143hg</a>
-
-Scnn1b               Scnn1b  -4.541403  0.5700621 -10.243065 1.190487e-07 3.018546e-06  8.023327      45               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Scnn1b'>Scnn1b</a>
-
-Mir125b-2         Mir125b-2  -2.896115  7.2737925 -10.091091 1.420082e-07 3.522420e-06  7.876068    3837         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a>
-
-Mir1a-1             Mir1a-1  -4.402568  0.4498447  -9.950346 1.675164e-07 4.066729e-06  7.692790      42             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1a-1'>Mir1a-1</a>
-
-Mir378               Mir378  -2.733247  7.2964165  -9.922980 1.730212e-07 4.112858e-06  7.672216    4075               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a>
-
-Mir199b             Mir199b  -5.651345  2.8029895  -9.883978 1.812024e-07 4.219426e-06  7.548022     370             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a>
-
-Mir155               Mir155  -4.158272  3.8002361  -9.845490 1.896814e-07 4.328530e-06  7.604112     463               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a>
-
-
-</pre>
-
-<div class="toolFormTitle">edgeR images and outputs</div>
-(Click on a thumbnail image to download the corresponding original PDF image)<br/>
-<div><table class="simple" cellpadding="2" cellspacing="2">
-<tr>
-<td><a href="edgeR_edgeRtest_BCV_vs_abundance.pdf"><img src="edgeR_edgeRtest_BCV_vs_abundance.png" title="Click to download a PDF of edgeR_edgeRtest_BCV_vs_abundance.pdf" hspace="5" width="400"
-                           alt="Image called edgeR_edgeRtest_BCV_vs_abundance.pdf"/></a></td>
-
-<td><a href="edgeR_edgeRtest_GoodnessofFit.pdf"><img src="edgeR_edgeRtest_GoodnessofFit.png" title="Click to download a PDF of edgeR_edgeRtest_GoodnessofFit.pdf" hspace="5" width="400"
-                           alt="Image called edgeR_edgeRtest_GoodnessofFit.pdf"/></a></td>
-
-<td><a href="edgeR_edgeRtest_MDSplot.pdf"><img src="edgeR_edgeRtest_MDSplot.png" title="Click to download a PDF of edgeR_edgeRtest_MDSplot.pdf" hspace="5" width="400"
-                           alt="Image called edgeR_edgeRtest_MDSplot.pdf"/></a></td>
-</tr>
-<tr>
-<td><a href="edgeR_edgeRtest_qqplot.pdf"><img src="edgeR_edgeRtest_qqplot.png" title="Click to download a PDF of edgeR_edgeRtest_qqplot.pdf" hspace="5" width="400"
-                           alt="Image called edgeR_edgeRtest_qqplot.pdf"/></a></td>
-
-<td><a href="edgeR_edgeRtest_smearplot.pdf"><img src="edgeR_edgeRtest_smearplot.png" title="Click to download a PDF of edgeR_edgeRtest_smearplot.pdf" hspace="5" width="400"
-                           alt="Image called edgeR_edgeRtest_smearplot.pdf"/></a></td>
-
-<td><a href="edgeR_edgeRtest_top_100_heatmap.pdf"><img src="edgeR_edgeRtest_top_100_heatmap.png" title="Click to download a PDF of edgeR_edgeRtest_top_100_heatmap.pdf" hspace="5" width="400"
-                           alt="Image called edgeR_edgeRtest_top_100_heatmap.pdf"/></a></td>
-</tr>
-
-</table></div>
-
-<div class="toolFormTitle">edgeR log output</div>
-
-<pre>
-
-[1] Common Dispersion = 0.228651460998105 CV =  0.478175136323613 getPriorN =  3.33333333333333
-
-[1] "No GLM fit outlier genes found\n"
-
-[1] @@ edgeR Top tags\n
-
-                       Name      logFC    logCPM        LR        PValue   adj.p.value Dispersion totreads                                                                                               URL
-
-Mir208a             Mir208a -11.840751  8.465017 594.16946 3.104543e-131 3.542284e-128 0.05171220     4638             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a>
-
-Mir149               Mir149  -7.008984  8.861767 484.30321 2.473909e-107 1.411365e-104 0.04959937     6164               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a>
-
-Mir208b             Mir208b -13.291635  9.905945 417.69758  7.737463e-93  2.942815e-90 0.10508096    14756             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a>
-
-Mir122a             Mir122a  10.514683 12.478088 415.17429  2.740525e-92  7.817349e-90 0.10803882    90428             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a>
-
-Mir204               Mir204  -7.498162  7.634507 341.30678  3.313430e-76  7.561247e-74 0.06907958     2601               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a>
-
-Mir499               Mir499 -13.577454  8.700078 325.79199  7.930755e-73  1.508165e-70 0.12042284     6527               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a>
-
-Mir490               Mir490  -8.534394  6.991023 303.17184  6.710366e-68  1.093790e-65 0.07949711     1741               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a>
-
-Mir192               Mir192   6.953853 17.169364 217.22867  3.638307e-49  5.189135e-47 0.12700995  2325567               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a>
-
-Mir802               Mir802  11.440805  6.593380 212.88059  3.231644e-48  4.097007e-46 0.12273671     1514               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a>
-
-Mir1948             Mir1948   7.418142  7.252734 195.66958  1.840248e-44  2.099723e-42 0.12060221     2404             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a>
-
-Mir194-2           Mir194-2   5.298950  7.811522 191.85588  1.250960e-43  1.297587e-41 0.08670751     3570           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a>
-
-Mir23a               Mir23a  -3.153807  9.529402 177.53185  1.676248e-40  1.593833e-38 0.04442763    10118               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a>
-
-Mir181c             Mir181c  -3.767686 10.639598 169.87390  7.883295e-39  6.919107e-37 0.06368883    23605             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a>
-
-Mir3073             Mir3073  10.686337  5.859950 164.86740  9.778593e-38  7.969554e-36 0.14069249      904             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a>
-
-Mir181d             Mir181d  -3.643963  7.300371 162.18591  3.767663e-37  2.865936e-35 0.05729574     2139             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a>
-
-Mir195               Mir195  -3.203683  8.215089 150.20548  1.563314e-34  1.114838e-32 0.05235020     3962               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a>
-
-Mir10b               Mir10b  -5.182616 13.946466 147.24793  6.926819e-34  4.649118e-32 0.12268790   197340               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a>
-
-Mir101b             Mir101b   3.759962 11.863187 136.31359  1.703812e-31  1.080028e-29 0.07961343    59019             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a>
-
-Mir378               Mir378  -3.115599  8.119617 126.76408  2.092233e-29  1.256441e-27 0.05942391     4075               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a>
-
-Mir27a               Mir27a  -3.064687 10.642480 124.98911  5.117477e-29  2.919520e-27 0.06113852    21886               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a>
-
-Mir182               Mir182   5.057509  8.846381 123.17765  1.275060e-28  6.927826e-27 0.13653707     7189               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a>
-
-Mir322               Mir322  -3.194159  9.012888 107.34926  3.732413e-25  1.935765e-23 0.07536483     7074               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a>
-
-Mir199b             Mir199b  -5.520119  4.792610 102.10724  5.259607e-24  2.609223e-22 0.13417024      370             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a>
-
-Mir181a-2         Mir181a-2  -3.000177  7.637692 101.38361  7.578821e-24  3.603098e-22 0.06896654     2817         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-2'>Mir181a-2</a>
-
-Mir125b-2         Mir125b-2  -2.987759  8.144514  91.72544  9.957640e-22  4.488356e-20 0.07737381     3837         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a>
-
-Dnm3os               Dnm3os  -3.331215  6.686950  91.67250  1.022763e-21  4.488356e-20 0.08810497     1401               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'>Dnm3os</a>
-
-Mir184               Mir184  -5.111350  4.234160  84.35542  4.133639e-20  1.686711e-18 0.13502324      247               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a>
-
-Mir215               Mir215  -3.058208  6.447966  84.35278  4.139167e-20  1.686711e-18 0.08138517     1182               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'>Mir215</a>
-
-Mir133b             Mir133b  -8.383611  3.584760  83.96681  5.031517e-20  1.960318e-18 0.17482280      159             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'>Mir133b</a>
-
-Mir150               Mir150  -2.883446  8.307765  83.91918  5.154210e-20  1.960318e-18 0.08008123     4229               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir150'>Mir150</a>
-
-Mir3074-2         Mir3074-2  -2.778308  7.935651  83.74839  5.619282e-20  2.040616e-18 0.07424646     3470         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'>Mir3074-2</a>
-
-Mir24-2             Mir24-2  -2.778307  7.935651  83.71222  5.723024e-20  2.040616e-18 0.07427992     3470             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'>Mir24-2</a>
-
-Mir193               Mir193   5.176579  4.801090  83.19222  7.445011e-20  2.574169e-18 0.14794861      421               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'>Mir193</a>
-
-Scarna17           Scarna17   2.182159  9.244479  81.91330  1.421894e-19  4.771710e-18 0.04982909     9224           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Scarna17'>Scarna17</a>
-
-Mir214               Mir214  -3.271172  6.271755  80.43948  2.997458e-19  9.771712e-18 0.09566584     1048               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'>Mir214</a>
-
-Snord104           Snord104   2.330488 11.053611  79.50529  4.809369e-19  1.524303e-17 0.05915990    33458           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a>
-
-Mir200a             Mir200a   7.201555  4.139422  77.35503  1.428304e-18  4.365755e-17 0.19287764      264             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'>Mir200a</a>
-
-Mir200b             Mir200b   6.525423  5.752604  77.31985  1.453976e-18  4.365755e-17 0.26237966      888             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200b'>Mir200b</a>
-
-Mir21                 Mir21   2.923147 13.825255  75.51798  3.620938e-18  1.059357e-16 0.09395834   229120                 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a>
-
-Mir203               Mir203   1.956427  8.767610  75.17870  4.299815e-18  1.226522e-16 0.04381710     6739               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a>
-
-Mir155               Mir155  -3.886731  5.068563  73.81316  8.587210e-18  2.389758e-16 0.12522673      463               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a>
-
-Cyp3a25             Cyp3a25   8.681501  3.972085  72.29680  1.851471e-17  5.029829e-16 0.23125383      226             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'>Cyp3a25</a>
-
-Rabggtb             Rabggtb   1.934093 10.298211  72.02043  2.129809e-17  5.651422e-16 0.04596646    19535             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a>
-
-Mir23b               Mir23b  -2.100584 10.184110  71.44225  2.854935e-17  7.403367e-16 0.05416378    16387               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23b'>Mir23b</a>
-
-Snord52             Snord52   2.207491 10.217554  71.27974  3.100027e-17  7.860292e-16 0.05941483    18059             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord52'>Snord52</a>
-
-Gm5441               Gm5441  -6.881248  3.538457  70.05615  5.764004e-17  1.429724e-15 0.20097284      142               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm5441'>Gm5441</a>
-
-Mir153               Mir153  -6.857671  3.517446  69.37600  8.137282e-17  1.975455e-15 0.20158808      140               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir153'>Mir153</a>
-
-Mir132               Mir132  -2.858294  5.938312  64.52507  9.531204e-16  2.265647e-14 0.09274248      857               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'>Mir132</a>
-
-1110038B12Rik 1110038B12Rik   2.195962 11.253090  62.92015  2.152583e-15  5.012443e-14 0.06712174    37066 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1110038B12Rik'>1110038B12Rik</a>
-
-Snord91a           Snord91a   2.654072  6.557504  62.40549  2.795431e-15  6.379174e-14 0.08637410     1437           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'>Snord91a</a>
-
-[1] @@ 416 tags significant at adj p= 0.05
-
-
-</pre>
-
-<div class="toolFormTitle">Other log output</div>
-
-<pre>
-
-## Toolfactory generated command line = Rscript - None None
-
-Got TCols=1 5 6 7; CCols=2 3 4 8
-
-R version 3.0.2 (2013-09-25)
-
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-locale:
-
- [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8     LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=en_AU.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
-
-attached base packages:
-
-[1] parallel  splines   methods   stats     graphics  grDevices utils     datasets  base
-
-other attached packages:
-
- [1] RColorBrewer_1.0-5      DESeq2_1.2.10           RcppArmadillo_0.4.400.0 Rcpp_0.11.2             GenomicRanges_1.14.4    XVector_0.2.0           IRanges_1.20.7          BiocGenerics_0.8.0      edgeR_3.4.2             limma_3.18.13           gplots_2.15.0           stringr_0.6.2
-
-loaded via a namespace (and not attached):
-
- [1] annotate_1.40.1      AnnotationDbi_1.24.0 Biobase_2.22.0       bitops_1.0-6         caTools_1.17.1       DBI_0.3.0            gdata_2.13.3         genefilter_1.44.0    grid_3.0.2           gtools_3.4.1         KernSmooth_2.23-13   lattice_0.20-29      locfit_1.5-9.1       RSQLite_0.11.4       stats4_3.0.2         survival_2.37-7      XML_3.98-1.1         xtable_1.7-4
-
-
-</pre>
-
-<div class="toolFormTitle">All output files available for downloading</div>
-
-<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>
-
-<tr><td><a href="DESeq2.log">DESeq2.log</a></td><td>10.5 KB</td></tr>
-<tr class="odd_row"><td><a href="DESeq2_edgeRtest_MA_plot.pdf">DESeq2_edgeRtest_MA_plot.pdf</a></td><td>15.0 KB</td></tr>
-<tr><td><a href="DESeq2_edgeRtest_PCA_plot.pdf">DESeq2_edgeRtest_PCA_plot.pdf</a></td><td>4.9 KB</td></tr>
-<tr class="odd_row"><td><a href="DESeq2_edgeRtest_dispersion_estimates.pdf">DESeq2_edgeRtest_dispersion_estimates.pdf</a></td><td>190.6 KB</td></tr>
-<tr><td><a href="DESeq2_edgeRtest_qqplot.pdf">DESeq2_edgeRtest_qqplot.pdf</a></td><td>13.3 KB</td></tr>
-<tr class="odd_row"><td><a href="DESeq2_edgeRtest_sample_distance_plot.pdf">DESeq2_edgeRtest_sample_distance_plot.pdf</a></td><td>9.5 KB</td></tr>
-<tr><td><a href="Differential.log">Differential.log</a></td><td>1.4 KB</td></tr>
-<tr class="odd_row"><td><a href="Differential_Counts.Rscript">Differential_Counts.Rscript</a></td><td>28.3 KB</td></tr>
-<tr><td><a href="Differential_Counts_error.log">Differential_Counts_error.log</a></td><td>1.8 KB</td></tr>
-<tr class="odd_row"><td><a href="Differential_Counts_runner.log">Differential_Counts_runner.log</a></td><td>1.3 KB</td></tr>
-<tr><td><a href="Differential_rowsum_bar_charts.pdf">Differential_rowsum_bar_charts.pdf</a></td><td>6.3 KB</td></tr>
-<tr class="odd_row"><td><a href="Differential_venn_edgeRtest_significant_genes_overlap.pdf">Differential_venn_edgeRtest_significant_genes_overlap.pdf</a></td><td>9.7 KB</td></tr>
-<tr><td><a href="VOOM.log">VOOM.log</a></td><td>10.1 KB</td></tr>
-<tr class="odd_row"><td><a href="VOOM_edgeRtest_mean_variance_plot.pdf">VOOM_edgeRtest_mean_variance_plot.pdf</a></td><td>18.3 KB</td></tr>
-<tr><td><a href="VOOM_edgeRtest_qqplot.pdf">VOOM_edgeRtest_qqplot.pdf</a></td><td>17.5 KB</td></tr>
-<tr class="odd_row"><td><a href="edgeR.log">edgeR.log</a></td><td>10.4 KB</td></tr>
-<tr><td><a href="edgeR_edgeRtest_BCV_vs_abundance.pdf">edgeR_edgeRtest_BCV_vs_abundance.pdf</a></td><td>17.4 KB</td></tr>
-<tr class="odd_row"><td><a href="edgeR_edgeRtest_GoodnessofFit.pdf">edgeR_edgeRtest_GoodnessofFit.pdf</a></td><td>13.1 KB</td></tr>
-<tr><td><a href="edgeR_edgeRtest_MDSplot.pdf">edgeR_edgeRtest_MDSplot.pdf</a></td><td>4.9 KB</td></tr>
-<tr class="odd_row"><td><a href="edgeR_edgeRtest_qqplot.pdf">edgeR_edgeRtest_qqplot.pdf</a></td><td>15.2 KB</td></tr>
-<tr><td><a href="edgeR_edgeRtest_smearplot.pdf">edgeR_edgeRtest_smearplot.pdf</a></td><td>16.7 KB</td></tr>
-<tr class="odd_row"><td><a href="edgeR_edgeRtest_top_100_heatmap.pdf">edgeR_edgeRtest_top_100_heatmap.pdf</a></td><td>11.2 KB</td></tr>
-</table></div><br/>
-</div></body></html>
-
--- a/test-data/edgeRtest1out.xls	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1142 +0,0 @@
-Name	logFC	logCPM	LR	PValue	adj.p.value	Dispersion	totreads	liver_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam	liver_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam	liver_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam	liver_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam	liver_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam	liver_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam	liver_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam	liver_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam	heart_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam
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-Mir547	-6.66011335809291	2.02926234079525	32.1757247444912	1.40839495182909e-08	1.67393608337186e-07	0.356062106085492	42	0	0	0	0	11.1735364742662	6.18723758471189	3.70917200867911	6.52817021039044	0	0	0	0	15	8	10	9
-Mir881	6.92242693660381	2.43150015105785	31.8703484033748	1.64813993283766e-08	1.93868831275028e-07	0.370279592330057	64	5.26974025936037	4.90063242902982	6.92330537224672	15.6495271151601	0	0	0	0	14	8	13	29	0	0	0	0
-Mir27b	-1.95516453426235	15.4186543579805	30.595089371413	3.17897329481618e-08	3.70123319325027e-07	0.111845033647616	625308	18613.4754160979	21116.2125576359	13257.064663949	18851.7441462415	71776.563603391	59949.6917676697	79898.532404555	66936.2305761156	49450	34471	24893	34934	96357	77514	215408	92281
-Aqp9	8.19229863204226	3.5352617978307	30.3562801716902	3.59541023416974e-08	4.14380108806835e-07	0.749799110600422	161	16.5620408151326	1.22515810725746	56.9841288331076	4.31711092832003	0	0	0	0	44	2	107	8	0	0	0	0
-Scnn1b	-6.7550624116295	2.10304596442489	30.2150787499351	3.86691357845228e-08	4.41214839301405e-07	0.435413067052871	45	0	0	0	0	14.8980486323549	3.86702349044493	4.08008920954702	6.52817021039044	0	0	0	0	20	5	11	9
-Mir181b-2	-3.54546890200837	3.48002491544831	30.1957160084194	3.90571232971685e-08	4.41229482000686e-07	0.240287369758545	154	0.752820037051481	0.612579053628728	2.66280975855643	2.15855546416002	13.4082437691194	13.1478798675128	32.6407136763762	13.7816926663798	2	1	5	4	18	17	88	19
-Mir100	-2.08208383567209	9.45805496813327	29.9934658553768	4.33504630524858e-08	4.81686883150544e-07	0.127291307489351	8837	260.852142838338	346.71974435386	142.726603058625	325.402236222123	1667.09164196051	1175.57514109526	445.100641041494	1268.64107755254	693	566	268	603	2238	1520	1200	1749
-Terc	5.21771020547043	2.45892803726519	29.9875603383992	4.34826897147292e-08	4.81686883150544e-07	0.271012413195945	66	8.2810204075663	11.0264229653171	5.32561951711286	8.09458299060006	0.744902431617745	0	0	0	22	18	10	15	1	0	0	0
-Mir1943	-2.61041648518405	4.04592065704575	29.6095212454359	5.28447244716874e-08	5.79767602136493e-07	0.150350529501716	215	3.01128014820593	4.90063242902982	2.13024780684514	8.09458299060006	30.5409996963275	30.1627832254705	23.3677836546784	26.8380330871607	8	8	4	15	41	39	63	37
-Mir592	5.26455131499402	2.51336669857798	29.3766649205822	5.95908885183844e-08	6.47554321899777e-07	0.295160714986275	70	10.5394805187207	9.18868580443092	4.26049561369029	9.71349958872008	0.744902431617745	0	0	0	28	15	8	18	1	0	0	0
-Gm5424	6.51058257196433	3.6277355700989	28.3912820406119	9.91106210410071e-08	1.06101806985287e-06	0.633362236913299	181	35.0061317228939	3.06289526814364	41.007270281769	2.69819433020002	0.744902431617745	0	0	0	93	5	77	5	1	0	0	0
-Mir1a-1	-6.40640610166023	1.96181506629844	28.3837008582372	9.94995034831353e-08	1.06101806985287e-06	0.385914420297783	42	0	0	0	0	2.97960972647098	3.86702349044493	8.90201282082987	6.52817021039044	0	0	0	0	4	5	24	9
-Mir488	-4.6205195277827	2.68764552181444	28.1369449830819	1.13027509862125e-07	1.194114710673e-06	0.343799313074837	78	0.376410018525741	0.612579053628728	0.532561951711286	0	19.3674632220614	4.64042818853392	12.611184829509	6.52817021039044	1	1	1	0	26	6	34	9
-Mir700	-3.09486213889018	3.85060319268209	27.6111662260505	1.48320064530137e-07	1.55259810668703e-06	0.226307066994367	177	1.8820500926287	3.67547432177237	1.06512390342257	5.39638866040004	23.8368778117678	39.4436396025383	11.4984332269053	29.0140898239575	5	6	2	10	32	51	31	40
-Snord33	1.27159425930436	7.86760196119859	26.3468118464628	2.85289441410394e-07	2.95922956953873e-06	0.0542381696925429	3442	244.290102023206	366.322274069979	377.586423763302	324.862597356083	140.786559575754	150.813916127352	114.984332269053	137.816926663798	649	598	709	602	189	195	310	190
-Gas5	1.23567952981659	10.6560730401044	26.0110899566739	3.39461779491679e-07	3.48942243603609e-06	0.05436572567263	24887	2148.92479576345	2360.87967268512	2450.85010177534	2092.1798836371	949.750600312625	774.178102787076	1251.8455529292	866.070581245132	5709	3854	4602	3877	1275	1001	3375	1194
-Hgd	6.1563594319405	3.3051855066134	25.8054782137686	3.776151667588e-07	3.84695451135527e-06	0.625414653477864	137	20.7025510189157	2.45031621451491	37.8118985715013	3.23783319624003	0	0.773404698088987	0	0	55	4	71	6	0	1	0	0
-Mir30e	-1.85723201668611	13.6073354406477	25.4678357159928	4.49819843898125e-07	4.54198621139612e-06	0.121988367526065	186796	5967.60443370709	6574.19840354351	2870.50891972383	6182.10284935429	17494.7785089744	16148.690096098	28479.0226826382	16115.8761927172	15854	10732	5390	11456	23486	20880	76780	22218
-Mir30b	-1.34107822414692	9.39851902833947	25.3163005367251	4.86579335958129e-07	4.8700615993704e-06	0.0650633323488782	10011	494.602764342823	430.030495647367	238.055192414945	363.176956844923	907.291161710413	860.799428973042	1254.07105613441	843.584661631565	1314	702	447	673	1218	1113	3381	1163
-Mir144	-2.55785018805589	10.1640960703833	24.5353302610081	7.29596930339336e-07	7.23887041319289e-06	0.227712790159503	13709	272.144443394111	286.686997098245	177.343129919858	597.380224706285	2855.21102039082	1869.31915528108	389.463060911307	2740.38078387279	723	468	333	1107	3833	2417	1050	3778
-Mir324	-2.28424718120018	4.17315991586526	23.9323140747965	9.97827456151237e-07	9.81483730576346e-06	0.151576006194663	248	3.76410018525741	3.06289526814364	8.52099122738058	8.09458299060006	31.2859021279453	23.2021409426696	36.3498856850553	23.211271859166	10	5	16	15	42	30	98	32
-Mir328	-1.36751360079392	7.40878077961745	23.8145973935344	1.06074962080877e-06	1.03445753619043e-05	0.0692261870455037	2362	114.428645631825	92.4994370979379	66.5702439639108	104.689940011761	297.21607021548	211.912887276382	203.633543276483	265.478921889211	304	151	125	194	399	274	549	366
-Taf1d	1.59487824983541	6.5338407262267	23.4453936800637	1.28510058178821e-06	1.24262691849182e-05	0.0919378143797076	1421	134.378376613689	106.588755331399	181.603625533549	124.116939189201	39.4798288757405	43.3106630929833	57.8630833353942	39.1690212623426	357	174	341	230	53	56	156	54
-Mirlet7b	-1.08960892589461	9.41902411828326	23.4221409471161	1.30072801904636e-06	1.2471686300268e-05	0.0467359778440644	10012	480.299183638845	481.48713615218	348.828078370892	438.186759224483	1000.40396566263	795.060029635478	1106.44601018898	818.922685281201	1276	786	655	812	1343	1028	2983	1129
-Snord72	1.83779702132032	5.33493032320198	23.2713228050061	1.40682046673181e-06	1.33765179378417e-05	0.112824081350989	603	59.8491929455928	62.4830634701303	65.5051200604882	54.5035254700404	15.6429510639726	18.5617127541357	21.1422804494709	11.605635929583	159	102	123	101	21	24	57	16
-Mir141	2.88915086991569	5.67544057110177	23.2108537994482	1.45175647773815e-06	1.36897036454482e-05	0.279514947463099	835	181.806038947933	73.5094864354474	22.9001639235853	64.2170250587605	16.3878534955904	13.1478798675128	5.56375801301867	11.605635929583	483	120	43	119	22	17	15	16
-Proc	7.21629473958689	2.70683026697518	22.9043042488944	1.70270146697476e-06	1.59244456870345e-05	0.796393393323758	83	10.5394805187207	1.22515810725746	26.6280975855643	1.61891659812001	0	0	0	0	28	2	50	3	0	0	0	0
-Mir99b	-2.23913504318397	11.5020751055957	22.0783948637961	2.61739093423791e-06	2.42800248452476e-05	0.199042770144399	35086	956.834267092433	1235.57195116914	602.327567385465	1262.21530766757	7677.7093626841	5160.92955034781	1038.93907963102	5277.66293897787	2542	2017	1131	2339	10307	6673	2801	7276
-Mir34a	-2.07114523247407	3.75691724165632	21.961565698742	2.78165502995287e-06	2.55957128159373e-05	0.123060595589901	180	4.51692022230889	3.67547432177237	5.32561951711286	5.93602752644005	18.6225607904436	20.8819268484026	22.6259492529426	20.3098628767703	12	6	10	11	25	27	61	28
-Mir181b-1	-5.48921274049575	1.27181133630627	21.9354116154913	2.81982280976034e-06	2.57393426074924e-05	0.242122526338113	19	0	0	0	0	3.72451215808872	3.09361879235595	1.85458600433956	3.62676122799469	0	0	0	0	5	4	5	5
-Gm6307	-4.39870684316405	2.18858336553985	21.7305852165356	3.13748994928966e-06	2.84117145407897e-05	0.370895152041695	52	0	0	0	1.07927773208001	4.46941458970647	4.64042818853392	10.7565988251694	6.52817021039044	0	0	0	2	6	6	29	9
-Mir221	-1.07812210165087	8.58912556756594	21.663166046321	3.24971783399815e-06	2.91962838471802e-05	0.0488937958816463	5659	293.976224468604	234.005198486174	197.580484084887	262.264488895442	548.993092102278	473.32367523046	625.366400663298	438.112756341758	781	382	371	486	737	612	1686	604
-Mtmr2	-2.22116729273076	5.29859867913154	21.4813923350997	3.57278773873821e-06	3.18480532023461e-05	0.184322775413225	580	7.52820037051481	9.18868580443092	22.9001639235853	14.5702493830801	49.9084629183889	54.9117335643181	102.373147439544	44.2464869815352	20	15	43	27	67	71	276	61
-Mir30d	-1.5700505645796	13.4267132944328	21.4496924092332	3.63234285499244e-06	3.21279317639253e-05	0.105319950550229	161730	6034.9818270232	6672.21105212411	3115.48741751102	6369.89717473621	15507.3788214182	14583.3189871659	21344.8012411448	14456.9956070324	16033	10892	5850	11804	20818	18856	57546	19931
-Mir17hg	1.27210083938303	13.2259422744804	21.2189407216706	4.09695720946436e-06	3.59586782769142e-05	0.0709557697736229	145451	12165.5717987519	14813.3866748499	13237.3598717357	13978.8051859003	5533.13526205661	5174.07743021532	5994.39288322631	5737.5362626876	32320	24182	24856	25904	7428	6690	16161	7910
-1700003L19Rik	-5.6106648861221	1.40157648597084	21.0598947754594	4.45148145598171e-06	3.8772063673856e-05	0.34919246441115	23	0	0	0	0	3.72451215808872	3.09361879235595	4.45100641041494	1.45070449119788	0	0	0	0	5	4	12	2
-Mir92-1	1.27208728155686	13.225932249746	20.83739364254	4.99972167212147e-06	4.32172911203833e-05	0.0722548060893577	145450	12165.5717987519	14813.3866748499	13236.827309784	13978.8051859003	5533.13526205661	5174.07743021532	5994.39288322631	5737.5362626876	32320	24182	24855	25904	7428	6690	16161	7910
-Dcaf11	4.49868505204508	3.00449999491973	20.7186493289855	5.31953836084826e-06	4.5636039622014e-05	0.492589105168061	105	11.668710574298	3.67547432177237	30.3560312475433	4.31711092832003	0.744902431617745	0.773404698088987	0	0.725352245598938	31	6	57	8	1	1	0	1
-Rpph1	1.90981175506483	4.84751487377934	20.6554906053639	5.49792966032868e-06	4.64021174764464e-05	0.132837721736145	430	47.0512523157176	29.4037945741789	54.8538810262625	39.9332760869603	7.44902431617745	11.6010704713348	14.8366880347165	10.8802836839841	125	48	103	74	10	15	40	15
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-Mir186	-1.32751183499243	10.3693859127252	20.6440640284678	5.53083959403743e-06	4.64021174764464e-05	0.0788254366048164	18235	766.747207736934	878.438362903596	393.030720362929	976.206708666368	2193.73766111426	1868.54575058299	1537.45179759749	1966.42993781872	2037	1434	738	1809	2945	2416	4145	2711
-Mirlet7i	-1.2364401524856	10.7623495817031	20.5423256221769	5.83271615690353e-06	4.85775849272039e-05	0.0690881136567856	26242	950.435296777495	923.156633818493	1190.27596207472	1074.42098228565	2244.39102646426	1862.35851299828	3632.76306530032	2008.50036806346	2525	1507	2235	1991	3013	2408	9794	2769
-Mir1960	-3.97365882122652	1.82974168570486	19.7765382288987	8.70446207821658e-06	7.19695016756892e-05	0.280749037728032	36	0	0	0.532561951711286	0.539638866040004	4.46941458970647	5.41383288662291	5.56375801301867	4.35211347359363	0	0	1	1	6	7	15	6
-Atf5	6.49323151909569	2.13557412776247	19.5695873517246	9.70010812448817e-06	7.96246285614461e-05	0.721655869823689	50	6.39897031493759	0.612579053628728	14.3791726962047	2.69819433020002	0	0	0	0	17	1	27	5	0	0	0	0
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-Mir34b	-4.03579710334285	2.21737061689032	19.1766918105239	1.19159517189387e-05	9.64262475979363e-05	0.403917173085682	54	1.12923005557722	0	0	0	5.95921945294196	4.64042818853392	11.4984332269053	4.35211347359363	3	0	0	0	8	6	31	6
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-Mir137	5.73150408678226	1.59255424435437	18.5911172800407	1.61973519549994e-05	0.000127456404004513	0.43564853832054	29	4.89333024083463	4.90063242902982	1.06512390342257	3.23783319624003	0	0	0	0	13	8	2	6	0	0	0	0
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-Mir320	-1.417807675918	6.77936159714582	18.1473156304664	2.0445743283858e-05	0.000157625628965419	0.0970223952510303	1651	68.5066233716848	64.9333796846452	42.6049561369029	60.9791918625205	135.57224255443	127.611775184683	238.499760158067	130.563404207809	182	106	80	113	182	165	643	180
-Mir190	-2.06186671147307	3.5998220294058	18.0095366523899	2.19801077073476e-05	0.000168317469087809	0.152993290679203	163	3.76410018525741	5.51321148265855	2.66280975855643	5.39638866040004	14.8980486323549	14.6946892636907	24.1096180564142	18.1338061399734	10	9	5	10	20	19	65	25
-Eci2	6.9439041935401	2.48959191851546	17.7164311100808	2.56402263684653e-05	0.000195036655242793	1.07311229890853	69	9.41025046314352	1.83773716088618	21.8350400201627	0	0	0	0	0	25	3	41	0	0	0	0	0
-Gm10768	6.67795356804963	2.30119145986701	17.5669627862943	2.77365422621582e-05	0.000209585395504123	0.965376367909245	59	9.78666048166926	0	15.4442965996273	2.15855546416002	0	0	0	0	26	0	29	4	0	0	0	0
-0610005C13Rik	4.33430685796486	3.22549471895908	17.528736488906	2.82997915388519e-05	0.000211677617768981	0.608471609830426	126	15.0564007410296	3.67547432177237	37.27933661979	3.23783319624003	0	1.54680939617797	0	1.45070449119788	40	6	70	6	0	2	0	2
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-Tmem205	4.17237062275844	3.00561070371572	17.2103088150022	3.34615654181685e-05	0.000247919780143703	0.565035895321497	106	13.1743506484009	2.45031621451491	30.3560312475433	3.23783319624003	1.48980486323549	0	0.370917200867911	0.725352245598938	35	4	57	6	2	0	1	1
-Snord4a	1.53801116007182	6.21817544635495	17.0751119198907	3.59300928476077e-05	0.000264491844768519	0.118420243440335	1151	121.95684600234	120.065494511231	87.8727220323622	103.071023413641	37.2451215808872	30.1627832254705	57.4921661345263	22.4859196135671	324	196	165	191	50	39	155	31
-Mir3107	-1.51664159875239	10.2873979799643	16.9393352070726	3.85935213362642e-05	0.000282276973363317	0.124378994027959	16873	296.987504616809	380.41159230344	1067.78671318113	843.995186486567	1945.68515138555	2140.78420431032	1468.46119823606	1853.27498750529	789	621	2005	1564	2612	2768	3959	2555
-Mir486	-1.51664148007241	10.2873979799643	16.9213390276156	3.89611278839383e-05	0.000283150617296647	0.12451180641996	16873	296.987504616809	380.41159230344	1067.78671318113	843.995186486567	1945.68515138555	2140.78420431032	1468.46119823606	1853.27498750529	789	621	2005	1564	2612	2768	3959	2555
-Snhg1	0.980597513898155	11.2837096888593	16.9017689204076	3.9364881274721e-05	0.000284274237559853	0.053394852582613	37837	3080.91600163319	3546.83272051034	3449.93632318571	3151.49097767362	1696.1428367936	1460.9614746901	1889.82313842201	1655.97917670237	8185	5790	6478	5840	2277	1889	5095	2283
-Mir674	-1.82542509565834	4.82285792788346	16.8672226859914	4.00879026718801e-05	0.000287674823576196	0.155466064049001	392	9.41025046314352	13.476739179832	5.85818146882415	19.9666380434802	51.3982677816244	34.0298067159154	48.9610705145643	37.7183167711448	25	22	11	37	69	44	132	52
-2610019E17Rik	1.62113501139589	5.89267132002437	16.8268839421272	4.09490701665377e-05	0.000292018056625122	0.131012768408947	892	93.3496845943837	120.065494511231	52.191071267706	83.6440242362007	33.5206094227985	17.0149033579577	31.8988792746404	30.4647943151554	248	196	98	155	45	22	86	42
-Mir291a	5.48313063221466	1.42869805781767	16.6950162643579	4.3896163749103e-05	0.000311090203961035	0.431873205787426	24	3.76410018525741	4.90063242902982	2.13024780684514	1.07927773208001	0	0	0	0	10	8	4	2	0	0	0	0
-Sra1	5.14603544431495	1.23718853524288	16.6688737955746	4.45052776064607e-05	0.000313460010796121	0.248666331699246	19	2.63487012968019	1.22515810725746	2.13024780684514	3.23783319624003	0	0	0	0	7	2	4	6	0	0	0	0
-Mir741	4.35308428126377	1.81656232793202	16.6316520890867	4.53872281396331e-05	0.000317710596977432	0.378055298017873	36	3.76410018525741	6.73836958991601	2.13024780684514	5.39638866040004	0	0	0	0.725352245598938	10	11	4	10	0	0	0	1
-Mir298	-3.94434405607657	1.83139145353586	16.2612707580985	5.51803674392698e-05	0.000383907312489066	0.404281561845252	37	0.376410018525741	0	0.532561951711286	0	4.46941458970647	1.54680939617797	7.41834401735823	5.07746571919256	1	0	1	0	6	2	20	7
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-Gm16596	-4.82899955725277	0.942786439456933	16.1147635150582	5.96174681092608e-05	0.00040978030790763	0.179133307406571	12	0	0	0	0	2.23470729485323	2.32021409426696	1.11275160260373	2.17605673679681	0	0	0	0	3	3	3	3
-Mir30c-1	-1.42220731103062	5.71194886484988	16.0808033155748	6.06962010242324e-05	0.000414696798614666	0.104647032731209	732	29.7363914635335	31.2415317350651	18.639668309895	32.9179708284403	75.2351455933922	68.8330181299198	74.5543573744502	83.4155082438778	79	51	35	61	101	89	201	115
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-2310001H17Rik	3.58428400419001	2.24442690645195	16.0087482840433	6.30504665559748e-05	0.000425683919173771	0.352731951590502	54	4.89333024083463	5.51321148265855	7.45586732395801	8.09458299060006	0	0	0	2.17605673679681	13	9	14	15	0	0	0	3
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-Mir680-2	2.81016620132679	3.29905245912823	15.7957708667004	7.05600497170873e-05	0.000470812963316939	0.316057949871155	134	18.820500926287	10.4138439116884	23.9652878270079	4.85674979436004	1.48980486323549	4.64042818853392	1.48366880347165	0.725352245598938	50	17	45	9	2	6	4	1
-Ank1	-1.51817010474615	11.2887123048386	15.6854094796533	7.47989553557263e-05	0.000496195395702812	0.13487533441033	33776	594.72782927067	760.82318460688	2135.57342636226	1687.99037297313	3895.83971736081	4287.75564620534	2939.88973407906	3710.90208848417	1580	1242	4010	3128	5230	5544	7926	5116
-Mir466f-3	3.55526593924808	3.27987930172684	15.6391420081383	7.66514180624486e-05	0.000505544901787594	0.509611720321542	134	19.9497309818643	2.45031621451491	33.551402957811	3.23783319624003	1.48980486323549	0.773404698088987	1.11275160260373	1.45070449119788	53	4	63	6	2	1	3	2
-0610012G03Rik	5.00676582631102	1.15611396657999	15.530372952363	8.11902004511487e-05	0.000532402406406671	0.240935839917648	17	2.25846011115444	2.45031621451491	2.66280975855643	1.07927773208001	0	0	0	0	6	4	5	2	0	0	0	0
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-4930581F22Rik	4.27156743666297	1.71729906230074	15.4593377301288	8.42994173468423e-05	0.000546509290867881	0.399514955315848	33	3.76410018525741	1.83773716088618	6.92330537224672	3.23783319624003	0	0	0.370917200867911	0	10	3	13	6	0	0	1	0
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-6430411K18Rik	-5.31898602947628	1.18068438167062	15.2786060675236	9.27611815591187e-05	0.000591287755077958	0.575163809877839	17	0	0	0	0	0.744902431617745	1.54680939617797	1.85458600433956	6.52817021039044	0	0	0	0	1	2	5	9
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-0610008F07Rik	4.18322641944353	1.74022139632347	15.1119581190244	0.000101319449264228	0.000638704373538586	0.383719723622995	34	6.02256029641185	4.90063242902982	2.66280975855643	2.15855546416002	0	0	0	0.725352245598938	16	8	5	4	0	0	0	1
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-4833418N02Rik	1.89246300895627	3.64367143451302	13.6807235071753	0.000216667113856488	0.00127431534489821	0.173547254179735	170	12.7979406298752	17.1522135016044	27.1606595372756	13.4909716510001	6.7041218845597	2.32021409426696	5.19284081215076	4.35211347359363	34	28	51	25	9	3	14	6
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-AW495222	5.70358294256303	2.93735738560613	13.2417244298067	0.000273785921073402	0.00158573470022717	1.28500704559326	101	13.5507606669267	0	33.551402957811	0.539638866040004	0	0	0	0.725352245598938	36	0	63	1	0	0	0	1
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-Gm16157	5.30927238982004	1.34792882149269	12.771094923717	0.000352016726297873	0.00197857677195011	0.711879772823525	22	4.14051020378315	3.06289526814364	3.19537171026772	0	0	0	0	0	11	5	6	0	0	0	0	0
-Phyhd1	5.23960166659277	1.28258631948388	12.761734288476	0.000353782722124899	0.0019787553232574	0.660971832490644	20	1.8820500926287	0.612579053628728	6.39074342053543	1.07927773208001	0	0	0	0	5	1	12	2	0	0	0	0
-Sec16b	5.0670703954575	2.2681415176373	12.7482346070013	0.000356345346237498	0.00198336604905846	1.01651406350117	57	8.2810204075663	0	16.5094205030499	1.61891659812001	0	0	0.370917200867911	0	22	0	31	3	0	0	1	0
-D830005E20Rik	-4.39448068938813	0.771304509662565	12.7122700604847	0.000363264195301064	0.00201206042154619	0.141933620715364	9	0	0	0	0	2.23470729485323	1.54680939617797	1.11275160260373	0.725352245598938	0	0	0	0	3	2	3	1
-Mir297c	-5.46028214667453	1.16310624569704	12.6858246432756	0.000368438065669653	0.00203085909627572	0.98423436642105	16	0	0	0	0	5.21431702132421	4.64042818853392	0	2.17605673679681	0	0	0	0	7	6	0	3
-Mir210	-1.55181848254692	5.30748652269599	12.6346249640967	0.000378667171342058	0.00207720789664081	0.160090784081351	592	26.3487012968019	12.2515810725746	19.7047922133176	19.4269991774402	60.3370969610373	34.8032114140044	90.5037970117704	42.7957824903373	70	20	37	36	81	45	244	59
-Snord43	1.0724635094025	7.57752500010569	12.5209323640319	0.000402418025745375	0.00217646953900115	0.0831700356050766	2975	250.689072338143	271.985099811155	268.411223662488	236.361823325522	90.1331942257471	92.8085637706784	201.037122870408	101.549314383851	666	444	504	438	121	120	542	140
-Chka	5.16699807309474	1.27093683631409	12.5079968264951	0.00040521383696247	0.00217646953900115	0.646760843961031	20	3.38769016673167	0.612579053628728	4.79305756540158	0.539638866040004	0	0	0	0	9	1	9	1	0	0	0	0
-Meg3	-4.82330172761411	0.937921787971393	12.5028578410907	0.000406329978523492	0.00217646953900115	0.528764555179598	12	0	0	0	0	2.97960972647098	3.09361879235595	1.48366880347165	0	0	0	0	0	4	4	4	0
-Pknox1	4.89944680170079	1.10919918577088	12.496528888137	0.000407708833339551	0.00217646953900115	0.448595704681969	16	2.25846011115444	1.22515810725746	3.727933661979	0.539638866040004	0	0	0	0	6	2	7	1	0	0	0	0
-Snhg12	1.14167877936141	8.41345066353433	12.4958643239978	0.00040785389192246	0.00217646953900115	0.0959907545464561	5403	479.169953583268	496.801612492898	491.022119477806	400.951677467723	173.562266566935	139.212845656018	386.866640505231	143.61974462859	1273	811	922	743	233	180	1043	198
-BC024386	5.8839804957065	1.74985172014701	12.4942464278731	0.00040820725797217	0.00217646953900115	1.15280541507025	35	7.52820037051481	0.612579053628728	7.45586732395801	0	0	0	0	0	20	1	14	0	0	0	0	0
-Clip4	-4.61840308730762	0.83639259989645	12.468097368021	0.000413961525770984	0.00219688419025438	0.351654560530334	10	0	0	0	0	0.744902431617745	3.09361879235595	0.741834401735823	2.17605673679681	0	0	0	0	1	4	2	3
-Mir5109	2.53995860641125	6.27289124905161	12.3774719523889	0.000434545111972646	0.00229544431833699	0.437367055362434	1282	206.272690152106	24.5031621451491	254.564612917995	24.8233878378402	21.6021705169146	9.28085637706784	39.3172232919986	16.6831016487756	548	40	478	46	29	12	106	23
-Mir340	1.07425135199083	8.56601383462481	12.3150626029681	0.000449317640241304	0.00236254114062363	0.0864547028702847	5906	452.821252286466	452.083341578001	734.935493361575	407.427343860203	214.531900305911	184.070318145179	392.43039851825	178.436652417339	1203	738	1380	755	288	238	1058	246
-Gm15787	4.81965375867379	1.03182680206019	12.2510725211142	0.000464990815436558	0.00243373633217024	0.406167431652228	14	0.752820037051481	3.67547432177237	1.06512390342257	2.15855546416002	0	0	0	0	2	6	2	4	0	0	0	0
-Mir878	4.64926579956119	0.974349957697144	12.1747184405386	0.000484416112234324	0.00252383006419801	0.291223458606252	13	1.12923005557722	0.612579053628728	2.66280975855643	2.15855546416002	0	0	0	0	3	1	5	4	0	0	0	0
-Mir668	-3.52129994082869	1.08090432140149	12.1407472953393	0.000493320047462953	0.00255853715525104	0.207929799163583	15	0.376410018525741	0	0	0	2.97960972647098	1.54680939617797	1.85458600433956	2.17605673679681	1	0	0	0	4	2	5	3
-Tardbp	3.26639735890803	2.17673793517601	12.0648228191212	0.000513821930353412	0.00265280915173413	0.464336478347039	52	6.77538033346333	4.2880533754011	10.6512390342257	1.61891659812001	0	0.773404698088987	1.11275160260373	0	18	7	20	3	0	1	3	0
-Snhg8	1.68922702390604	3.59241000016082	12.0323448532091	0.000522852596989171	0.00268727393317407	0.153879783915183	164	15.8092207780811	22.0528459306342	10.1186770825144	17.8080825793201	5.21431702132421	3.86702349044493	5.19284081215076	5.8028179647915	42	36	19	33	7	5	14	8
-D830015G02Rik	-4.00371870378144	1.28556444411428	11.8850471165561	0.000565863940687806	0.0028952948714116	0.531321627547746	19	0	0	0	0.539638866040004	2.23470729485323	3.86702349044493	0.741834401735823	5.8028179647915	0	0	0	1	3	5	2	8
-Mir450b	-1.669966329175	3.92884750600962	11.8198719908045	0.000586018888662336	0.00298503371412377	0.172866406803916	205	6.39897031493759	11.6390020189458	2.13024780684514	7.55494412456006	25.3266826750033	20.8819268484026	24.8514524581501	16.6831016487756	17	19	4	14	34	27	67	23
-Mir98	-0.972268646438484	8.07481173834386	11.7905438404766	0.000595323414062094	0.00301895117975488	0.0735460143075853	4140	204.014230040951	156.820237728954	162.431395271942	201.824935898962	303.175289668422	273.785263123501	560.826807712282	282.887375783586	542	256	305	374	407	354	1512	390
-Fam120b	3.75131245111122	1.86837805067541	11.7409123378485	0.000611410447816025	0.00308681115468179	0.569615866289326	39	6.02256029641185	2.45031621451491	7.98842927566929	1.07927773208001	0	0	0.741834401735823	0	16	4	15	2	0	0	2	0
-Bmp1	3.66149432091536	1.8801824905183	11.665863530871	0.000636573890071191	0.00319969519194374	0.530656556816999	39	4.89333024083463	1.83773716088618	9.58611513080315	1.61891659812001	0	0.773404698088987	0.370917200867911	0	13	3	18	3	0	1	1	0
-Snora24	1.63741748625607	3.55764726367568	11.497464026246	0.00069691209253932	0.00348761709468142	0.149950706883842	160	15.8092207780811	20.215108769748	10.1186770825144	17.2684437132801	5.21431702132421	3.86702349044493	5.19284081215076	5.8028179647915	42	33	19	32	7	5	14	8
-1700001L05Rik	4.88144873590407	1.10745614758089	11.4650741302457	0.000709164020922668	0.00353343296014308	0.573423120149153	16	2.25846011115444	0	3.19537171026772	2.15855546416002	0	0	0	0	6	0	6	4	0	0	0	0
-2700038G22Rik	-1.25849182157	5.88412568701528	11.4274331531451	0.000723675930059116	0.00359006189651066	0.119235436185093	872	33.8769016673167	35.5295851104662	36.2142127163675	31.2990542303202	80.4494626147164	77.3404698088987	119.435338679467	49.3239527007278	90	58	68	58	108	100	322	68
-Slc25a44	2.74553045634904	2.63138227225825	11.2881477473413	0.000780034653951903	0.00385289844224728	0.401109003764869	77	8.2810204075663	1.83773716088618	17.5745444064724	5.93602752644005	1.48980486323549	0.773404698088987	1.11275160260373	1.45070449119788	22	3	33	11	2	1	3	2
-Il15ra	3.00551920736734	2.65160751389579	11.2720227673552	0.000786839449484599	0.00386975780974969	0.509183428949874	78	6.77538033346333	8.57610675080219	18.1071063581837	2.15855546416002	1.48980486323549	0	2.22550320520747	0	18	14	34	4	2	0	6	0
-1810026B05Rik	4.75071736662571	1.05600706004695	11.1589298507598	0.00083628149491269	0.00409526689139648	0.515986885031515	15	3.01128014820593	0.612579053628728	2.66280975855643	0.539638866040004	0	0	0	0	8	1	5	1	0	0	0	0
-Mtfr1	3.73003225422435	1.91330931155963	11.1142371717879	0.000856675520466582	0.00417720841389902	0.613480486146352	40	3.76410018525741	1.22515810725746	11.7163629376483	2.15855546416002	0	0.773404698088987	0.370917200867911	0	10	2	22	4	0	1	1	0
-Def8	4.90789710047339	1.11114655162227	11.096533385925	0.000864892407824037	0.00419932866947756	0.649697634051271	16	1.8820500926287	0.612579053628728	4.79305756540158	0.539638866040004	0	0	0	0	5	1	9	1	0	0	0	0
-0610007N19Rik	-1.91109645132926	3.00746728871961	11.0514352352139	0.00088618569914032	0.00428448255389451	0.213195586419926	104	3.76410018525741	1.22515810725746	3.19537171026772	3.23783319624003	12.6633413375017	8.50745167897886	15.5785224364523	7.25352245598938	10	2	6	6	17	11	42	10
-Mir222	-0.996993817462646	6.95463686071109	10.9709990222955	0.000925488536859068	0.00445562202766328	0.0807689646866884	1840	94.8553246684867	89.4365418297943	64.9725581087769	79.3269133078806	174.307168998552	128.385179882772	218.099314110332	134.190165435803	252	146	122	147	234	166	588	185
-1810008I18Rik	5.51339780558776	1.46232515362208	10.9112078435794	0.000955841823980723	0.00458241815614288	1.17648456256356	25	3.01128014820593	0	8.52099122738058	0.539638866040004	0	0	0	0	8	0	16	1	0	0	0	0
-Hspa13	4.5335645533697	0.923155569802891	10.8598794998719	0.000982700888940462	0.0046817828848222	0.364299931981324	12	1.12923005557722	0.612579053628728	3.19537171026772	1.07927773208001	0	0	0	0	3	1	6	2	0	0	0	0
-Tysnd1	4.67245989477327	0.97728986546695	10.8559756409294	0.000984774664642706	0.0046817828848222	0.482797884010873	13	0.752820037051481	0.612579053628728	3.727933661979	1.61891659812001	0	0	0	0	2	1	7	3	0	0	0	0
-Syce2	5.60456215929372	1.50299640923642	10.8209128992786	0.00100359992206734	0.00475148344845991	1.26696371509922	26	2.25846011115444	0	10.1186770825144	0.539638866040004	0	0	0	0	6	0	19	1	0	0	0	0
-4732471J01Rik	2.22905943203287	2.65196176434118	10.7525539076486	0.00104135513708647	0.00490986037775067	0.261444787615312	78	9.41025046314352	6.12579053628728	7.45586732395801	9.71349958872008	2.97960972647098	0	1.11275160260373	2.90140898239575	25	10	14	18	4	0	3	4
-Mir551b	4.26611873811836	0.813539566342934	10.7376057354544	0.00104980072958944	0.00492931124469773	0.146325189223804	10	1.50564007410296	1.22515810725746	0.532561951711286	1.61891659812001	0	0	0	0	4	2	1	3	0	0	0	0
-Trmt61b	4.96768908260398	1.18270780146598	10.7120164693483	0.0010644195900925	0.00497747029629319	0.773738385816363	18	4.14051020378315	1.22515810725746	2.66280975855643	0	0	0	0	0	11	2	5	0	0	0	0	0
-Mir128-2	-3.78373754460994	1.78339732783065	10.6675011607303	0.00109034315627492	0.00507788384208034	0.714609405506066	35	0	0.612579053628728	0	0.539638866040004	3.72451215808872	0	6.6765096156224	7.25352245598938	0	1	0	1	5	0	18	10
-Mir1b	-3.85021280938264	1.37783937763947	10.6302574176847	0.00111252227563716	0.00516011348171545	0.5875434000809	23	0	0	0	0.539638866040004	1.48980486323549	0.773404698088987	5.93467521388658	2.17605673679681	0	0	0	1	2	1	16	3
-Mir1a-2	-3.85009338813086	1.37783937763947	10.6208757299766	0.00111818079763341	0.00516536149837945	0.588513644740939	23	0	0	0	0.539638866040004	1.48980486323549	0.773404698088987	5.93467521388658	2.17605673679681	0	0	0	1	2	1	16	3
-Snord34	1.90390608992052	3.05424390245224	10.5999961177142	0.00113087897509442	0.00519686244671008	0.214688193528694	110	12.7979406298752	12.8641601262033	7.98842927566929	10.2531384547601	2.97960972647098	2.32021409426696	4.82192361128285	0.725352245598938	34	21	15	19	4	3	13	1
-Mir218-1	-4.02966665256279	0.645094641469066	10.5947222820167	0.00113410933324348	0.00519686244671008	0.0535461111607123	7	0	0	0	0	0.744902431617745	0.773404698088987	1.11275160260373	1.45070449119788	0	0	0	0	1	1	3	2
-U05342	3.31281709881391	2.21106562280467	10.5202106383261	0.00118076025095335	0.00538898978535108	0.596379396994342	54	7.52820037051481	1.83773716088618	12.7814868410709	1.61891659812001	0	0.773404698088987	1.11275160260373	0	20	3	24	3	0	1	3	0
-Mir190b	-2.97565754853409	1.252164062092	10.5071479244671	0.00118913651980964	0.00540559668965259	0.232689411631899	19	0	0.612579053628728	0	0.539638866040004	1.48980486323549	2.32021409426696	2.96733760694329	2.90140898239575	0	1	0	1	2	3	8	4
-Mir483	-4.04934244562863	0.651060211291411	10.4898498526861	0.00120032125582041	0.00543478790829798	0.0832379094928258	7	0	0	0	0	0.744902431617745	1.54680939617797	0.741834401735823	1.45070449119788	0	0	0	0	1	2	2	2
-Gm16023	4.53803195644533	2.35992965031332	10.4655844099125	0.00121619066761642	0.00548487569861792	1.15151574664551	61	5.64615027788611	0	21.8350400201627	1.61891659812001	0	0	0.741834401735823	0	15	0	41	3	0	0	2	0
-Snhg7	2.13792964635227	3.65141323422922	10.4572204775174	0.00122170967955348	0.00548807379673433	0.324086063919423	173	12.7979406298752	9.18868580443092	43.6700800403255	7.01530525852006	2.97960972647098	2.32021409426696	5.56375801301867	5.07746571919256	34	15	82	13	4	3	15	7
-Snord65	-2.14580419998559	4.72864529291437	10.2850879004778	0.00134109630909823	0.00600074858306307	0.371398200453088	320	10.9158905372465	10.4138439116884	10.1186770825144	8.09458299060006	55.1227799397131	69.6064228280088	2.96733760694329	49.3239527007278	29	17	19	15	74	90	8	68
-Schip1	-3.88047326506437	1.2021248273388	10.266705033868	0.00135452497117686	0.0060371601254406	0.618935763226322	17	0	0	0.532561951711286	0	4.46941458970647	4.64042818853392	0.370917200867911	2.17605673679681	0	0	1	0	6	6	1	3
-Mir3098	1.61240704288353	4.18647370891867	10.2460832253074	0.00136975126241499	0.00608126922340661	0.191902479688272	259	24.8430612226989	12.2515810725746	44.735203943748	18.8873603114001	8.93882917941294	11.6010704713348	7.41834401735823	5.07746571919256	66	20	84	35	12	15	20	7
-Bc1	1.71937890140519	6.08233428298449	10.2256826747664	0.00138498469802816	0.00612506798624082	0.251905498663074	1034	119.32197587266	32.4666898423226	206.101475312268	46.4089424794404	37.990024012505	34.0298067159154	19.2876944451314	31.9154988063533	317	53	387	86	51	44	52	44
-Snord123	-1.86495872183133	2.9794457728339	10.2155084134146	0.00139264597748347	0.00613517011702177	0.221948043620607	102	3.76410018525741	1.22515810725746	3.19537171026772	3.23783319624003	11.9184389058839	8.50745167897886	15.5785224364523	6.52817021039044	10	2	6	6	16	11	42	9
-Tmem41a	4.65538966305359	1.01032960633687	10.1935579633291	0.00140932126638837	0.00618475217288126	0.592503832690623	14	2.63487012968019	0	2.66280975855643	1.07927773208001	0	0	0	0	7	0	5	2	0	0	0	0
-Nagpa	2.97352460471784	1.43647698744846	10.0249506429243	0.00154433814628868	0.00675130201117006	0.269287136796875	24	2.25846011115444	3.06289526814364	3.727933661979	2.15855546416002	0.744902431617745	0	0.370917200867911	0	6	5	7	4	1	0	1	0
-Aes	3.23047856240698	3.06910239429643	9.97879758812414	0.00158353058690921	0.00689621526589085	0.724626715259297	113	15.4328107595554	0.612579053628728	31.4211551509659	1.61891659812001	0.744902431617745	0.773404698088987	1.85458600433956	1.45070449119788	41	1	59	3	1	1	5	2
-1810064F22Rik	4.64882868838756	1.00918245201978	9.93892927557888	0.00161819646190249	0.00702038845258839	0.634235826761283	14	3.01128014820593	1.22515810725746	2.13024780684514	0	0	0	0	0	8	2	4	0	0	0	0	0
-Cacnb2	-4.06302355816955	0.65099714984949	9.92842634511165	0.00162745612971598	0.00703381607578007	0.184783458472923	7	0	0	0	0	2.23470729485323	0.773404698088987	0.741834401735823	0.725352245598938	0	0	0	0	3	1	2	1
-Snora16a	1.50286567657067	4.93292122441817	9.91346035900646	0.00164074336699186	0.00706448370467063	0.18884377733825	464	45.5456122416146	26.953478359664	60.1795005433753	37.7747206228003	8.93882917941294	12.3744751694238	27.8187900650933	9.42957919278619	121	44	113	70	12	16	75	13
-Gm8883	4.91911838145189	1.11319601658003	9.89152843403226	0.00166041397844841	0.00712230206544977	0.886810026012311	16	1.50564007410296	0	5.32561951711286	1.07927773208001	0	0	0	0	4	0	10	2	0	0	0	0
-Zfp862	4.91910478654094	1.11319601658003	9.87749718644328	0.00167312367357036	0.00714994049267334	0.889880816099473	16	1.50564007410296	0	5.32561951711286	1.07927773208001	0	0	0	0	4	0	10	2	0	0	0	0
-Snord99	1.35178914626321	7.9464134418647	9.85562974243291	0.00169312831478341	0.00720843062376073	0.169702504284287	3970	350.437727247465	401.239280126817	355.751383743139	299.499570652202	93.8577063838358	61.8723758471189	314.166869135121	78.3380425246853	931	655	668	555	126	80	847	108
-A530013C23Rik	4.67075454704127	1.01144457530478	9.84837000626928	0.0016998230654676	0.00721003017731796	0.669417175490588	14	3.01128014820593	2.45031621451491	0	1.07927773208001	0	0	0	0	8	4	0	2	0	0	0	0
-Mir1249	-1.46196390939412	4.19931305895641	9.82633430101478	0.0017203085737606	0.00726989660244759	0.166204289312369	240	7.15179035198907	17.7647925552331	4.79305756540158	8.63422185664007	29.051194833092	30.1627832254705	19.2876944451314	26.8380330871607	19	29	9	16	39	39	52	37
-Gm10319	4.53304281290706	0.923052521660086	9.77251106903352	0.00177140354790803	0.00745819722569397	0.545741897178871	12	1.12923005557722	0.612579053628728	3.727933661979	0.539638866040004	0	0	0	0	3	1	7	1	0	0	0	0
-Mug-ps1	5.60817971617749	1.61849867045231	9.71926086467138	0.00182346835924321	0.00764918160991363	1.55477121145156	31	9.03384044461778	0	3.727933661979	0	0	0	0	0	24	0	7	0	0	0	0	0
-Gm16551	4.354131830696	0.863214695780697	9.68568288562137	0.00185709354868663	0.00776169867784414	0.396645406829089	11	1.8820500926287	0	1.59768585513386	1.61891659812001	0	0	0	0	5	0	3	3	0	0	0	0
-Pglyrp2	5.56628198650275	1.52062717057535	9.66528823530379	0.00187782297490911	0.0078196934831069	1.5286005087766	27	4.51692022230889	0	7.98842927566929	0	0	0	0	0	12	0	15	0	0	0	0	0
-Mir3071	-1.64821204185904	5.6368744677411	9.59854303938321	0.00194731817778514	0.00805540203810413	0.247041989240933	753	49.6861224453978	22.0528459306342	6.92330537224672	14.5702493830801	59.5921945294196	58.778757054763	111.275160260373	64.5563498583055	132	36	13	27	80	76	300	89
-Mir136	-1.64821282526039	5.6368744677411	9.59738565084825	0.0019485459794187	0.00805540203810413	0.247073475964354	753	49.6861224453978	22.0528459306342	6.92330537224672	14.5702493830801	59.5921945294196	58.778757054763	111.275160260373	64.5563498583055	132	36	13	27	80	76	300	89
-Snord93	-1.25494995245598	4.9078644847231	9.54842408438958	0.00200121158519751	0.00824325782927928	0.134055986983375	435	16.9384508336583	17.7647925552331	18.1071063581837	15.6495271151601	40.2247313073582	38.6702349044493	59.7176693397337	23.9366241047649	45	29	34	29	54	50	161	33
-Snord35a	1.70174715785811	3.3252210053787	9.52627964515323	0.00202550306451539	0.00830004911298375	0.198593315269338	131	9.41025046314352	14.0893182334607	20.7699161167402	9.71349958872008	5.95921945294196	4.64042818853392	2.59642040607538	3.62676122799469	25	23	39	18	8	6	7	5
-Snord23	2.88031870193835	2.69743879475134	9.52261884471627	0.00202954750440181	0.00830004911298375	0.586806680247479	83	10.5394805187207	2.45031621451491	19.1722302616063	3.23783319624003	1.48980486323549	0	2.59642040607538	0	28	4	36	6	2	0	7	0
-Tor2a	4.96504912312765	1.11925190379954	9.51300562087038	0.0020402071903048	0.00830838313528841	1.0145183891617	16	0.752820037051481	0	5.85818146882415	1.61891659812001	0	0	0	0	2	0	11	3	0	0	0	0
-Mir3096b	-1.25490289934181	4.9078644847231	9.50766947436512	0.00204614869501844	0.00830838313528841	0.134706201674069	435	16.9384508336583	17.7647925552331	18.1071063581837	15.6495271151601	40.2247313073582	38.6702349044493	59.7176693397337	23.9366241047649	45	29	34	29	54	50	161	33
-Mir330	-1.20825291558236	4.07448049484978	9.43463187929297	0.00212925824962758	0.00861519029370592	0.108534074025367	231	9.41025046314352	9.80126485805965	9.05355317909186	10.2531384547601	23.0919753801501	20.8819268484026	25.9642040607538	18.8591583855724	25	16	17	19	31	27	70	26
-Etfb	4.09388203108547	2.19374397059567	9.4213161204447	0.00214477530309658	0.00864730961425157	1.02983418513461	53	7.52820037051481	1.22515810725746	15.4442965996273	0	0	0.773404698088987	0.370917200867911	0	20	2	29	0	0	1	1	0
-Snord19	0.876063538519006	7.21003568721168	9.33474081083471	0.00224849891563529	0.00903358191105587	0.0736533400795967	2203	137.766066780421	192.349822839421	247.641307545748	182.397936721521	110.245559879426	91.2617543745005	123.515427889014	87.7676217174715	366	314	465	338	148	118	333	121
-A630019I02Rik	-3.15210527541715	0.934489707344401	9.3251997860741	0.00226023675505872	0.00904887767551578	0.18560808406755	12	0	0	0	0.539638866040004	2.23470729485323	0.773404698088987	1.48366880347165	2.17605673679681	0	0	0	1	3	1	4	3
-Mir350	-1.60121543359202	3.66362250766063	9.30565441155721	0.00228447691844911	0.00911394462919731	0.203881256962694	172	9.78666048166926	4.90063242902982	4.26049561369029	3.77747206228003	26.8164875382388	13.1478798675128	21.1422804494709	9.42957919278619	26	8	8	7	36	17	57	13
-Crem	3.18849826131013	1.10946210718244	9.26902714988639	0.00233061462335807	0.00926561423432597	0.222801951473068	16	1.8820500926287	1.22515810725746	1.59768585513386	2.69819433020002	0	0	0.370917200867911	0	5	2	3	5	0	0	1	0
-4930483K19Rik	3.68708641143103	1.43136350552071	9.24893122586361	0.00235632905873876	0.00933531755562821	0.593716597068376	24	3.01128014820593	1.83773716088618	5.85818146882415	0.539638866040004	0	0	0	0.725352245598938	8	3	11	1	0	0	0	1
-Mir185	-1.52777986019704	4.47414367933805	9.21460941178877	0.00240091428983647	0.00947904223080766	0.205682901562663	331	11.668710574298	14.0893182334607	8.52099122738058	8.63422185664007	29.051194833092	18.5617127541357	59.7176693397337	15.2323971575777	31	23	16	16	39	24	161	21
-1810058I24Rik	3.16972279689547	2.15278125335152	9.20827778497473	0.00240923235256968	0.00947908315269658	0.637376497428484	50	3.76410018525741	1.22515810725746	14.911734647916	3.23783319624003	0	1.54680939617797	0.741834401735823	0	10	2	28	6	0	2	2	0
-B830017H08Rik	-4.44239357520754	0.726906104594824	9.19329238199305	0.00242903586504193	0.00952415780760425	0.740706895714857	8	0	0	0	0	2.23470729485323	2.32021409426696	0	1.45070449119788	0	0	0	0	3	3	0	2
-Mir467c	2.92708834617398	1.77425408890849	9.10640612194132	0.00254715833512069	0.00995310842593392	0.441263037280115	35	4.51692022230889	1.83773716088618	7.45586732395801	1.61891659812001	0.744902431617745	0	0.370917200867911	0.725352245598938	12	3	14	3	1	0	1	1
-Snord49b	1.11873941989238	6.5987615159351	9.08854414693623	0.00257215541582898	0.0100164823531088	0.122736252183783	1485	147.929137280616	150.081868139038	65.5051200604882	158.653826615761	46.1839507603002	44.0840677910723	80.1181153874688	68.9084633318991	393	245	123	294	62	57	216	95
-AF357399	1.85711926889977	2.83472686690931	9.07537195172805	0.0025907487524393	0.0100224275886828	0.226204379786583	89	6.77538033346333	7.35094864354474	13.8466107444934	9.17386072268007	2.97960972647098	2.32021409426696	1.48366880347165	3.62676122799469	18	12	26	17	4	3	4	5
-1700123M08Rik	-3.46961229422527	1.04557348195539	9.0750182483449	0.00259124990242018	0.0100224275886828	0.462934892296261	14	0	0	0.532561951711286	0	4.46941458970647	2.32021409426696	1.11275160260373	0.725352245598938	0	0	1	0	6	3	3	1
-Mir503	-1.66833908183362	2.85131451985718	9.05034999145096	0.00262644536807953	0.0101242370438471	0.187155656042091	88	2.63487012968019	3.67547432177237	3.727933661979	2.15855546416002	8.93882917941294	12.3744751694238	8.53109561996196	9.42957919278619	7	6	7	4	12	16	23	13
-Gm5105	-4.08556021018168	0.651395980359643	8.99964033627207	0.00270032744138716	0.010373985220952	0.39219669962746	7	0	0	0	0	2.23470729485323	1.54680939617797	0.741834401735823	0	0	0	0	0	3	2	2	0
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-Snora17	2.24774829663748	3.50549089812129	8.93298185216231	0.00280066746910172	0.0106532886861418	0.425476507007645	155	12.0451205928237	5.51321148265855	43.6700800403255	4.31711092832003	2.23470729485323	2.32021409426696	4.45100641041494	4.35211347359363	32	9	82	8	3	3	12	6
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-Abhd1	4.58505458443503	0.966932348394413	8.77916365072011	0.00304690868021172	0.0115116649143098	0.827851653717027	13	1.8820500926287	0	3.727933661979	0.539638866040004	0	0	0	0	5	0	7	1	0	0	0	0
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-Snord42b	1.04767977027251	6.25057583145994	8.58324123649257	0.00339270920040068	0.0126920695005153	0.111552873006959	1095	85.4450742053431	140.280603280979	72.9609873844462	105.229578877801	52.1431702132421	51.0447100738731	32.6407136763762	60.2042363847118	227	229	137	195	70	66	88	83
-Mir331	-1.17409481969869	4.69005885814387	8.49349652126477	0.00356418179440943	0.0132899719850365	0.128795803186841	359	18.4440909077613	21.4402668770055	9.05355317909186	12.4116939189201	40.2247313073582	30.9361879235595	36.3498856850553	31.1901465607543	49	35	17	23	54	40	98	43
-Gm14207	-3.00324823073087	0.871092925706287	8.44703117613916	0.00365639833384185	0.0135894153059073	0.159852546031247	11	0.376410018525741	0	0	0	1.48980486323549	1.54680939617797	1.85458600433956	0.725352245598938	1	0	0	0	2	2	5	1
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-Mir542	-1.2202802541791	4.03013743187161	8.31210440681503	0.0039381736087853	0.0144021028449488	0.130055566109976	225	7.52820037051481	9.18868580443092	12.2489248893596	8.09458299060006	17.1327559272081	24.7489503388476	27.8187900650933	15.9577494031766	20	15	23	15	23	32	75	22
-Wac	3.38322498366109	1.79633313978989	8.30390614338839	0.00395599015505876	0.0144210375940001	0.721861346918632	36	5.64615027788611	1.22515810725746	7.98842927566929	1.07927773208001	0	0	0	1.45070449119788	15	2	15	2	0	0	0	2
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-Mir743b	3.78685035606387	0.637395804592281	8.19262127631599	0.00420611065033774	0.0151872539621372	0.0566083797972507	7	1.12923005557722	0.612579053628728	0.532561951711286	1.07927773208001	0	0	0	0	3	1	1	2	0	0	0	0
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-4933405D12Rik	3.80231024017593	1.50330582921603	8.10201405332819	0.00442160998977043	0.0158649591142392	0.860774753937791	26	3.01128014820593	2.45031621451491	6.92330537224672	0	0	0	0	0.725352245598938	8	4	13	0	0	0	0	1
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-B430010I23Rik	3.55351468097294	0.571660508506008	6.35449206344282	0.011708445969525	0.0351404017542308	0.247102671096052	6	1.12923005557722	0	1.06512390342257	0.539638866040004	0	0	0	0	3	0	2	1	0	0	0	0
-A330009N23Rik	-3.68670927513927	0.572526588246057	6.34887545534638	0.0117455729259622	0.0351404017542308	0.748592452363965	6	0	0	0	0	1.48980486323549	0	1.48366880347165	0	0	0	0	0	2	0	4	0
-Mir1191	2.78203182954521	1.72966994996824	6.34597436170453	0.0117647970816093	0.0351404017542308	0.684946592724875	34	6.39897031493759	0	5.32561951711286	2.15855546416002	0	0.773404698088987	0.370917200867911	0.725352245598938	17	0	10	4	0	1	1	1
-Gm10791	4.21529090100729	0.811121956733704	6.3147673466929	0.0119736455436053	0.0356708343740304	1.27425151461522	10	1.50564007410296	0	3.19537171026772	0	0	0	0	0	4	0	6	0	0	0	0	0
-Mir19a	1.07983908902366	3.97679686744879	6.30696566028169	0.0120264495886749	0.0357348410955157	0.131590049176006	223	24.8430612226989	14.7018972870895	18.639668309895	19.4269991774402	11.1735364742662	10.8276657732458	9.64384722256569	5.07746571919256	66	24	35	36	15	14	26	7
-2900009J06Rik	4.10958364300505	0.80184621361431	6.21618597638906	0.0126587522914407	0.0375159386091787	1.18480450374023	10	2.63487012968019	0	1.59768585513386	0	0	0	0	0	7	0	3	0	0	0	0	0
-Rnu11	1.78240522507158	4.31503812639707	6.21011957428464	0.0127022049640469	0.0375471913574548	0.428450018330416	297	39.146641926677	6.73836958991601	61.7771863985092	4.31711092832003	5.95921945294196	9.28085637706784	11.1275160260373	5.8028179647915	104	11	116	8	8	12	30	8
-Mir29a	-0.775009906550968	11.1527649022789	6.20113246301565	0.0127668601444967	0.037640794379511	0.091723717434537	33081	1846.29114086876	2137.90089716426	774.877639739921	1956.73052826106	2932.68087327906	2669.79301780318	3109.76981207657	2779.54980513513	4905	3490	1455	3626	3937	3452	8384	3832
-4931440P22Rik	2.69422554144544	0.922636384440636	6.17634518384168	0.0129469437517748	0.0380549211837155	0.214786057182669	12	1.50564007410296	0.612579053628728	1.06512390342257	2.15855546416002	0.744902431617745	0	0	0	4	1	2	4	1	0	0	0
-4930405P13Rik	4.2497789323049	0.814222276413148	6.17264809633618	0.0129740265911177	0.0380549211837155	1.39750324685185	10	1.12923005557722	0	3.727933661979	0	0	0	0	0	3	0	7	0	0	0	0	0
-Mir666	-2.80615346530042	0.780065908832119	6.10876775946343	0.0134512878698625	0.0393536396397771	0.307684101038562	9	0	0	0	0.539638866040004	1.48980486323549	1.54680939617797	0.370917200867911	2.17605673679681	0	0	0	1	2	2	1	3
-Gm12718	3.58286595198089	0.575822842137927	6.08629772931823	0.0136234261485357	0.0397553177377985	0.387364388500097	6	0.752820037051481	0.612579053628728	1.59768585513386	0	0	0	0	0	2	1	3	0	0	0	0	0
-2700089E24Rik	3.04034763305122	2.50936021306659	6.06545978102017	0.0137850862827778	0.0401244475730853	1.06340957990033	70	10.163070500195	0	19.1722302616063	1.07927773208001	0.744902431617745	0.773404698088987	0	2.17605673679681	27	0	36	2	1	1	0	3
-Hdac10	5.17854776665305	1.21291547686687	5.98797443232457	0.0144037329545824	0.041818471504271	2.86212993058383	18	0	0	9.58611513080315	0	0	0	0	0	0	0	18	0	0	0	0	0
-A730056A06Rik	-3.88862786022957	0.58495654109149	5.9669442918666	0.0145765187166657	0.0421493177137813	1.24086248700792	6	0	0	0	0	0	3.09361879235595	0.741834401735823	0	0	0	0	0	0	4	2	0
-E230016K23Rik	-4.09400759049538	0.596535776513554	5.96512462383297	0.0145915692348323	0.0421493177137813	1.56212556694857	6	0	0	0	0	1.48980486323549	3.09361879235595	0	0	0	0	0	0	2	4	0	0
-4930545L23Rik	3.63102556342012	0.580100294207571	5.9593789278012	0.0146391971142683	0.0421801108772226	0.505866402722076	6	0.376410018525741	1.22515810725746	1.59768585513386	0	0	0	0	0	1	2	3	0	0	0	0	0
-C730036E19Rik	3.98433831465098	0.747886821890444	5.94057307659931	0.0147962069484563	0.0425251187108025	1.13720095722367	9	2.25846011115444	0	1.59768585513386	0	0	0	0	0	6	0	3	0	0	0	0	0
-Mir1198	2.02251815934069	1.98527793197627	5.93243014248034	0.0148647291287332	0.0426147134067451	0.384617578192951	43	4.51692022230889	1.22515810725746	8.52099122738058	3.23783319624003	0.744902431617745	0.773404698088987	1.11275160260373	1.45070449119788	12	2	16	6	1	1	3	2
-A630066F11Rik	3.35419612704175	0.511731227834426	5.90780023314489	0.0150739837354798	0.0431063043663719	0.0712524213497889	5	0.376410018525741	0.612579053628728	0.532561951711286	1.07927773208001	0	0	0	0	1	1	1	2	0	0	0	0
-9130019P16Rik	2.58125904051328	1.28957758188625	5.88489474717361	0.0152713104274963	0.0435614129944333	0.461891716738449	20	1.8820500926287	1.83773716088618	4.26049561369029	1.07927773208001	0	1.54680939617797	0	0	5	3	8	2	0	2	0	0
-Gm3230	3.820043754688	0.642568227191324	5.85518944635986	0.0155311861241487	0.0441922278495105	0.881550821291204	7	0.376410018525741	0	2.66280975855643	0.539638866040004	0	0	0	0	1	0	5	1	0	0	0	0
-AW011738	3.69345791739811	0.584493580316665	5.83435047741961	0.0157162108288891	0.044607454118812	0.652463491341992	6	0	1.83773716088618	1.06512390342257	0.539638866040004	0	0	0	0	0	3	2	1	0	0	0	0
-D330050I16Rik	2.63984206412724	0.86834372342209	5.78744553150091	0.0161410268360658	0.0456995325557098	0.215318076592647	11	1.12923005557722	1.22515810725746	0.532561951711286	2.15855546416002	0	0	0.370917200867911	0	3	2	1	4	0	0	1	0
-Gm16119	2.68544441180761	1.44185052363263	5.77488176874989	0.0162568137423185	0.0459134269306568	0.687146649591784	24	0.376410018525741	2.45031621451491	4.26049561369029	4.31711092832003	0	0	1.11275160260373	0	1	4	8	8	0	0	3	0
-F730035M05Rik	2.49547258142172	1.1680071013812	5.76885606090423	0.0163126497982226	0.0459573664685728	0.387824572820115	17	0.752820037051481	3.06289526814364	3.19537171026772	1.07927773208001	0	0	0.370917200867911	0.725352245598938	2	5	6	2	0	0	1	1
-5730522E02Rik	2.76245017880368	0.916793078443015	5.75511512145183	0.0164407183097961	0.0462040876637373	0.353082848916159	12	1.50564007410296	0.612579053628728	2.66280975855643	0.539638866040004	0	0	0.370917200867911	0	4	1	5	1	0	0	1	0
-Cox18	3.51416475693794	0.56943721310897	5.68624937371652	0.0170983609672658	0.0479342257092146	0.476558529526638	6	1.50564007410296	0.612579053628728	0.532561951711286	0	0	0	0	0	4	1	1	0	0	0	0	0
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-Gm13944	3.33654009544205	0.507320755410798	5.66676109801095	0.0172893498080461	0.0482326360170675	0.149058956909043	5	0.752820037051481	0	1.06512390342257	0.539638866040004	0	0	0	0	2	0	2	1	0	0	0	0
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-Gm10658	3.34091350906989	0.50829381891728	5.62818209841236	0.0176739512203612	0.0489465493748354	0.171540653150806	5	0.752820037051481	0.612579053628728	1.06512390342257	0	0	0	0	0	2	1	2	0	0	0	0	0
-9530080O11Rik	3.34072482895183	0.50829381891728	5.61473474367925	0.0178100767010679	0.049204110208035	0.177314392730007	5	0.752820037051481	0.612579053628728	1.06512390342257	0	0	0	0	0	2	1	2	0	0	0	0	0
-Raver1-fdx1l	2.8728632178986	1.67787191601149	5.60461635526638	0.0179132168240584	0.0493695178653396	0.905203978574987	32	4.51692022230889	0	7.98842927566929	1.07927773208001	0	0	0.741834401735823	0.725352245598938	12	0	15	2	0	0	2	1
-Gm12992	3.04875774551117	1.01806369607825	5.59241069956709	0.01803845421986	0.0495896018271841	0.699288529447959	14	1.50564007410296	1.22515810725746	3.727933661979	0	0	0	0.370917200867911	0	4	2	7	0	0	0	1	0
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-1300002E11Rik	3.81596413981077	1.56184945519892	5.56469634831358	0.0183261884495175	0.0501443189949627	1.57902727583979	28	3.76410018525741	0	9.05355317909186	0	0.744902431617745	0	0	0	10	0	17	0	1	0	0	0
-3110039I08Rik	-3.80472087952198	0.525704134119528	5.55032129496486	0.018477294935278	0.0504368266056272	1.31921588338298	5	0	0	0	0	0	1.54680939617797	0	2.17605673679681	0	0	0	0	0	2	0	3
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-1810014B01Rik	3.62213282254788	0.579022863117308	5.49945295161865	0.0190224236430342	0.0515548346239954	0.729733922836589	6	0.376410018525741	0.612579053628728	2.13024780684514	0	0	0	0	0	1	1	4	0	0	0	0	0
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-Gm19705	-3.45826853931583	0.453052534566718	5.47006614470426	0.0193448933972833	0.0519353490971771	0.662830924165095	4	0	0	0	0	1.48980486323549	1.54680939617797	0	0	0	0	0	0	2	2	0	0
-Mir701	-3.45826853931583	0.453052534566718	5.47006614470426	0.0193448933972833	0.0519353490971771	0.662830924165095	4	0	0	0	0	1.48980486323549	1.54680939617797	0	0	0	0	0	0	2	2	0	0
-Mir5121	1.94742522131415	1.83785122259044	5.46371186689073	0.0194153605201342	0.0520021745386694	0.376976641878831	37	3.01128014820593	6.73836958991601	3.727933661979	2.15855546416002	0.744902431617745	0.773404698088987	1.85458600433956	0	8	11	7	4	1	1	5	0
-B330016D10Rik	-2.50767371876481	0.698215002345895	5.44772199489155	0.0195938600007735	0.0522167328421979	0.124397604157986	8	0.376410018525741	0	0	0	0.744902431617745	0.773404698088987	1.48366880347165	0.725352245598938	1	0	0	0	1	1	4	1
-Slc25a43	-3.44800799301466	0.452586463588376	5.44551853309808	0.0196185905357389	0.0522167328421979	0.662845665062969	4	0	0	0	0	2.97960972647098	0	0	0	0	0	0	0	4	0	0	0
-4930412C18Rik	-3.44582480224269	0.452480479327341	5.4401904806621	0.0196785233322919	0.0522167328421979	0.662849017103242	4	0	0	0	0	0.744902431617745	0.773404698088987	0	1.45070449119788	0	0	0	0	1	1	0	2
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-Gm1976	3.96505343635482	0.701085179925696	5.40774000758032	0.0200476543735815	0.0529499389820753	1.41043256341209	8	0.752820037051481	0	3.19537171026772	0	0	0	0	0	2	0	6	0	0	0	0	0
-4930546K05Rik	3.37806997507641	0.512599363933001	5.3736330786425	0.020443354046408	0.0538703625102807	0.345436453514549	5	0.376410018525741	1.22515810725746	1.06512390342257	0	0	0	0	0	1	2	2	0	0	0	0	0
-Mir708	-2.70135803241191	1.11706885896262	5.34908005477761	0.020733207145999	0.0545082703999651	0.690665480064639	16	0.376410018525741	0.612579053628728	0	0	4.46941458970647	0	2.59642040607538	0.725352245598938	1	1	0	0	6	0	7	1
-4930564K09Rik	3.6917066956466	0.632949774856083	5.32081113352027	0.0210722003093536	0.0552721392022356	1.00252818613139	7	1.50564007410296	0	1.59768585513386	0	0	0	0	0	4	0	3	0	0	0	0	0
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-4930549G23Rik	3.7329128881827	0.636115959509291	5.28578795016413	0.021500161093926	0.0560967768632042	1.08974823744359	7	1.12923005557722	0	2.13024780684514	0	0	0	0	0	3	0	4	0	0	0	0	0
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-4930556M19Rik	2.66295078370768	1.38828943045466	4.44749356732844	0.0349525109569089	0.0791286011941132	0.905594146357706	23	4.14051020378315	1.83773716088618	3.727933661979	0	1.48980486323549	0	0	0	11	3	7	0	2	0	0	0
-Slc35c2	3.30865205063321	0.507213862469314	4.43963505660486	0.0351137526308395	0.079336221290669	0.861401174147407	5	0.752820037051481	0	1.59768585513386	0	0	0	0	0	2	0	3	0	0	0	0	0
-Gm19710	3.30854550904046	0.507213862469314	4.43368996784918	0.0352362513208005	0.0794233311815247	0.866290226188262	5	0.752820037051481	0	1.59768585513386	0	0	0	0	0	2	0	3	0	0	0	0	0
-2310044G17Rik	2.05621267074145	1.32862828701613	4.43101455523806	0.0352915240219396	0.0794233311815247	0.404465860932417	21	2.25846011115444	1.83773716088618	3.727933661979	1.07927773208001	0	1.54680939617797	0	0.725352245598938	6	3	7	2	0	2	0	1
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-Gm3219	3.60737493223815	0.577946067919671	4.421480162378	0.0354892385246105	0.0795040126826207	1.52667717941297	6	0.376410018525741	0	2.66280975855643	0	0	0	0	0	1	0	5	0	0	0	0	0
-D030028A08Rik	3.60738151612533	0.577946067919671	4.41596802198372	0.0356040719631981	0.0795040126826207	1.5322501246408	6	0.376410018525741	0	2.66280975855643	0	0	0	0	0	1	0	5	0	0	0	0	0
-Gm14403	3.60738163135027	0.577946067919671	4.41587124501828	0.0356060915695173	0.0795040126826207	1.53234812927796	6	0.376410018525741	0	2.66280975855643	0	0	0	0	0	1	0	5	0	0	0	0	0
-Dennd2d	3.6073885554531	0.577946067919671	4.4100360359284	0.0357280863769671	0.0796205987424209	1.53826760209751	6	0.376410018525741	0	2.66280975855643	0	0	0	0	0	1	0	5	0	0	0	0	0
-4933404O12Rik	3.41483468754033	0.513876008043094	4.39861857465169	0.0359680543296903	0.0798701167406372	1.0997339968646	5	0	0	1.59768585513386	1.07927773208001	0	0	0	0	0	0	3	2	0	0	0	0
-9330133O14Rik	3.40993147550957	0.565287625816317	4.39628480824711	0.0360173118390578	0.0798701167406372	1.17471994051209	6	1.8820500926287	0	0	0.539638866040004	0	0	0	0	5	0	0	1	0	0	0	0
-A230108P19Rik	2.12431096016161	1.3211306056393	4.39473543004656	0.036050052691874	0.0798701167406372	0.471731682383365	21	2.25846011115444	0.612579053628728	4.79305756540158	1.07927773208001	0	0.773404698088987	0.370917200867911	0.725352245598938	6	1	9	2	0	1	1	1
-Mea1	1.99180528749444	0.974328529879471	4.39009023045989	0.0361484000501709	0.0799327993357462	0.160665324005786	13	1.12923005557722	1.22515810725746	2.13024780684514	1.07927773208001	0.744902431617745	0	0.370917200867911	0	3	2	4	2	1	0	1	0
-Stap2	1.96836921762169	1.45702826044348	4.24688515308066	0.039322390488878	0.0865226045963398	0.447793446770451	25	3.76410018525741	0.612579053628728	3.19537171026772	2.15855546416002	0	1.54680939617797	0.741834401735823	0	10	1	6	4	0	2	2	0
-Runx2	-1.8781114233878	1.22110313001472	4.24585644988074	0.0393462193619179	0.0865226045963398	0.341255251954501	18	0	1.83773716088618	0	0.539638866040004	2.23470729485323	1.54680939617797	1.85458600433956	2.90140898239575	0	3	0	1	3	2	5	4
-Mir188	-1.59922257277923	1.98299554425222	4.24251863370676	0.0394236408768871	0.0865226045963398	0.371629476825589	41	1.8820500926287	3.06289526814364	0	1.07927773208001	4.46941458970647	6.18723758471189	2.59642040607538	5.8028179647915	5	5	0	2	6	8	7	8
-Snora61	0.969697226409014	3.56540254224777	4.24140176384522	0.0394495826345953	0.0865226045963398	0.154073484156933	164	18.820500926287	12.8641601262033	12.7814868410709	10.7927773208001	7.44902431617745	3.86702349044493	8.53109561996196	7.97887470158832	50	21	24	20	10	5	23	11
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-Ano4	-2.44102696576186	0.940052484741396	4.21583782979193	0.0400482838831723	0.0875384902503823	0.691619616682358	12	0.376410018525741	0	0.532561951711286	0	4.46941458970647	0	0.741834401735823	1.45070449119788	1	0	1	0	6	0	2	2
-Snord37	0.76109809770672	5.37374937014978	4.21175926855503	0.0401446813370918	0.0875814176015712	0.115843179216952	605	50.4389424824493	69.2214330600463	39.4095844266352	43.1711092832003	29.051194833092	24.7489503388476	33.0116308772441	31.9154988063533	134	113	74	80	39	32	89	44
-5430405H02Rik	2.58778573027926	1.29115223901932	4.19097143820825	0.0406398034555258	0.0884923964556392	0.897399733720162	20	2.25846011115444	4.90063242902982	0	2.15855546416002	0	0	0	1.45070449119788	6	8	0	4	0	0	0	2
-Polg2	3.14592319385234	0.445065788409347	4.15497735392699	0.0415123289754101	0.0900486071500817	0.663083507648294	4	0	1.22515810725746	1.06512390342257	0	0	0	0	0	0	2	2	0	0	0	0	0
-Sgsm1	3.14592319385234	0.445065788409347	4.15497735392699	0.0415123289754101	0.0900486071500817	0.663083507648294	4	0	1.22515810725746	1.06512390342257	0	0	0	0	0	0	2	2	0	0	0	0	0
-4930502E09Rik	3.13239036710484	0.444300161259273	4.12791304512818	0.0421813397945613	0.0913262024774088	0.663107719639279	4	0	0.612579053628728	0	1.61891659812001	0	0	0	0	0	1	0	3	0	0	0	0
-Zfp414	3.35771159436211	0.510435203260448	4.12231625073913	0.0423210970545786	0.0913615869544494	1.26204273440692	5	0.376410018525741	0	2.13024780684514	0	0	0	0	0	1	0	4	0	0	0	0	0
-9230116N13Rik	3.12878070152109	0.444084400555259	4.1205980357213	0.0423641000944858	0.0913615869544494	0.663114542800252	4	0	0.612579053628728	1.06512390342257	0.539638866040004	0	0	0	0	0	1	2	1	0	0	0	0
-4833417C18Rik	2.35006375536511	0.758568204956497	4.1176536784517	0.0424378975336181	0.0913615869544494	0.280418772687045	9	0.752820037051481	0.612579053628728	0.532561951711286	2.15855546416002	0	0	0.370917200867911	0	2	1	1	4	0	0	1	0
-Gm9999	3.42407156884898	0.514090525834695	4.10976571594314	0.0426362683774495	0.091615785722542	1.4146566386221	5	0	0	0.532561951711286	2.15855546416002	0	0	0	0	0	0	1	4	0	0	0	0
-Mir191	-0.724056835181618	13.66326713492	4.10168206666822	0.0428405722202924	0.0917658811710067	0.121505260548983	174890	8555.79972109009	11471.1553582516	6973.89875765929	12136.4780972397	19775.6697545879	18076.0146037358	8338.58959271151	18458.7639460018	22730	18726	13095	22490	26548	23372	22481	25448
-Snord88c	0.772003291829085	4.59608010488051	4.09905735285379	0.0429071296584209	0.0917658811710067	0.111637028219497	341	27.8543413709048	25.1157411987778	34.6165268612336	28.0612210340802	21.6021705169146	17.0149033579577	12.9821020303769	16.6831016487756	74	41	65	52	29	22	35	23
-Scarna13	2.37818263258555	0.813784050534841	4.09747145562217	0.0429473974980873	0.0917658811710067	0.357362691777861	10	1.50564007410296	0	1.06512390342257	1.61891659812001	0.744902431617745	0	0	0	4	0	2	3	1	0	0	0
-Mir880	3.11348514900424	0.443211372076721	4.09021063177916	0.0431322667093954	0.0918100626826892	0.663142151271534	4	0	0	0.532561951711286	1.61891659812001	0	0	0	0	0	0	1	3	0	0	0	0
-9330151L19Rik	3.10988480327256	0.442995740899849	4.08298153447082	0.0433171597691468	0.0918100626826892	0.663148970364241	4	0	0	1.59768585513386	0.539638866040004	0	0	0	0	0	0	3	1	0	0	0	0
-Lins	3.10988480327256	0.442995740899849	4.08298153447082	0.0433171597691468	0.0918100626826892	0.663148970364241	4	0	0	1.59768585513386	0.539638866040004	0	0	0	0	0	0	3	1	0	0	0	0
-1190002F15Rik	3.10808521622628	0.442887934784986	4.07937303310311	0.0434097633799645	0.0918100626826892	0.66315237961443	4	0	0	2.13024780684514	0	0	0	0	0	0	0	4	0	0	0	0	0
-Tsix	3.10808521622628	0.442887934784986	4.07937303310311	0.0434097633799645	0.0918100626826892	0.66315237961443	4	0	0	2.13024780684514	0	0	0	0	0	0	0	4	0	0	0	0	0
-Snhg10	2.37733284339773	0.813784050534841	4.07777411895888	0.0434508622687574	0.0918100626826892	0.363056531257373	10	1.50564007410296	0	1.06512390342257	1.61891659812001	0.744902431617745	0	0	0	4	0	2	3	1	0	0	0
-Mir19b-2	1.95460141884699	0.967543811537092	4.0667510821653	0.0437353173957902	0.0922402904779974	0.194698364222462	13	1.8820500926287	0.612579053628728	1.06512390342257	1.61891659812001	0	0.773404698088987	0.370917200867911	0	5	1	2	3	0	1	1	0
-4930451G09Rik	3.09940234131105	0.441920006309181	4.05747128275669	0.0439763063671282	0.0924069347419766	0.663182989421909	4	0.376410018525741	1.22515810725746	0	0.539638866040004	0	0	0	0	1	2	0	1	0	0	0	0
-4933421O10Rik	3.09940234131105	0.441920006309181	4.05747128275669	0.0439763063671282	0.0924069347419766	0.663182989421909	4	0.376410018525741	1.22515810725746	0	0.539638866040004	0	0	0	0	1	2	0	1	0	0	0	0
-Icmt	3.04158259524875	1.01298978592042	4.05190611070757	0.0441214993656041	0.0925416006914601	1.3064646184198	14	1.8820500926287	0	4.26049561369029	0	0	0	0.370917200867911	0	5	0	8	0	0	0	1	0
-BC029722	3.19971849117159	0.500786066092323	4.04549638503555	0.0442893518018265	0.0927232117539156	1.02178221965242	5	1.50564007410296	0	0.532561951711286	0	0	0	0	0	4	0	1	0	0	0	0	0
-D630024D03Rik	4.06980775023739	0.7082404013179	4.03667263086978	0.0445215210737058	0.0928700527056171	3.02593312554975	8	0	0	0	4.31711092832003	0	0	0	0	0	0	0	8	0	0	0	0
-0610040F04Rik	2.01747357743795	0.977371680002771	4.03475819286646	0.0445720624037807	0.0928700527056171	0.261267095275759	13	0.752820037051481	1.22515810725746	2.66280975855643	1.07927773208001	0.744902431617745	0	0.370917200867911	0	2	2	5	2	1	0	1	0
-Xist	4.05419301544092	0.707395597770275	4.03069585480579	0.044679508661419	0.0928700527056171	3.00206353206744	8	0	0	4.26049561369029	0	0	0	0	0	0	0	8	0	0	0	0	0
-2810013P06Rik	2.06704188927481	0.967762003264331	4.03048606761081	0.0446850647987588	0.0928700527056171	0.309415407092658	13	1.50564007410296	2.45031621451491	1.06512390342257	0.539638866040004	0	0	0.741834401735823	0	4	4	2	1	0	0	2	0
-Gm10516	3.08232207628801	0.440940231955907	4.02405179815349	0.0448558278758383	0.0929488328525612	0.663213974165843	4	0.376410018525741	0.612579053628728	0	1.07927773208001	0	0	0	0	1	1	0	2	0	0	0	0
-Snord100	0.6356135257733	6.08912492232221	4.02292153108604	0.0448858956194226	0.0929488328525612	0.0868973478133586	1006	74.1527736495709	82.0855931862496	85.2099122738058	83.6440242362007	51.3982677816244	43.3106630929833	59.3467521388658	54.4014184199203	197	134	160	155	69	56	160	75
-Mir129-1	3.07872386541409	0.440724844947958	4.01695170825076	0.0450450593212474	0.0931094432709118	0.663220785698829	4	0.376410018525741	0.612579053628728	1.06512390342257	0	0	0	0	0	1	1	2	0	0	0	0	0
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-2010003O02Rik	1.98348239078607	1.15376056733645	3.98420909577422	0.0459286580442539	0.09362004436208	0.398601895191898	17	1.12923005557722	1.22515810725746	3.19537171026772	1.61891659812001	0	0	1.11275160260373	0	3	2	6	3	0	0	3	0
-1700034P13Rik	3.06170605431538	0.439745726709386	3.98398193097084	0.0459348517545847	0.09362004436208	0.663251750248398	4	0.376410018525741	0	1.06512390342257	0.539638866040004	0	0	0	0	1	0	2	1	0	0	0	0
-Fam120aos	3.06170605431538	0.439745726709386	3.98398193097084	0.0459348517545847	0.09362004436208	0.663251750248398	4	0.376410018525741	0	1.06512390342257	0.539638866040004	0	0	0	0	1	0	2	1	0	0	0	0
-A730020M07Rik	3.05991259759458	0.439638100977263	3.98047653835878	0.0460305389019517	0.09362004436208	0.663255153938683	4	0.376410018525741	0	1.59768585513386	0	0	0	0	0	1	0	3	0	0	0	0	0
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-Mir3470a	1.65480723982971	2.35026931926344	3.95738885462005	0.0466660378918481	0.0947436819120973	0.475481922307067	61	8.65743042609204	1.22515810725746	10.1186770825144	2.69819433020002	3.72451215808872	0.773404698088987	1.85458600433956	0.725352245598938	23	2	19	5	5	1	5	1
-Mir669e	2.01012761708276	0.976761579480109	3.9359265892494	0.0472650955169254	0.0957894742181384	0.278646788851285	13	0.752820037051481	0.612579053628728	2.13024780684514	2.15855546416002	0	0.773404698088987	0.370917200867911	0	2	1	4	4	0	1	1	0
-5730408K05Rik	3.03229149852466	0.437585811559422	3.92303893681648	0.0476287099607381	0.0963552447964578	0.663320059640923	4	0.752820037051481	0.612579053628728	0	0.539638866040004	0	0	0	0	2	1	0	1	0	0	0	0
-Alg11	2.54969310547171	0.91195896231435	3.91254406008439	0.0479269939780155	0.096787079874187	0.684211815681331	12	2.63487012968019	0	1.59768585513386	0.539638866040004	0	0	0	0.725352245598938	7	0	3	1	0	0	0	1
-BC031361	3.01357415843156	0.436500850557613	3.88761656046388	0.0486434097482271	0.0977411274915922	0.663354373887436	4	0.752820037051481	0	0.532561951711286	0.539638866040004	0	0	0	0	2	0	1	1	0	0	0	0
-1500015L24Rik	3.01178876963551	0.436393404882941	3.88421409864557	0.0487420697131604	0.0977411274915922	0.6633577721384	4	0.752820037051481	0	1.06512390342257	0	0	0	0	0	2	0	2	0	0	0	0	0
-Cyp2d37-ps	3.01178876963551	0.436393404882941	3.88421409864557	0.0487420697131604	0.0977411274915922	0.6633577721384	4	0.752820037051481	0	1.06512390342257	0	0	0	0	0	2	0	2	0	0	0	0	0
-Trpt1	3.01178876963551	0.436393404882941	3.88421409864557	0.0487420697131604	0.0977411274915922	0.6633577721384	4	0.752820037051481	0	1.06512390342257	0	0	0	0	0	2	0	2	0	0	0	0	0
-Mir297-2	1.54223903109753	2.09323284778587	3.86609308727373	0.0492710892079631	0.0986286189233086	0.370747786569295	47	4.14051020378315	3.67547432177237	8.52099122738058	2.15855546416002	3.72451215808872	0.773404698088987	1.11275160260373	0.725352245598938	11	6	16	4	5	1	3	1
-Snhg3	1.23410251082105	2.87013370923104	3.85237575582464	0.0496755798471384	0.0992641621814097	0.27141878267607	94	10.5394805187207	3.67547432177237	9.58611513080315	9.71349958872008	6.7041218845597	1.54680939617797	3.70917200867911	2.17605673679681	28	6	18	18	9	2	10	3
-Jmjd4	2.15432402469418	1.07901960587324	3.84243990202495	0.0499707537247533	0.0996794230768244	0.489459762198242	15	0.752820037051481	0.612579053628728	3.727933661979	1.61891659812001	0.744902431617745	0.773404698088987	0	0	2	1	7	3	1	1	0	0
-Mir465	2.98235832242491	0.434236891175304	3.82488410595021	0.0504968399885711	0.100553044375148	0.663425979374912	4	1.12923005557722	0.612579053628728	0	0	0	0	0	0	3	1	0	0	0	0	0	0
-D930015M05Rik	2.03574906278116	1.28114908660781	3.79290309757499	0.0514703043912074	0.102276096831736	0.528630729429243	20	2.25846011115444	0.612579053628728	4.79305756540158	0.539638866040004	0	0.773404698088987	0.370917200867911	0.725352245598938	6	1	9	1	0	1	1	1
-4930414N06Rik	2.96376667824733	0.433153838612419	3.79059200287469	0.0515414160194991	0.102276096831736	0.663460235996829	4	1.12923005557722	0	0.532561951711286	0	0	0	0	0	3	0	1	0	0	0	0	0
-Mirlet7f-2	0.586189945014016	9.80950112843876	3.78292421431226	0.0517780966945444	0.102567375570269	0.0860665007021305	14033	912.417884906396	1021.78186145272	1238.20653772874	1129.46414662173	555.697213986838	518.181147719621	1246.28179491618	541.838127462407	2424	1668	2325	2093	746	670	3360	747
-C920021L13Rik	1.76367634720302	1.70225824660072	3.77610679218444	0.0519894948473733	0.102807649256244	0.466172987214309	32	2.25846011115444	0.612579053628728	5.32561951711286	4.85674979436004	0	1.54680939617797	0.741834401735823	1.45070449119788	6	1	10	9	0	2	2	2
-Gm15417	1.67983957433177	1.80164992122508	3.71610400285244	0.053889975808787	0.106305509251098	0.437532321964174	35	1.50564007410296	2.45031621451491	3.727933661979	7.01530525852006	1.48980486323549	2.32021409426696	0.741834401735823	0	4	4	7	13	2	3	2	0
-Mir411	-0.788462638147409	7.18678006636406	3.71265997701833	0.0540012682008808	0.106305509251098	0.155219066494922	2196	193.474749522231	123.740968833003	42.0723941851916	64.2170250587605	152.704998481638	174.789461768111	222.179403319879	182.788765890932	514	202	79	119	205	226	599	252
-Mir33	1.05704167432929	3.14606659666135	3.71152933736439	0.05403785746331	0.106305509251098	0.207018017782448	118	13.5507606669267	9.80126485805965	5.32561951711286	11.8720550528801	2.97960972647098	6.18723758471189	6.30559241475449	3.62676122799469	36	16	10	22	4	8	17	5
-A630072M18Rik	1.68258239677861	1.28602336518056	3.70225213341255	0.0543390761193033	0.106516710338072	0.274903009044658	20	1.8820500926287	3.67547432177237	1.06512390342257	1.61891659812001	0.744902431617745	0	0.741834401735823	0.725352245598938	5	6	2	3	1	0	2	1
-4931403G20Rik	2.91589823742364	0.429919394234628	3.6996072386672	0.054425277937858	0.106516710338072	0.66356254574956	4	1.50564007410296	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0
-Gm4265	2.91589823742364	0.429919394234628	3.6996072386672	0.054425277937858	0.106516710338072	0.66356254574956	4	1.50564007410296	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0
-Osbpl1a	2.47060020253323	2.20538682744921	3.6943573914536	0.0545968093857622	0.106669451214306	1.23682270771556	52	3.38769016673167	0.612579053628728	19.1722302616063	0	0	1.54680939617797	0.370917200867911	2.17605673679681	9	1	36	0	0	2	1	3
-Mir484	-0.632510337899452	7.18286805708198	3.67311765279572	0.0552966595299158	0.107670420438573	0.0998462983245534	2052	107.276855279836	137.217708012835	53.7887571228399	155.415993419521	199.633851673556	173.242652371933	156.527058766259	174.084538943745	285	224	101	288	268	224	422	240
-Mir3086	-1.59145486347128	1.07120757999586	3.67308125064464	0.0552978671139384	0.107670420438573	0.155363987763911	15	0.752820037051481	0	0.532561951711286	0.539638866040004	1.48980486323549	1.54680939617797	1.85458600433956	1.45070449119788	2	0	1	1	2	2	5	2
-Zfp410	2.40050624799358	1.27209510414984	3.66070723723352	0.0557099804920593	0.108288054074003	0.882974817159586	20	3.76410018525741	0	3.19537171026772	1.07927773208001	0	1.54680939617797	0	0	10	0	6	2	0	2	0	0
-Snord71	0.812834481625127	4.57472241895141	3.65038540252946	0.0560562385400896	0.108775796214698	0.145077150673947	341	35.0061317228939	31.2415317350651	13.8466107444934	34.5368874265603	14.8980486323549	15.4680939617797	14.0948536329806	21.0352151223692	93	51	26	64	20	20	38	29
-Snhg4	-1.23773296257912	2.10618734501935	3.63164359745908	0.0566908046099028	0.10982038719847	0.232173331853086	47	2.63487012968019	1.83773716088618	1.59768585513386	1.61891659812001	2.97960972647098	4.64042818853392	4.08008920954702	7.25352245598938	7	3	3	3	4	6	11	10
-Mir5117	-0.918628727646437	3.53006698882797	3.61010752096311	0.0574294065617766	0.111062632011842	0.168816098394452	153	8.2810204075663	9.80126485805965	7.45586732395801	3.77747206228003	8.19392674779519	16.2414986598687	14.4657708338485	16.6831016487756	22	16	14	7	11	21	39	23
-A930005H10Rik	2.2883427208969	1.01956589864802	3.59998634434969	0.0577800457403129	0.111551661911501	0.713905461258377	14	1.12923005557722	1.83773716088618	0	3.23783319624003	0	0	0.741834401735823	0	3	3	0	6	0	0	2	0
-Mir29b-2	-1.44795171375512	1.94007043007237	3.59625076176146	0.057910035538767	0.111613767820495	0.353810081926908	41	1.8820500926287	0	1.06512390342257	2.69819433020002	2.23470729485323	3.86702349044493	5.56375801301867	4.35211347359363	5	0	2	5	3	5	15	6
-Mir3069	2.21555843820045	0.756685666656939	3.56973154216641	0.0588418232862539	0.113218415463096	0.320229313843863	9	1.50564007410296	0	1.06512390342257	1.07927773208001	0.744902431617745	0	0	0	4	0	2	2	1	0	0	0
-Gm12359	2.16898632974039	1.4882686924078	3.56048945040345	0.0591702894445667	0.113658754640153	0.838744382533676	26	3.38769016673167	0	5.85818146882415	1.07927773208001	0.744902431617745	0	1.11275160260373	0	9	0	11	2	1	0	3	0
-4930512B01Rik	2.311695754453	0.751907182201018	3.55318085590357	0.059431418716913	0.113968485304198	0.447887175675667	9	1.50564007410296	0.612579053628728	1.59768585513386	0	0	0	0.370917200867911	0	4	1	3	0	0	0	1	0
-9330175M20Rik	-1.83913038229394	0.769181445189637	3.53330463673361	0.0601477950442438	0.115148715009198	0.123494361527078	9	0.376410018525741	0	0	0.539638866040004	1.48980486323549	1.54680939617797	0.741834401735823	0.725352245598938	1	0	0	1	2	2	2	1
-Ythdf3	1.5347336844379	2.1872400075988	3.52392375500849	0.0604890850104411	0.115608117247761	0.437839949042422	52	6.02256029641185	1.83773716088618	10.1186770825144	1.61891659812001	2.97960972647098	0.773404698088987	1.48366880347165	1.45070449119788	16	3	19	3	4	1	4	2
-Gm7854	1.81305255344412	1.40076095337658	3.51463719659719	0.0608289735971144	0.11606330915436	0.460693165702023	23	3.01128014820593	4.2880533754011	0.532561951711286	1.61891659812001	0.744902431617745	1.54680939617797	0.370917200867911	0	8	7	1	3	1	2	1	0
-Snord15a	1.26556615125299	2.28978783966135	3.51074804715134	0.0609719200016601	0.116141837599156	0.266691310482511	57	7.52820037051481	6.12579053628728	4.79305756540158	2.15855546416002	2.97960972647098	2.32021409426696	1.85458600433956	1.45070449119788	20	10	9	4	4	3	5	2
-A330023F24Rik	-0.578411379373362	8.69459977720854	3.50754760326356	0.0610898210772275	0.116172476415194	0.0896883068034673	6103	408.781280118954	382.861908517955	135.803297686378	398.793122003563	490.890702436094	450.894938985879	549.699291686245	488.887413533684	1086	625	255	739	659	583	1482	674
-Wdr13	-1.72012346832078	0.929108611192646	3.49925174417671	0.0613965633152006	0.116561528689923	0.196132662445624	12	0.752820037051481	0	0.532561951711286	0	1.48980486323549	0.773404698088987	1.11275160260373	2.17605673679681	2	0	1	0	2	1	3	3
-2010310C07Rik	2.19594941926093	0.754516632646807	3.46141486343187	0.062816542575302	0.119059260927607	0.339304015131098	9	1.8820500926287	0.612579053628728	0.532561951711286	0.539638866040004	0.744902431617745	0	0	0	5	1	1	1	1	0	0	0
-0610009L18Rik	-1.70953095047412	1.30558372556489	3.43327303137063	0.0638953538182766	0.120828775503036	0.427738354254068	21	1.12923005557722	0	0.532561951711286	0.539638866040004	1.48980486323549	1.54680939617797	4.08008920954702	0.725352245598938	3	0	1	1	2	2	11	1
-Mir29c	-0.57990984920207	8.6801221089121	3.43155254206587	0.0639619460156302	0.120828775503036	0.0921705197944408	6038	403.88794987812	382.249329464326	130.477678169265	395.555288807323	487.911092709623	447.027915495434	542.651864869754	483.809947814491	1073	624	245	733	655	578	1463	667
-9530026P05Rik	-1.7605961243528	1.31005038813491	3.41337010539105	0.0646702465899932	0.12196487827964	0.474959668640155	20	0	0.612579053628728	2.13024780684514	0	2.23470729485323	3.09361879235595	1.48366880347165	2.90140898239575	0	1	4	0	3	4	4	4
-Ranbp3	1.94585296479222	1.31602180536296	3.39704288003483	0.0653134106352512	0.122974589991455	0.559883347902171	21	3.76410018525741	1.22515810725746	2.66280975855643	0.539638866040004	0	1.54680939617797	0	0.725352245598938	10	2	5	1	0	2	0	1
-Snora26	1.8683951204978	0.97078207300884	3.34850130949932	0.0672662297995007	0.126442781221137	0.296473421144586	13	2.25846011115444	1.22515810725746	0.532561951711286	1.07927773208001	0	0.773404698088987	0	0.725352245598938	6	2	1	2	0	1	0	1
-Gm5089	2.28406661080162	1.75471782411861	3.34448804982674	0.0674304514099843	0.126543001741434	1.08109229776745	34	3.76410018525741	0	10.1186770825144	0.539638866040004	0.744902431617745	0.773404698088987	0	1.45070449119788	10	0	19	1	1	1	0	2
-9430008C03Rik	1.89674596190827	1.50713232824516	3.33238405268818	0.0679283448542543	0.127268048405097	0.617315662878852	26	3.01128014820593	0.612579053628728	5.32561951711286	1.61891659812001	1.48980486323549	0	0	1.45070449119788	8	1	10	3	2	0	0	2
-Snora28	1.1496742270066	2.54114084010672	3.32568386547373	0.0682056434892409	0.127578097083974	0.243892218993338	71	9.41025046314352	4.2880533754011	6.92330537224672	4.31711092832003	2.23470729485323	3.86702349044493	1.85458600433956	3.62676122799469	25	7	13	8	3	5	5	5
-3110045C21Rik	1.74505885736988	1.30320211999434	3.3208335221709	0.0684071384609135	0.127745572805077	0.429780317265874	21	3.76410018525741	1.83773716088618	1.06512390342257	1.07927773208001	0	0	1.11275160260373	0.725352245598938	10	3	2	2	0	0	3	1
-5730422E09Rik	2.17813007132661	0.69991420705385	3.31793285722386	0.0685279432377808	0.127762064108346	0.327848806595131	8	0.752820037051481	1.22515810725746	1.59768585513386	0	0	0	0.370917200867911	0	2	2	3	0	0	0	1	0
-Snora74a	-1.46979985963907	1.81113794389193	3.30479031516379	0.0690781609579881	0.128577784099616	0.401038547067819	36	2.63487012968019	0	1.06512390342257	1.07927773208001	1.48980486323549	3.86702349044493	4.08008920954702	5.07746571919256	7	0	2	2	2	5	11	7
-Pum2	1.97782252499119	2.18824816941497	3.29348582658385	0.0695552100767045	0.129112655729198	0.877647605651709	52	6.02256029641185	0	13.8466107444934	1.07927773208001	0.744902431617745	3.09361879235595	0.741834401735823	0.725352245598938	16	0	26	2	1	4	2	1
-Mir431	-1.72596294938892	1.80454385303357	3.29262137881222	0.0695918345954921	0.129112655729198	0.631937651435167	36	3.38769016673167	0.612579053628728	0	0	4.46941458970647	3.86702349044493	4.45100641041494	2.17605673679681	9	1	0	0	6	5	12	3
-Mirlet7f-1	-0.618847205867557	5.7539252385751	3.28552344026105	0.0698933385511864	0.129461524816402	0.100580078676248	779	40.2758719822543	47.7811661830408	42.0723941851916	37.2350817567603	72.2555358669212	57.231947658585	76.0380261779218	50.7746571919256	107	78	79	69	97	74	205	70
-BC090627	-1.38077885406157	2.12933903001116	3.26984270915537	0.0705643889819361	0.130492654503062	0.38779446564538	47	2.63487012968019	0.612579053628728	1.06512390342257	3.23783319624003	2.97960972647098	9.28085637706784	2.22550320520747	6.52817021039044	7	1	2	6	4	12	6	9
-Ldha	1.4151914066291	1.99546173390349	3.24154662394186	0.0717928357574312	0.132549555985807	0.366966029357314	43	3.38769016673167	1.22515810725746	6.39074342053543	5.39638866040004	0.744902431617745	3.09361879235595	1.48366880347165	0.725352245598938	9	2	12	10	1	4	4	1
-Snord55	0.573286347215212	7.06540834889277	3.23258793923296	0.0721865298674548	0.133061115636132	0.0928136804736426	2070	173.901428558892	189.286927571277	135.803297686378	134.909716510001	92.3679015206004	78.1138745069877	166.91274039056	86.3169172262736	462	309	255	250	124	101	450	119
-Rmst	-2.27195246552724	0.840227711282082	3.20385958582753	0.0734647080686582	0.135198761139256	0.855385796986541	10	0.376410018525741	0	0.532561951711286	0	0.744902431617745	3.86702349044493	0	1.45070449119788	1	0	1	0	1	5	0	2
-Gm10804	1.94827721953343	0.916648370305759	3.19714363707075	0.0737670015006333	0.135536471356236	0.415145004229017	12	1.12923005557722	0.612579053628728	2.66280975855643	0.539638866040004	0	0	0.741834401735823	0	3	1	5	1	0	0	2	0
-9330162012Rik	2.29431770590354	0.808162714550698	3.19179001668869	0.074008931117939	0.135762363996091	0.637228874009647	10	2.25846011115444	0.612579053628728	1.06512390342257	0	0	0	0	0.725352245598938	6	1	2	0	0	0	0	1
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-Mir1964	-0.921738155510775	3.085190439632	2.85508210154932	0.0910855891795843	0.160383730330101	0.20745603034252	105	3.38769016673167	6.73836958991601	4.26049561369029	7.01530525852006	16.3878534955904	10.0542610751568	7.04742681649032	7.25352245598938	9	11	8	13	22	13	19	10
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-Gm16861	1.57504923281323	1.06927468979849	2.5107217291075	0.113074135454052	0.189453140312883	0.377495401730676	15	1.50564007410296	0.612579053628728	3.19537171026772	0.539638866040004	0.744902431617745	0	0.370917200867911	0.725352245598938	4	1	6	1	1	0	1	1
-0610043K17Rik	1.59193471931028	1.68820971418152	2.46950646868686	0.116074366214038	0.194194797434336	0.6863291991413	32	3.38769016673167	2.45031621451491	6.39074342053543	0	1.48980486323549	0	1.48366880347165	0.725352245598938	9	4	12	0	2	0	4	1
-Mir142	0.477450694101481	10.3704685920882	2.42583672109981	0.119349499937417	0.199381814683153	0.0894373702514067	19662	1637.00717056845	1402.80603280979	808.961604649444	2308.57506891914	1406.3757908943	918.031376631627	954.740875034004	1143.88049130952	4349	2290	1519	4278	1888	1187	2574	1577
-Snora68	0.754003906242898	3.90979075966623	2.41002362725282	0.120560546434849	0.201110502166905	0.184317945134853	213	13.9271706854524	14.0893182334607	20.2373541650289	20.5062769095202	7.44902431617745	3.86702349044493	17.8040256416597	10.1549314383851	37	23	38	38	10	5	48	14
-Snapc4	2.49041637651916	0.760217106525064	2.40088808695279	0.121266389890555	0.201992629000179	1.61489860751689	9	0.376410018525741	0	3.727933661979	0	0	0	0.370917200867911	0	1	0	7	0	0	0	1	0
-Mir376a	-1.79770055073478	0.517679009939258	2.37704742209835	0.123130071001091	0.204797975236509	0.102220591668492	5	0	0	0	0.539638866040004	0.744902431617745	0.773404698088987	0.370917200867911	0.725352245598938	0	0	0	1	1	1	1	1
-Rcbtb2	1.458035208065	1.60444406392876	2.35253785974524	0.125079230443677	0.207737120722323	0.538994375824386	29	3.38769016673167	0.612579053628728	5.32561951711286	1.61891659812001	0	1.54680939617797	0.370917200867911	2.17605673679681	9	1	10	3	0	2	1	3
-Snord90	1.1096339738508	3.57021134154446	2.34966428764857	0.125309990954589	0.207775058281642	0.444973843624013	171	16.1856307966069	8.57610675080219	25.5629736821417	5.39638866040004	1.48980486323549	2.32021409426696	17.0621912399239	3.62676122799469	43	14	48	10	2	3	46	5
-Crebzf	1.48789117978595	2.10034166627966	2.34772153016079	0.125466270951842	0.207775058281642	0.677464272763054	48	7.90461038904056	1.83773716088618	3.19537171026772	4.85674979436004	2.23470729485323	0	0	4.35211347359363	21	3	6	9	3	0	0	6
-Luc7l	1.85138125131535	1.80223159919757	2.31153472918671	0.128417201039621	0.212353661429287	1.10295734287816	36	4.51692022230889	0	8.52099122738058	1.07927773208001	0	3.09361879235595	0.741834401735823	0	12	0	16	2	0	4	2	0
-2410006H16Rik	0.40156959601146	7.61300879663147	2.30675053201469	0.128813086960121	0.212700046630243	0.0639546143922365	2927	246.924972152886	252.995149148665	140.59635525178	244.996045182162	164.623437387522	153.907534919708	178.782090818333	172.633834452547	656	413	264	454	221	199	482	238
-A130049A11Rik	2.22933043064375	0.697786475584508	2.28173485990148	0.130905372741767	0.215842529332884	1.15629017151209	8	0.752820037051481	0	2.66280975855643	0	0	0	0.370917200867911	0	2	0	5	0	0	0	1	0
-Ppp4r1l-ps	2.22958240216918	0.697786475584508	2.27881534539566	0.131152017407923	0.215937159974661	1.16004588257661	8	0.752820037051481	0	2.66280975855643	0	0	0	0.370917200867911	0	2	0	5	0	0	0	1	0
-Gpr137b-ps	-1.96393866359511	0.519974483427559	2.26067676098759	0.132696060422496	0.218164560435257	0.507894910982119	5	0.376410018525741	0	0	0	0.744902431617745	0.773404698088987	0	1.45070449119788	1	0	0	0	1	1	0	2
-Afg3l1	1.48878907990836	1.53298802047544	2.2559764943476	0.133099473557556	0.218512948675067	0.605022791017894	27	2.63487012968019	0	5.32561951711286	2.15855546416002	0	1.54680939617797	1.11275160260373	0.725352245598938	7	0	10	4	0	2	3	1
-1110054M08Rik	1.62607709394344	1.10846820178379	2.2388377970179	0.134582101005543	0.220362784792543	0.586825375590316	16	2.25846011115444	0	3.19537171026772	0.539638866040004	0.744902431617745	0	0.370917200867911	0.725352245598938	6	0	6	1	1	0	1	1
-Gabpb1	1.88779347440519	0.644895200393036	2.23848862109124	0.134612498685716	0.220362784792543	0.466643761334266	7	0.376410018525741	0	1.59768585513386	1.07927773208001	0	0	0	0.725352245598938	1	0	3	2	0	0	0	1
-1810062O18Rik	1.90655347492813	0.646997412885778	2.21392370033664	0.136770417256079	0.223574564597689	0.519639812466009	7	0.376410018525741	1.22515810725746	1.59768585513386	0	0.744902431617745	0	0	0	1	2	3	0	1	0	0	0
-Rnu73b	1.12944302096526	2.00334001585587	2.2098905120464	0.137128403040659	0.223839067052063	0.342987267600863	44	6.77538033346333	1.22515810725746	4.26049561369029	2.69819433020002	1.48980486323549	2.32021409426696	1.11275160260373	2.17605673679681	18	2	8	5	2	3	3	3
-Mir485	-1.61130823956713	0.965437135424848	2.17449466030659	0.140315599881039	0.228479476572061	0.595115069085141	13	0.376410018525741	1.22515810725746	0	0	0.744902431617745	0	2.59642040607538	1.45070449119788	1	2	0	0	1	0	7	2
-Mir299	-0.985886212461873	1.99934376192861	2.1738797045983	0.140371702959697	0.228479476572061	0.247192543395644	43	2.63487012968019	2.45031621451491	1.06512390342257	1.61891659812001	5.21431702132421	3.86702349044493	4.45100641041494	2.17605673679681	7	4	2	3	7	5	12	3
-Zfp326	1.42800580995752	1.2389494049915	2.16911987791298	0.140806800254442	0.228861195285353	0.441869400453635	19	2.63487012968019	0	2.13024780684514	2.15855546416002	1.48980486323549	0.773404698088987	0.370917200867911	0	7	0	4	4	2	1	1	0
-Vaultrc5	-0.439038195253424	7.1589713812088	2.15762491685997	0.141863820034024	0.23025123564555	0.0814636369902265	2022	91.467634501755	97.4000695269678	139.531231348357	154.336715687441	192.184827357378	124.518156392327	142.06128793241	195.845106311713	243	159	262	286	258	161	383	270
-Kat5	1.82250028311508	0.936987866235069	2.13330398894876	0.144129793252217	0.233596724574971	0.804256971018474	12	0.376410018525741	2.45031621451491	2.66280975855643	0	0	0.773404698088987	0	0.725352245598938	1	4	5	0	0	1	0	1
-Mir676	-0.446802845859049	6.53242393015933	2.11909507201475	0.145472474888188	0.235438430989252	0.0840718829945521	1363	70.7650834828393	83.310751293507	76.1563590947139	79.8665521739206	108.755755016191	77.3404698088987	135.01386111592	100.098609892653	188	136	143	148	146	100	364	138
-D130009I18Rik	1.81913658193298	1.05660008926899	2.10048735839325	0.147252196626356	0.237981241289905	0.969130775042647	15	2.25846011115444	3.06289526814364	0.532561951711286	0	0	0	1.11275160260373	0	6	5	1	0	0	0	3	0
-4930471I20Rik	2.00255834299285	0.971879491293969	2.08654611461341	0.148601701031468	0.239822547209201	1.19610967428727	13	1.12923005557722	0	4.26049561369029	0	0	0	0.370917200867911	0.725352245598938	3	0	8	0	0	0	1	1
-Nkx6-2	-1.80867083405477	0.51905820233254	2.07067246210755	0.150155301197704	0.241987568738107	0.37896785158673	5	0	0.612579053628728	0	0	0.744902431617745	1.54680939617797	0.370917200867911	0	0	1	0	0	1	2	1	0
-2810008D09Rik	0.992275793226989	3.31325368484776	2.05928786692252	0.151280855720876	0.243457625356162	0.384912015572866	137	21.831781074493	2.45031621451491	16.5094205030499	3.77747206228003	6.7041218845597	3.09361879235595	4.82192361128285	7.97887470158832	58	4	31	7	9	4	13	11
-Adam17	1.29736312070881	1.35888681515096	2.0539905010054	0.151807840172597	0.243736197402701	0.389874918889432	22	3.01128014820593	0.612579053628728	2.66280975855643	1.61891659812001	0	1.54680939617797	0.370917200867911	1.45070449119788	8	1	5	3	0	2	1	2
-4932702P03Rik	1.88393800726104	1.28729255558247	2.0532548355054	0.151881188740859	0.243736197402701	1.16513146848787	20	2.25846011115444	1.22515810725746	4.79305756540158	0	2.23470729485323	0	0	0	6	2	9	0	3	0	0	0
-Mir26b	0.381406457171371	11.5945020692124	2.04699251410788	0.152507192823816	0.244397060410077	0.0679015278661535	46666	3701.99253220066	4096.3161316153	2404.51721197646	3786.64592300271	2674.94463193932	2413.02265803764	3120.52641090174	2530.0286326491	9835	6687	4515	7017	3591	3120	8413	3488
-Flcn	1.46960323620755	1.35192910254652	2.0423319282605	0.152974978906093	0.244802876482261	0.621458151498979	22	2.63487012968019	1.22515810725746	4.26049561369029	0	0	0.773404698088987	1.11275160260373	0.725352245598938	7	2	8	0	0	1	3	1
-Trpc4	-1.74596825626096	0.71317759283185	2.03938585284699	0.15327151711866	0.244933895003349	0.663998693170537	8	0.376410018525741	0	0	0.539638866040004	2.97960972647098	0.773404698088987	0.370917200867911	0	1	0	0	1	4	1	1	0
-2610203C22Rik	1.96678109286616	1.0656769981979	2.03321410371973	0.153894851376589	0.245586049539424	1.20264987743208	15	2.25846011115444	0	3.727933661979	0	0.744902431617745	0.773404698088987	0	0	6	0	7	0	1	1	0	0
-Mir223	-0.528641311338076	6.02781187693563	2.0162824031966	0.155619736425797	0.247991786678539	0.123647105456996	928	44.7927922045631	63.095642523759	28.2257834406982	75.0098023795606	73.000438298539	84.3011120916996	79.376280985733	67.4577588407012	119	103	53	139	98	109	214	93
-F630028O10Rik	-0.528638261240286	6.02781187693563	2.01389528549142	0.155864679908194	0.24803570401011	0.123802877012886	928	44.7927922045631	63.095642523759	28.2257834406982	75.0098023795606	73.000438298539	84.3011120916996	79.376280985733	67.4577588407012	119	103	53	139	98	109	214	93
-Mir103-2	0.785120714479267	2.71893553817616	2.01013244005869	0.15625167715619	0.248305241831773	0.185923137317638	81	6.39897031493759	6.12579053628728	6.92330537224672	8.09458299060006	5.21431702132421	4.64042818853392	3.3382548078112	2.90140898239575	17	10	13	15	7	6	9	4
-1500011K16Rik	2.11413372855869	0.642522158922088	2.00221094742783	0.15706995194267	0.249258435558535	1.18295805516123	7	0	0	2.13024780684514	1.07927773208001	0	0	0.370917200867911	0	0	0	4	2	0	0	1	0
-Mir5128	1.61359485230054	1.2738493521877	1.99369519162128	0.157955050290775	0.25031487830802	0.813074408757027	20	2.63487012968019	0	4.26049561369029	0.539638866040004	0	0	0.741834401735823	1.45070449119788	7	0	8	1	0	0	2	2
-Mphosph9	1.79236689198877	0.636333411267628	1.98570831058774	0.158790337521116	0.251289563261571	0.508944007080391	7	1.50564007410296	0.612579053628728	0	0.539638866040004	0	0	0	0.725352245598938	4	1	0	1	0	0	0	1
-Asb7	1.94541351595689	0.63279837173148	1.97745174523747	0.159659117691603	0.252036586419259	0.831752840190151	7	1.12923005557722	0	1.59768585513386	0	0	0	0.370917200867911	0	3	0	3	0	0	0	1	0
-4930417O13Rik	1.71715010444166	0.926786533466471	1.97702503038691	0.159704164751204	0.252036586419259	0.743832722550637	12	1.12923005557722	0	2.66280975855643	1.07927773208001	1.48980486323549	0	0	0	3	0	5	2	2	0	0	0
-Gm14872	1.93209082136727	0.649147101892238	1.96034555973411	0.161476366300663	0.254481400482122	0.816935547976381	7	0.376410018525741	2.45031621451491	0.532561951711286	0	0.744902431617745	0	0	0	1	4	1	0	1	0	0	0
-Mir3087	-1.63929497611101	0.718224595257031	1.95615046599623	0.161925618237476	0.254532253128661	0.535428871476747	8	0	1.22515810725746	0	0	2.23470729485323	0.773404698088987	0.370917200867911	0.725352245598938	0	2	0	0	3	1	1	1
-Dlg1	1.1960400330417	2.0318454145106	1.95587851743632	0.161954790334275	0.254532253128661	0.500980604510724	45	4.14051020378315	0.612579053628728	9.58611513080315	1.61891659812001	1.48980486323549	1.54680939617797	2.22550320520747	1.45070449119788	11	1	18	3	2	2	6	2
-Mir154	-1.46711327280047	1.20680226810302	1.95174361269988	0.162399083698592	0.254879442228464	0.655814506748791	18	0.752820037051481	1.83773716088618	0	0	1.48980486323549	0	2.22550320520747	3.62676122799469	2	3	0	0	2	0	6	5
-Mir3060	-1.2113586950711	1.23412841117325	1.93404842060065	0.164316207060804	0.257328754943386	0.342800597958162	19	1.12923005557722	0.612579053628728	0	1.07927773208001	0.744902431617745	0.773404698088987	2.96733760694329	2.17605673679681	3	1	0	2	1	1	8	3
-4930599N23Rik	2.03468754035397	1.36669597820702	1.93318191859516	0.164410747023425	0.257328754943386	1.57432540168325	22	1.8820500926287	0	7.45586732395801	0	0	0.773404698088987	0	1.45070449119788	5	0	14	0	0	1	0	2
-C130046K22Rik	-1.82497997235466	0.998221956150761	1.90372752095523	0.167661683631986	0.262057508252186	1.16292533112971	13	0	1.83773716088618	0	0	3.72451215808872	2.32021409426696	0.741834401735823	0	0	3	0	0	5	3	2	0
-Mir363	1.7925748432871	0.975582225481886	1.88387767295086	0.169894078650183	0.26518350716807	0.983197148538151	13	1.50564007410296	0	0.532561951711286	3.23783319624003	0	1.54680939617797	0	0	4	0	1	6	0	2	0	0
-Mir211	1.80225349548499	0.577821381939333	1.87026630610884	0.171444582259026	0.267238071526706	0.546888078761148	6	0.376410018525741	1.83773716088618	0.532561951711286	0	0	0	0.370917200867911	0	1	3	1	0	0	0	1	0
-Sfpq	1.41155876308153	1.97010238359724	1.86288970907692	0.172291656568755	0.268192060225034	0.790546173016142	42	4.14051020378315	0	10.6512390342257	1.07927773208001	2.23470729485323	2.32021409426696	0.741834401735823	0.725352245598938	11	0	20	2	3	3	2	1
-1700011J10Rik	1.58529666871194	1.38182570088593	1.84526179546736	0.17433548274507	0.271003795384366	0.92161221471478	22	0.376410018525741	5.51321148265855	0	3.77747206228003	1.48980486323549	0	0.741834401735823	0.725352245598938	1	9	0	7	2	0	2	1
-Cep57	2.09388623287428	0.709989751010093	1.83551465851988	0.175477567836488	0.2724080338795	1.41172807747113	8	0	0	2.66280975855643	1.07927773208001	0.744902431617745	0	0	0	0	0	5	2	1	0	0	0
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-Gm5512	1.599390849938	0.914673682188187	1.82135273515121	0.177152341331285	0.274014982163759	0.660513634807064	12	2.63487012968019	0	1.06512390342257	0.539638866040004	0.744902431617745	0.773404698088987	0	0	7	0	2	1	1	1	0	0
-Snord15b	0.634264424966565	3.97493859739106	1.82067308368825	0.17723317864755	0.274014982163759	0.170515835027921	219	23.7138311671217	14.7018972870895	13.3140487927822	18.3477214453601	19.3674632220614	12.3744751694238	8.90201282082987	5.07746571919256	63	24	25	34	26	16	24	7
-9430091E24Rik	1.24004384386007	1.97440463854726	1.81187270965708	0.178283743947416	0.275266240654941	0.592727378101855	42	3.76410018525741	0	8.52099122738058	3.23783319624003	1.48980486323549	1.54680939617797	0.741834401735823	2.90140898239575	10	0	16	6	2	2	2	4
-Gm11974	1.58863800329829	0.875550395278672	1.79977459683717	0.179739747410812	0.277139259183427	0.63054958225523	11	1.12923005557722	0.612579053628728	0	2.69819433020002	0.744902431617745	0.773404698088987	0	0	3	1	0	5	1	1	0	0
-Scarna3a	0.392736064199881	6.8892050947325	1.77303910592734	0.183006412533395	0.281795299191098	0.0783650330620069	1742	102.383525039001	120.065494511231	179.473377726703	131.132244447721	110.245559879426	84.3011120916996	111.275160260373	99.3732576470545	272	196	337	243	148	109	300	137
-Snord58b	-0.959891864807025	4.98142254057752	1.77051212370834	0.183318712396997	0.281895755855759	0.47326365851791	394	17.3148608521841	26.953478359664	15.4442965996273	26.4423044359602	63.3167066875083	62.6457805452079	1.11275160260373	41.3450779991395	46	44	29	49	85	81	3	57
-Gm16845	1.16526727898083	1.63063614891485	1.76110092055298	0.184487255890688	0.283310846529307	0.454435805433799	30	3.01128014820593	1.22515810725746	3.19537171026772	3.23783319624003	0	3.09361879235595	1.48366880347165	0	8	2	6	6	0	4	4	0
-Srsf3	0.982399310397274	2.33968123763579	1.75436232006604	0.185329266491131	0.284221361648361	0.371507892039207	57	2.63487012968019	6.73836958991601	4.79305756540158	7.55494412456006	1.48980486323549	6.18723758471189	0.741834401735823	2.90140898239575	7	11	9	14	2	8	2	4
-Mir1843b	0.389851350484067	6.8875736230068	1.74809462813014	0.186116443545358	0.285045452463427	0.0783163508130989	1740	102.383525039001	120.065494511231	178.408253823281	131.132244447721	110.245559879426	84.3011120916996	111.275160260373	99.3732576470545	272	196	335	243	148	109	300	137
-Brd2	1.00128525429008	2.18480127537997	1.73149429803874	0.188220174345743	0.287880990520767	0.385373147794512	52	5.64615027788611	1.22515810725746	5.32561951711286	5.39638866040004	2.97960972647098	3.09361879235595	2.59642040607538	0	15	2	10	10	4	4	7	0
-4930509E16Rik	1.75126346079812	0.87017516287105	1.72215200430019	0.189416270293976	0.289322576178616	1.0217878616986	11	1.50564007410296	3.06289526814364	0	0	0	0	0.370917200867911	0.725352245598938	4	5	0	0	0	0	1	1
-1700061I17Rik	-1.70286107672665	0.502549782041057	1.71784121912438	0.189971164767189	0.289782217913586	0.554180080413085	5	0.376410018525741	0	0	0	0	0	1.11275160260373	0.725352245598938	1	0	0	0	0	0	3	1
-Dnm1l	1.80630973357149	1.49903122302948	1.71111377842606	0.190840927195392	0.290720290961204	1.46946215427461	26	2.25846011115444	0	7.98842927566929	0	0	1.54680939617797	1.11275160260373	0	6	0	15	0	0	2	3	0
-Snora69	1.08869295668089	1.71806156711187	1.70101781749446	0.192154922565122	0.292331688862405	0.418293401275129	33	2.25846011115444	2.45031621451491	5.32561951711286	1.61891659812001	0.744902431617745	0	2.59642040607538	1.45070449119788	6	4	10	3	1	0	7	2
-E130317F20Rik	1.36400541731017	0.758279590034866	1.69878195937302	0.192447347321435	0.29238671543776	0.231002884653887	9	1.12923005557722	1.22515810725746	1.06512390342257	0	0	0	0.370917200867911	0.725352245598938	3	2	2	0	0	0	1	1
-A330032B11Rik	1.39818933903739	0.748748186748486	1.69255365943135	0.193264682471244	0.292951825626326	0.291687482396111	9	1.50564007410296	0.612579053628728	0.532561951711286	0.539638866040004	0	0	0.741834401735823	0	4	1	1	1	0	0	2	0
-9330178D15Rik	-1.98679676261059	0.524020298165048	1.6920359770324	0.193332799909399	0.292951825626326	1.32055320070191	5	0	0	0.532561951711286	0	0.744902431617745	2.32021409426696	0	0	0	0	1	0	1	3	0	0
-4930573O16Rik	1.59328290275498	0.573882656582763	1.68701797417748	0.193994532694442	0.29330671354677	0.314421647995217	6	1.12923005557722	0	0.532561951711286	0.539638866040004	0	0	0	0.725352245598938	3	0	1	1	0	0	0	1
-Chpt1	1.47700523685698	2.34168158861338	1.68501921353139	0.194258849908786	0.29330671354677	1.06784086774854	61	8.2810204075663	0	13.3140487927822	0	0.744902431617745	2.32021409426696	2.96733760694329	1.45070449119788	22	0	25	0	1	3	8	2
-Adamts10	1.92767141679509	0.821256886501406	1.68441986181312	0.194338190570866	0.29330671354677	1.44950467480637	10	0	2.45031621451491	2.13024780684514	0	0	0	0.741834401735823	0	0	4	4	0	0	0	2	0
-C030016D13Rik	1.59399520847017	0.575124507193501	1.6469661240288	0.199372361326147	0.300507086226068	0.358787781200112	6	1.12923005557722	0.612579053628728	0	0.539638866040004	0.744902431617745	0	0	0	3	1	0	1	1	0	0	0
-Gm12238	1.17743427197628	1.70057102419889	1.63788298021632	0.200616179289878	0.301982929511545	0.538476450306431	32	3.38769016673167	2.45031621451491	5.85818146882415	0	1.48980486323549	1.54680939617797	0.741834401735823	1.45070449119788	9	4	11	0	2	2	2	2
-Snord57	0.270666655754117	9.42386361558089	1.62967609673575	0.201747854774754	0.303286300787872	0.0422885257396755	10268	653.824202179212	827.594301452412	769.552020222808	749.018746063526	584.74840881993	575.413095378206	753.332834962728	570.852217286364	1737	1351	1445	1388	785	744	2031	787
-Pnpla2	1.09889328874311	1.70024363448059	1.6174148337219	0.203452629195912	0.305177538464227	0.442839629674893	32	3.76410018525741	0.612579053628728	4.26049561369029	2.69819433020002	2.97960972647098	0.773404698088987	0.370917200867911	1.45070449119788	10	1	8	5	4	1	1	2
-St5	1.14540632239047	1.43909936145621	1.6167836371524	0.203540847301732	0.305177538464227	0.427462889995017	24	2.63487012968019	1.22515810725746	3.727933661979	1.07927773208001	2.23470729485323	0	0.370917200867911	1.45070449119788	7	2	7	2	3	0	1	2
-D930016D06Rik	1.7668558791528	0.638496128878626	1.61355760724048	0.203992432363846	0.305453235337465	0.887870673528645	7	1.12923005557722	0	1.59768585513386	0	0.744902431617745	0	0	0	3	0	3	0	1	0	0	0
-Brat1	1.6380671683081	0.580282133402032	1.61096898735123	0.204355645830273	0.305596057526004	0.498016793789895	6	0.376410018525741	0	1.59768585513386	0.539638866040004	0	0	0	0.725352245598938	1	0	3	1	0	0	0	1
-Malat1	0.892035948892433	5.26274681035236	1.60518554183735	0.205169889202452	0.306412098926698	0.452900590043496	560	62.8604730937987	5.51321148265855	107.044952293969	16.7288048472401	26.8164875382388	31.7095926216485	9.27293002169778	36.2676122799469	167	9	201	31	36	41	25	50
-Snord73a	0.93858190397472	2.39902888782473	1.60119404201623	0.205734079634238	0.30685305210806	0.385824614926357	64	10.9158905372465	3.67547432177237	4.79305756540158	1.07927773208001	2.23470729485323	2.32021409426696	2.59642040607538	3.62676122799469	29	6	9	2	3	3	7	5
-Gm11602	1.10178148053281	2.35647563869741	1.5959101403641	0.206483767793295	0.307569163253459	0.576530554130015	61	8.2810204075663	1.83773716088618	9.58611513080315	1.07927773208001	0	3.09361879235595	2.22550320520747	4.35211347359363	22	3	18	2	0	4	6	6
-Pradc1	1.9167474053492	0.650415606184684	1.57297893520174	0.209774926985713	0.312044254071375	1.33002709755067	7	0	1.22515810725746	2.13024780684514	0	0	0.773404698088987	0	0	0	2	4	0	0	1	0	0
-Snord89	0.96657482446075	1.80434757594844	1.57118472936867	0.210035045685203	0.312044254071375	0.331669301498173	36	4.89333024083463	1.22515810725746	4.26049561369029	1.61891659812001	1.48980486323549	1.54680939617797	1.11275160260373	2.17605673679681	13	2	8	3	2	2	3	3
-Kcnq1ot1	1.46901866975376	1.11073986368061	1.55307987897301	0.212681342683352	0.315564905073738	0.811842989993786	16	2.63487012968019	0	2.66280975855643	0.539638866040004	0.744902431617745	0	0	1.45070449119788	7	0	5	1	1	0	0	2
-Mir879	-0.957422875609432	1.24255605117401	1.52112436303966	0.217449422870424	0.322220508435265	0.1980926097388	19	1.12923005557722	0.612579053628728	1.06512390342257	0.539638866040004	2.23470729485323	0.773404698088987	2.22550320520747	1.45070449119788	3	1	2	1	3	1	6	2
-Lrrc57	1.88476764894334	0.578218702340393	1.51434673605246	0.218477009254603	0.323323304227629	1.28595199102671	6	0	0	0.532561951711286	2.15855546416002	0	0	0.370917200867911	0	0	0	1	4	0	0	1	0
-A630010A05Rik	-1.06389667985719	1.44453280447873	1.49582215764934	0.22131531189294	0.327099444131923	0.426267937473463	24	1.8820500926287	0.612579053628728	0	1.61891659812001	1.48980486323549	4.64042818853392	1.85458600433956	1.45070449119788	5	1	0	3	2	6	5	2
-BC039771	1.70841256711358	0.635554931729437	1.49045675435669	0.222145605354745	0.327492387207715	0.929498731793885	7	1.50564007410296	0	1.06512390342257	0	0	0.773404698088987	0	0	4	0	2	0	0	1	0	0
-Snhg6	0.363253836479981	6.93079477897274	1.49039475507366	0.222155221471316	0.327492387207715	0.0798920540285094	1762	146.799907225039	144.56865665638	136.335859638089	116.022356198601	108.010852584573	106.72984833628	70.8451853657711	138.542278909397	390	236	256	215	145	138	191	191
-Mir199a-1	1.05557681067525	1.20418398339966	1.48741805741718	0.222617495316257	0.327750402781741	0.279838591324166	18	1.50564007410296	1.22515810725746	2.13024780684514	1.61891659812001	2.23470729485323	0	0.741834401735823	0	4	2	4	3	3	0	2	0
-Mir196b	-1.32127454614675	0.829618781393242	1.48196745688318	0.223466948968203	0.328281648202684	0.492761700442821	10	0	0.612579053628728	0	1.07927773208001	1.48980486323549	0	0.741834401735823	2.17605673679681	0	1	0	2	2	0	2	3
-Pex11b	1.65995741405811	0.804639894669543	1.47905585289627	0.223922301313245	0.328281648202684	1.10073827773443	10	1.50564007410296	0	2.13024780684514	0	0	0	0.741834401735823	0	4	0	4	0	0	0	2	0
-Mir15b	-0.394788504230464	6.20734639920343	1.4787933591529	0.223963407918088	0.328281648202684	0.0932309163706916	1064	61.3548330196957	61.8704844165015	46.3328897988819	82.5647465041206	90.1331942257471	75.0202557146317	85.6818734004875	80.5140992614821	163	101	87	153	121	97	231	111
-Uqcc	1.33988788764212	1.20237271081473	1.47773532337606	0.224129188387284	0.328281648202684	0.713263179340033	18	1.8820500926287	0	4.26049561369029	0.539638866040004	0	1.54680939617797	0.370917200867911	0.725352245598938	5	0	8	1	0	2	1	1
-Snord87	0.36153140061073	6.92853158846234	1.47349079202399	0.224795731993602	0.328835807954744	0.0800479607573439	1759	146.799907225039	143.956077602751	135.803297686378	116.022356198601	108.010852584573	106.72984833628	70.4742681649032	138.542278909397	390	235	255	215	145	138	190	191
-Parp6	1.3190923221389	1.41955838570186	1.46960938805771	0.225407333574819	0.329308281189332	0.794694003082677	24	3.01128014820593	0	4.26049561369029	1.07927773208001	0	1.54680939617797	1.48366880347165	0	8	0	8	2	0	2	4	0
-9130221H12Rik	1.81174070735102	0.574594586237392	1.46729534955508	0.225772911888377	0.329420578599282	1.16623629038014	6	0.376410018525741	0	2.13024780684514	0	0	0	0.370917200867911	0	1	0	4	0	0	0	1	0
-Mir381	-0.53542313827606	4.2010882103495	1.44983721423112	0.228554074655131	0.33283918500619	0.158316469766086	263	22.5846011115444	12.2515810725746	12.7814868410709	8.09458299060006	20.8572680852969	20.8819268484026	26.3351212616217	13.0563404207809	60	20	24	15	28	27	71	18
-Snora21	0.977714238380409	1.28903949414256	1.44893251156917	0.228699317304867	0.33283918500619	0.238819239186323	20	1.12923005557722	2.45031621451491	2.13024780684514	1.61891659812001	1.48980486323549	0	1.11275160260373	0.725352245598938	3	4	4	3	2	0	3	1
-Deaf1	1.44422489044724	1.025131982023	1.42887089792923	0.231948796935886	0.337138315036746	0.852249424298378	14	1.12923005557722	0.612579053628728	3.727933661979	0	0	1.54680939617797	0.370917200867911	0	3	1	7	0	0	2	1	0
-Mir134	-1.14682917623374	1.93261005672816	1.42668479712669	0.232306244213063	0.337171083801986	0.695658731239936	40	2.63487012968019	4.2880533754011	0	0	1.48980486323549	4.64042818853392	3.70917200867911	5.8028179647915	7	7	0	0	2	6	10	8
-Mir377	-1.61378491210756	0.717489850287155	1.42512097441657	0.232562351404175	0.337171083801986	1.06158652526653	8	0	1.22515810725746	0	0	0.744902431617745	0	0.370917200867911	2.90140898239575	0	2	0	0	1	0	1	4
-Ndufs6	1.53512539963964	0.813159285646448	1.4074042846571	0.235487775099432	0.340979126127477	0.899781590706239	10	1.12923005557722	0	2.66280975855643	0	0	0	0.370917200867911	0.725352245598938	3	0	5	0	0	0	1	1
-6720401G13Rik	2.25133431427165	0.768838918008168	1.40046929457477	0.236645011247734	0.34207404316652	2.81750531855614	9	0	0	4.26049561369029	0	0	0	0	0.725352245598938	0	0	8	0	0	0	0	1
-Airn	-1.47387977089094	2.52944495005948	1.39928361818783	0.236843553112665	0.34207404316652	1.34618529718774	66	0	0	10.6512390342257	0	2.97960972647098	13.9212845656018	4.45100641041494	8.70422694718725	0	0	20	0	4	18	12	12
-Gt(ROSA)26Sor	-1.15096936032426	1.54361967638261	1.39703783704678	0.237220163498798	0.342184837613311	0.643443268089939	26	1.12923005557722	0	2.13024780684514	1.61891659812001	2.23470729485323	6.96064228280088	0.370917200867911	2.17605673679681	3	0	4	3	3	9	1	3
-Sec23b	1.82895755193542	0.644850222597462	1.36976728055309	0.241851872143371	0.348227087420191	1.4841173419813	7	0.376410018525741	0	2.66280975855643	0	0.744902431617745	0	0	0	1	0	5	0	1	0	0	0
-E2f6	1.5397355275568	1.10259786021808	1.36879311363269	0.242019351730247	0.348227087420191	1.16390433951397	16	3.01128014820593	0	2.66280975855643	0	1.48980486323549	0	0.370917200867911	0	8	0	5	0	2	0	1	0
-Orc6	1.38084285450997	0.514031868300813	1.36557972586963	0.242572802205214	0.348583837929659	0.070272555699057	5	0.376410018525741	0.612579053628728	0.532561951711286	0.539638866040004	0.744902431617745	0	0	0	1	1	1	1	1	0	0	0
-Clk1	1.28610034119548	2.2155754729238	1.36240393310174	0.243121293147929	0.348932572933066	0.993302407697832	54	6.02256029641185	0	9.58611513080315	3.23783319624003	4.46941458970647	0	2.96733760694329	0	16	0	18	6	6	0	8	0
-Mir339	0.428143686688518	5.34471486348628	1.34652946892237	0.245885757253097	0.352343297417368	0.115900409276972	599	54.2030426677067	53.2943776656993	37.8118985715013	35.0765262926003	28.3062924014743	28.6159738292925	38.9463060911307	37.7183167711448	144	87	71	65	38	37	105	52
-Fbxo34	1.07477259541519	1.07377334724852	1.34522091059477	0.246115344471203	0.352343297417368	0.309939899179427	15	1.50564007410296	1.83773716088618	2.13024780684514	0	0.744902431617745	0.773404698088987	0.370917200867911	0.725352245598938	4	3	4	0	1	1	1	1
-B230217O12Rik	1.42461149495111	0.762941248991215	1.32991835054625	0.24881974382378	0.35576858108137	0.700957646095765	9	0.376410018525741	0	0.532561951711286	2.69819433020002	0	0	0.370917200867911	0.725352245598938	1	0	1	5	0	0	1	1
-Mir101c	1.42009163016917	0.50716611553616	1.32364738152275	0.249938507364293	0.356920947312464	0.214834900602068	5	0.376410018525741	0	1.06512390342257	0.539638866040004	0	0	0.370917200867911	0	1	0	2	1	0	0	1	0
-1700113A16Rik	1.04952261884512	0.8748029109583	1.31593390293379	0.251323093778211	0.358210561619927	0.132374100720653	11	0.752820037051481	0.612579053628728	1.06512390342257	1.61891659812001	0.744902431617745	0	0.370917200867911	0.725352245598938	2	1	2	3	1	0	1	1
-Abhd11	1.36437035299399	0.70312593778772	1.31512152223961	0.251469465256408	0.358210561619927	0.514223097681503	8	0	1.22515810725746	1.06512390342257	1.07927773208001	0	0	0.741834401735823	0	0	2	2	2	0	0	2	0
-Pisd-ps3	1.11246774172608	1.06559741280053	1.3073312903184	0.25287840878152	0.35976840950089	0.398841639277764	15	1.50564007410296	0	2.66280975855643	1.07927773208001	0.744902431617745	0.773404698088987	0.741834401735823	0	4	0	5	2	1	1	2	0
-Snord82	0.51854692486225	3.81085925901395	1.29940283327366	0.254322331781357	0.361372080401655	0.152756163370741	191	17.6912708707098	11.6390020189458	21.3024780684514	9.71349958872008	12.6633413375017	10.8276657732458	7.04742681649032	12.3309881751819	47	19	40	18	17	14	19	17
-1600002D24Rik	-1.33099651490946	0.646039493001314	1.29243978467413	0.255598815822695	0.362419286128489	0.48374758529178	7	0	0	1.06512390342257	0	0	0.773404698088987	0.741834401735823	1.45070449119788	0	0	2	0	0	1	2	2
-Tug1	0.970668426400342	2.63263861953326	1.29191908705431	0.255694588372861	0.362419286128489	0.588507799211238	78	6.77538033346333	1.22515810725746	14.3791726962047	3.23783319624003	2.23470729485323	0	5.93467521388658	4.35211347359363	18	2	27	6	3	0	16	6
-Mir744	0.37765134046399	7.1472318972363	1.28525775628189	0.256923728528825	0.363709645473187	0.102366882067393	2175	165.2439981328	196.025297161193	99.5890849700105	173.763714864881	110.245559879426	102.089420147746	189.167772442635	84.8662127350757	439	320	187	322	148	132	510	117
-Rn45s	0.651022221011047	14.5532392475354	1.27910700081611	0.258065139413137	0.364872768364794	0.315704197416047	360404	47537.5740596528	12635.6681391998	46475.0838399888	10847.8204851362	22385.8078749765	25986.39785579	4549.6703858458	21904.1871125967	126292	20627	87267	20102	30052	33600	12266	30198
-Mir1945	1.35210912624305	0.509953639891973	1.26278126265296	0.26112529137674	0.368742521609975	0.144131562458887	5	0.752820037051481	0	0.532561951711286	0.539638866040004	0	0.773404698088987	0	0	2	0	1	1	0	1	0	0
-Atp10d	1.53331436368778	1.15558274925479	1.25534657940519	0.262533761082479	0.370273203207797	1.32191949957874	17	2.63487012968019	0	3.727933661979	0	1.48980486323549	0	0	0.725352245598938	7	0	7	0	2	0	0	1
-Pvt1	1.13237061461134	0.706845363550169	1.24762806519257	0.264005987966719	0.371322591262319	0.112183395552759	8	0.752820037051481	0.612579053628728	1.06512390342257	0.539638866040004	0.744902431617745	0	0	0.725352245598938	2	1	2	1	1	0	0	1
-Gm2518	1.15651903727818	0.700596843013412	1.24659672944147	0.264203480347787	0.371322591262319	0.157869476939909	8	0.752820037051481	0	1.06512390342257	1.07927773208001	0	0.773404698088987	0.370917200867911	0	2	0	2	2	0	1	1	0
-C230091D08Rik	1.56585828620224	1.16194302765321	1.24517880888728	0.26447530037548	0.371322591262319	1.41644478133509	17	1.8820500926287	0	4.79305756540158	0	0	1.54680939617797	0	0.725352245598938	5	0	9	0	0	2	0	1
-Spns1	1.29130279990572	0.750979881345235	1.24463548150462	0.26457955012819	0.371322591262319	0.507406490692331	9	1.8820500926287	0.612579053628728	0.532561951711286	0	0	0	0.370917200867911	0.725352245598938	5	1	1	0	0	0	1	1
-Wbscr25	-1.5407040165776	0.450389373474786	1.23959677553576	0.265548779159877	0.372225008625823	0.662915151911734	4	0	0	0.532561951711286	0	1.48980486323549	0.773404698088987	0	0	0	0	1	0	2	1	0	0
-Rnf126	1.35231788236301	0.510697356258459	1.23574317095936	0.266293028528527	0.372810239939937	0.18394855075926	5	0.752820037051481	0.612579053628728	0.532561951711286	0	0.744902431617745	0	0	0	2	1	1	0	1	0	0	0
-Etohd2	1.5502415385845	0.577371011234276	1.22484337181513	0.268412228422148	0.375316608614793	0.885850182775548	6	0.752820037051481	0	1.59768585513386	0	0	0.773404698088987	0	0	2	0	3	0	0	1	0	0
-Gm10865	1.24385542624792	0.708647725781233	1.22065432524545	0.269232278648266	0.376002484623832	0.362664882724467	8	0	1.22515810725746	1.06512390342257	1.07927773208001	0	0	0.370917200867911	0.725352245598938	0	2	2	2	0	0	1	1
-Rbbp8	1.28319321283266	1.21201050991301	1.21613527012602	0.270120442407885	0.376781692894128	0.86165179283376	18	1.50564007410296	1.22515810725746	4.26049561369029	0	1.48980486323549	0	0	1.45070449119788	4	2	8	0	2	0	0	2
-Tubgcp4	1.33791798990343	0.977516297021678	1.20023072345027	0.273275568988221	0.380717245684445	0.874960057223072	13	0.752820037051481	0	3.727933661979	0.539638866040004	1.48980486323549	0	0.370917200867911	0	2	0	7	1	2	0	1	0
-C230037L18Rik	1.35620856225365	0.513286892568134	1.18625356562019	0.276086561126146	0.384164349079187	0.272029007915801	5	0.376410018525741	0	0.532561951711286	1.07927773208001	0	0.773404698088987	0	0	1	0	1	2	0	1	0	0
-Sh2d3c	-0.989172553511957	0.989564782427474	1.17988453972444	0.27737950222288	0.385290746832437	0.273751596021872	13	0.752820037051481	0	1.06512390342257	0.539638866040004	1.48980486323549	2.32021409426696	0.370917200867911	1.45070449119788	2	0	2	1	2	3	1	2
-Mir93	0.258061794981831	8.95936436014001	1.17894232081203	0.277571423221966	0.385290746832437	0.0530464153684566	7480	575.907328344383	575.211731357376	368.000308632499	644.328806051765	481.951873256681	371.234255082714	518.171329612472	436.662051850561	1530	939	691	1194	647	480	1397	602
-Zfp207	1.31573613172173	1.24971382078769	1.17162765492472	0.279067053512467	0.386840199590027	0.99740716223018	19	1.8820500926287	0	4.79305756540158	0.539638866040004	0	1.54680939617797	0	1.45070449119788	5	0	9	1	0	2	0	2
-Ubl4	1.06854952853087	1.0735946448232	1.17017272385923	0.279365753253446	0.386840199590027	0.432683488735946	15	1.12923005557722	0	2.66280975855643	1.61891659812001	0.744902431617745	0	0.370917200867911	1.45070449119788	3	0	5	3	1	0	1	2
-Gm13375	1.39917058732533	0.5170958137341	1.1679521666902	0.279822414959382	0.387002879355946	0.416936193500622	5	0	0.612579053628728	1.06512390342257	0.539638866040004	0	0	0	0.725352245598938	0	1	2	1	0	0	0	1
-Klc2	1.3759672979895	0.514840226031161	1.16016309288204	0.281431718975836	0.388757374517468	0.36711541560785	5	0.376410018525741	1.22515810725746	0.532561951711286	0	0	0	0	0.725352245598938	1	2	1	0	0	0	0	1
-Magix	1.58289386102875	0.511362021896504	1.15039037356871	0.283467445680704	0.39109595589079	1.00966038993489	5	0	0.612579053628728	1.59768585513386	0	0	0	0.370917200867911	0	0	1	3	0	0	0	1	0
-Mir1839	0.37048016403446	6.74498720522536	1.14821345887276	0.283923447713764	0.391251997393001	0.109391022289867	1655	113.299415576248	112.101966814057	129.945116217554	121.958383725041	84.9188772044229	58.005352356674	154.301555561051	69.633815577498	301	183	244	226	114	75	416	96
-Snord8	0.806893720861989	1.69268604059307	1.14641299535178	0.284301296093327	0.391300095105533	0.2905292208462	32	3.76410018525741	2.45031621451491	3.19537171026772	1.07927773208001	0.744902431617745	2.32021409426696	1.48366880347165	1.45070449119788	10	4	6	2	1	3	4	2
-4930481A15Rik	1.5515906543429	0.917398122432339	1.12447615887621	0.288956658701854	0.397228370576885	1.46738401507099	12	2.25846011115444	0	2.13024780684514	0	1.48980486323549	0	0	0	6	0	4	0	2	0	0	0
-Dleu2	0.317688954128477	8.19301158324783	1.10602978236287	0.292946616918167	0.402228748379818	0.0855423122435627	4399	326.723896080343	391.438015268757	153.910404044562	421.457954377243	248.797412160327	225.834171841984	319.73062714814	242.267650030045	868	639	289	781	334	292	862	334
-Mir434	-0.549195882867802	5.38683878237064	1.09770962210981	0.294769296489275	0.404244912613297	0.249691576736878	623	60.97842300117	39.8176384858673	6.92330537224672	23.7441101057602	49.1635604867712	43.3106630929833	59.7176693397337	40.6197257535405	162	65	13	44	66	56	161	56
-4930474G06Rik	-1.42872669597032	0.444547317099226	1.08660883725862	0.297223804128962	0.406328756182462	0.663099903648611	4	0	0	0.532561951711286	0	0.744902431617745	0.773404698088987	0.370917200867911	0	0	0	1	0	1	1	1	0
-A430093F15Rik	-1.42872669597032	0.444547317099226	1.08660883725862	0.297223804128962	0.406328756182462	0.663099903648611	4	0	0	0.532561951711286	0	0.744902431617745	0.773404698088987	0.370917200867911	0	0	0	1	0	1	1	1	0
-2900076A07Rik	0.357704988534701	6.75351475086346	1.08600912283684	0.297357152859207	0.406328756182462	0.107790840411909	1663	113.299415576248	112.101966814057	131.010240120976	121.958383725041	86.4086820676584	59.552161752852	154.672472761919	70.359167823097	301	183	246	226	116	77	417	97
-March2	1.14904502284407	1.81024670587524	1.08392796683563	0.297820501941738	0.406430179737368	0.923172571804893	36	5.26974025936037	0	6.39074342053543	1.07927773208001	2.97960972647098	1.54680939617797	0	1.45070449119788	14	0	12	2	4	2	0	2
-Mir3058	-1.20444655402421	0.776024090779448	1.08247837932884	0.298143786538279	0.406430179737368	0.624160194033208	9	0	0	1.06512390342257	0.539638866040004	1.48980486323549	2.32021409426696	0.370917200867911	0	0	0	2	1	2	3	1	0
-H2-T10	1.49091432235163	0.50705923050694	1.07820067470384	0.299100426298579	0.406871218021166	0.88735926488238	5	0.376410018525741	0	1.59768585513386	0	0	0	0.370917200867911	0	1	0	3	0	0	0	1	0
-Snord111	0.606752197653023	2.71306413132783	1.07784341016059	0.299180501244311	0.406871218021166	0.224746103079064	82	8.65743042609204	8.57610675080219	5.32561951711286	3.23783319624003	4.46941458970647	3.09361879235595	4.82192361128285	4.35211347359363	23	14	10	6	6	4	13	6
-Minos1	0.999689645568595	1.34246645297262	1.07429308634616	0.299977748774594	0.407469775418824	0.533576137746882	22	4.14051020378315	0.612579053628728	0.532561951711286	1.61891659812001	0.744902431617745	1.54680939617797	1.11275160260373	0	11	1	1	3	1	2	3	0
-Gm10336	-1.41305683603525	0.444085980903392	1.06720097889373	0.301578535289453	0.408671150552573	0.663114492823721	4	0	0	0	0.539638866040004	0	0	0.370917200867911	1.45070449119788	0	0	0	1	0	0	1	2
-Gm16897	-1.41305683603525	0.444085980903392	1.06720097889373	0.301578535289453	0.408671150552573	0.663114492823721	4	0	0	0	0.539638866040004	0	0	0.370917200867911	1.45070449119788	0	0	0	1	0	0	1	2
-Snord83b	0.412016510679223	3.99035331424224	1.05314046211572	0.304784917333601	0.412526204837056	0.115963050277066	221	18.0676808892356	13.476739179832	18.1071063581837	17.2684437132801	16.3878534955904	9.28085637706784	12.9821020303769	11.605635929583	48	22	34	32	22	12	35	16
-Fam19a2	-1.39439412627524	0.445236305727518	1.04277451756083	0.307177069371358	0.415271369849194	0.663078115250798	4	0	0.612579053628728	0	0	0.744902431617745	0	0.370917200867911	0.725352245598938	0	1	0	0	1	0	1	1
-Gm6793	1.43759807516063	0.570980107794987	1.02892446786792	0.310411413147432	0.41914724544523	0.985765099419783	6	1.50564007410296	0	0.532561951711286	0	0	0.773404698088987	0	0	4	0	1	0	0	1	0	0
-Mir677	-0.556581193997953	2.91938107006516	1.02166547373758	0.312124279613925	0.420961942127055	0.207540332078884	94	5.26974025936037	3.67547432177237	5.85818146882415	6.47566639248005	11.9184389058839	9.28085637706784	6.30559241475449	4.35211347359363	14	6	11	12	16	12	17	6
-Mir15a	0.323472503910934	8.01067798948637	1.01525465382521	0.313647269211347	0.422516569268178	0.0966057812088073	3883	289.83571426482	347.944902461118	124.08693474873	379.366122826123	219.001314895617	197.991602710781	287.089913471763	206.725389995697	770	568	233	703	294	256	774	285
-Rmi1	1.19649467559255	0.703415054910478	1.01273533114761	0.314248428983006	0.422827190412276	0.536557274958839	8	0.376410018525741	0	2.13024780684514	0.539638866040004	0.744902431617745	0	0.370917200867911	0	1	0	4	1	1	0	1	0
-Myeov2	1.3752791899028	0.500631880884289	1.00847898319514	0.315267503481701	0.42369872964973	0.68508973865761	5	1.12923005557722	0	0.532561951711286	0	0	0	0.370917200867911	0	3	0	1	0	0	0	1	0
-Snhg5	-0.89125240486064	0.988818036196163	1.00092990670477	0.317085602234455	0.425640790764134	0.242108615776921	13	0.376410018525741	1.22515810725746	0	1.07927773208001	2.23470729485323	0.773404698088987	0.741834401735823	1.45070449119788	1	2	0	2	3	1	2	2
-Alg9	1.38266937531919	0.822272867813765	0.991092837701364	0.319475422030725	0.428344837293839	1.20246527903066	10	0.752820037051481	0	3.19537171026772	0	0.744902431617745	0.773404698088987	0	0	2	0	6	0	1	1	0	0
-Mir296	-1.03122376978726	0.868255875953981	0.969681931175626	0.324759517149051	0.434918555243036	0.477911424633654	11	1.12923005557722	0	0.532561951711286	0	0.744902431617745	0	1.11275160260373	2.17605673679681	3	0	1	0	1	0	3	3
-Cnot2	1.1614798897584	1.30506961499225	0.964321981541516	0.326100354524109	0.436202232722166	0.976345068131583	21	4.14051020378315	0	2.66280975855643	0	0	1.54680939617797	0.741834401735823	0.725352245598938	11	0	5	0	0	2	2	1
-Gm10649	-1.17056180193159	0.590943716672117	0.950295172427507	0.329644175096118	0.440426233939895	0.45580559320795	6	0	0	0.532561951711286	0.539638866040004	1.48980486323549	0.773404698088987	0	0.725352245598938	0	0	1	1	2	1	0	1
-4933433G15Rik	-1.31542050079851	0.438830702629464	0.944809572865336	0.331044004303658	0.440822941307312	0.663280688352102	4	0	0	0	0.539638866040004	0	0.773404698088987	0.741834401735823	0	0	0	0	1	0	1	2	0
-Mir487b	-1.31542050079851	0.438830702629464	0.944809572865336	0.331044004303658	0.440822941307312	0.663280688352102	4	0	0	0	0.539638866040004	0	0.773404698088987	0.741834401735823	0	0	0	0	1	0	1	2	0
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-Slc12a4	0.356530102061702	0.700902916845484	0.109824556779557	0.74034396385002	0.820128604614439	0.388542539016462	8	1.12923005557722	0.612579053628728	0.532561951711286	0	0	1.54680939617797	0.370917200867911	0	3	1	1	0	0	2	1	0
-Mir669a-1	-0.282841976476736	1.02913129033703	0.104306295762486	0.746721860700429	0.825961375739553	0.250623912051666	14	1.12923005557722	1.22515810725746	0.532561951711286	0.539638866040004	1.48980486323549	0	0.741834401735823	2.17605673679681	3	2	1	1	2	0	2	3
-Mir30c-2	0.10691804578271	6.47799654504875	0.104020597004281	0.747057090064171	0.825961375739553	0.0993022410381055	1345	84.3158441497659	85.1484884543932	104.382142535412	90.6593294947207	86.4086820676584	68.0596134318308	123.515427889014	58.753531893514	224	139	196	168	116	88	333	81
-C330013E15Rik	0.425364547798013	0.705776351352618	0.1026909716117	0.748623952822721	0.826892478384051	1.06614869073738	8	0.376410018525741	0	0	2.15855546416002	0	0	0.370917200867911	1.45070449119788	1	0	0	4	0	0	1	2
-Gm10033	0.446605568438783	0.795097543606531	0.0981895104323094	0.75401320570929	0.832039717325242	1.46176677300876	10	1.8820500926287	0	0.532561951711286	0	0	0	1.48366880347165	0	5	0	1	0	0	0	4	0
-Mir667	-0.4125649978383	0.582911301651855	0.0971422364121199	0.755286394390143	0.832639397100631	0.753494772671829	6	0.752820037051481	0.612579053628728	0	0	0	0.773404698088987	0	1.45070449119788	2	1	0	0	0	1	0	2
-4930429F24Rik	0.372517002600183	0.716762658399084	0.0943811879277181	0.758679565760408	0.835572764992882	0.745918087137385	8	0	1.22515810725746	1.59768585513386	0	0.744902431617745	0.773404698088987	0	0.725352245598938	0	2	3	0	1	1	0	1
-Snord88a	-0.116579002052208	5.40848950651294	0.0915722883193331	0.76218775231403	0.838627025448706	0.128668821063436	589	36.1353617784711	37.9799013249811	53.7887571228399	31.8386930963603	51.3982677816244	56.458542960496	28.5606244668292	37.7183167711448	96	62	101	59	69	73	77	52
-Snora78	-0.26473132762471	1.35813176879128	0.085893819044049	0.769463699389677	0.845018366485607	0.476932540601757	22	1.8820500926287	0	2.13024780684514	1.07927773208001	1.48980486323549	0	1.85458600433956	2.90140898239575	5	0	4	2	2	0	5	4
-Snhg9	-0.26473349096456	1.35813176879128	0.0858830645434763	0.769477723732292	0.845018366485607	0.477052525751055	22	1.8820500926287	0	2.13024780684514	1.07927773208001	1.48980486323549	0	1.85458600433956	2.90140898239575	5	0	4	2	2	0	5	4
-Snora75	0.340239733242681	0.803580132003168	0.0837770977297607	0.77224259482451	0.847239231437275	0.73660805329414	10	1.50564007410296	0	1.06512390342257	0	0	0	1.11275160260373	0.725352245598938	4	0	2	0	0	0	3	1
-Gm11747	0.242704774785282	0.921496479498808	0.0796925124372807	0.777714541494161	0.851058778862465	0.131665348739081	12	0.752820037051481	0.612579053628728	1.06512390342257	1.07927773208001	0.744902431617745	0.773404698088987	1.11275160260373	0	2	1	2	2	1	1	3	0
-Mir5122	0.218731838517834	1.48404740864424	0.0794784379816886	0.778005463346698	0.851058778862465	0.293959292898116	25	2.25846011115444	2.45031621451491	0.532561951711286	2.15855546416002	2.23470729485323	1.54680939617797	0.741834401735823	2.17605673679681	6	4	1	4	3	2	2	3
-2410137F16Rik	0.341735189451036	0.50379538111903	0.0794570931045415	0.778034493679824	0.851058778862465	0.265951097280125	5	0.376410018525741	0	0.532561951711286	0.539638866040004	0	0	0.741834401735823	0	1	0	1	1	0	0	2	0
-Gm13483	-0.405259819671297	0.441450511506885	0.0788340733649076	0.778883699912491	0.851058778862465	0.663197836850272	4	0.752820037051481	0	0	0	0.744902431617745	0.773404698088987	0	0	2	0	0	0	1	1	0	0
-Cdh23	0.259325213039656	1.01902545028967	0.0778130211141939	0.780283308337702	0.851058778862465	0.323423403817609	14	0.752820037051481	1.22515810725746	1.59768585513386	0.539638866040004	0	0	1.48366880347165	1.45070449119788	2	2	3	1	0	0	4	2
-4921531C22Rik	0.364921908940289	0.515191961700188	0.0777501959057494	0.78036974848368	0.851058778862465	0.563424790023917	5	0	1.22515810725746	0	0.539638866040004	0	0.773404698088987	0.370917200867911	0	0	2	0	1	0	1	1	0
-4930526L06Rik	-0.400926323794431	0.441282103013107	0.0773326077835055	0.780945259832604	0.851058778862465	0.663203162665824	4	0.752820037051481	0	0	0	0	0.773404698088987	0	0.725352245598938	2	0	0	0	0	1	0	1
-4930565N06Rik	-0.275692016771903	0.704972067181646	0.0745521979400161	0.784820558538014	0.853759421766179	0.187746764272001	8	0.752820037051481	0	1.06512390342257	0	0.744902431617745	0.773404698088987	0.370917200867911	0.725352245598938	2	0	2	0	1	1	1	1
-Gm16039	0.203635382066304	1.53814985640646	0.0744816256008043	0.784919924130343	0.853759421766179	0.266864141711064	27	3.01128014820593	1.22515810725746	1.06512390342257	2.15855546416002	1.48980486323549	2.32021409426696	1.48366880347165	1.45070449119788	8	2	2	4	2	3	4	2
-1600020E01Rik	-0.390597471075805	0.440882265837424	0.073819783748645	0.785854266766057	0.853961636552449	0.663215807320349	4	0.752820037051481	0	0	0	0	0	0	1.45070449119788	2	0	0	0	0	0	0	2
-Fance	0.500843331601474	0.585954539136612	0.0720524697545653	0.78837149692123	0.855270402132738	3.124339363091	6	0	0	2.13024780684514	0	1.48980486323549	0	0	0	0	0	4	0	2	0	0	0
-E230016M11Rik	0.342721174693478	0.513769470217108	0.0717982032818814	0.788736368472031	0.855270402132738	0.488493855150857	5	0	0.612579053628728	1.06512390342257	0	0.744902431617745	0	0.370917200867911	0	0	1	2	0	1	0	1	0
-1700086O06Rik	0.236269656222048	1.48883272976725	0.0714012436871725	0.789307391275875	0.855270402132738	0.475993254072284	26	3.01128014820593	1.22515810725746	0.532561951711286	2.15855546416002	0.744902431617745	2.32021409426696	2.59642040607538	0	8	2	1	4	1	3	7	0
-4930473A02Rik	0.352471611631002	0.500477705690642	0.0701446217076507	0.791126327458405	0.856428026214459	0.646696111281133	5	0.752820037051481	0	0.532561951711286	0	0	0	0.741834401735823	0	2	0	1	0	0	0	2	0
-A230020J21Rik	-0.26702678833372	0.705749087999938	0.0694037210226259	0.792206947588702	0.856784954690719	0.20358355520215	8	0.752820037051481	0.612579053628728	0	0.539638866040004	0.744902431617745	0	0.370917200867911	1.45070449119788	2	1	0	1	1	0	1	2
-4930583K01Rik	-0.311376486734374	0.585998814634588	0.066941594503537	0.795843001811579	0.859902334343761	0.427642087614707	6	0.376410018525741	0.612579053628728	0	0.539638866040004	0.744902431617745	0	0	1.45070449119788	1	1	0	1	1	0	0	2
-Snord70	0.145331626973595	2.86064497684293	0.0657897222156478	0.797568584790053	0.860951518680654	0.222052907955902	92	8.65743042609204	6.12579053628728	3.19537171026772	7.55494412456006	4.46941458970647	8.50745167897886	5.93467521388658	4.35211347359363	23	10	6	14	6	11	16	6
-Arhgap1	0.339569541187172	1.2096566695747	0.064981287366308	0.798789310911968	0.861454256853077	1.44007572113629	18	2.25846011115444	0	3.19537171026772	0	2.23470729485323	2.32021409426696	0	0	6	0	6	0	3	3	0	0
-AI450353	0.340558451478882	0.910101618432891	0.063246612062132	0.801436227172968	0.863492667803925	1.43735572039671	12	3.01128014820593	0	0	0	0.744902431617745	0	0.370917200867911	1.45070449119788	8	0	0	0	1	0	1	2
-Abhd10	-0.276151496509402	0.585317127970325	0.0624086266547552	0.802728728192441	0.863759655420483	0.171774391618725	6	0.376410018525741	0	0.532561951711286	0.539638866040004	0.744902431617745	0.773404698088987	0	0.725352245598938	1	0	1	1	1	1	0	1
-Mir5114	-0.308027420198161	0.576749028964489	0.0613324248443501	0.804402249121322	0.863759655420483	0.546573167112509	6	0.752820037051481	0	0	0.539638866040004	0	1.54680939617797	0.370917200867911	0	2	0	0	1	0	2	1	0
-Svip	-0.350973217175863	0.444935973798038	0.0605317094627615	0.805657517222393	0.863759655420483	0.663087612872245	4	0.376410018525741	0	0.532561951711286	0	0	1.54680939617797	0	0	1	0	1	0	0	2	0	0
-Mir3473	-0.272529890056161	0.586398376437974	0.0604269268049213	0.805822433361309	0.863759655420483	0.185359945015693	6	0.376410018525741	0.612579053628728	0	0.539638866040004	0.744902431617745	0.773404698088987	0	0.725352245598938	1	1	0	1	1	1	0	1
-Mir1306	-0.1851941109557	1.27726022626805	0.0596401678006564	0.807065576372413	0.863759655420483	0.210448917094858	20	1.50564007410296	1.22515810725746	1.06512390342257	1.07927773208001	0.744902431617745	0.773404698088987	2.22550320520747	1.45070449119788	4	2	2	2	1	1	6	2
-Mir1946b	0.227524210048923	1.08130128376378	0.0593307770828053	0.807556818003022	0.863759655420483	0.410864247283655	15	0.752820037051481	0	2.13024780684514	1.61891659812001	0.744902431617745	0.773404698088987	0.370917200867911	2.17605673679681	2	0	4	3	1	1	1	3
-Gm19522	-0.282927454068442	0.707964870962573	0.0590387697299386	0.808021704992552	0.863759655420483	0.57947688717992	8	0.376410018525741	0	1.59768585513386	0	0	0.773404698088987	0.370917200867911	1.45070449119788	1	0	3	0	0	1	1	2
-B230319C09Rik	-0.344937059890468	0.444704025045226	0.0587065080967655	0.808552161679303	0.863759655420483	0.663094947958413	4	0.376410018525741	0	0.532561951711286	0	0.744902431617745	0.773404698088987	0	0	1	0	1	0	1	1	0	0
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-2610035D17Rik	-0.340603774495524	0.444535312857616	0.0574097330026992	0.810637819798016	0.863759655420483	0.663100283267538	4	0.376410018525741	0	0.532561951711286	0	0	0.773404698088987	0	0.725352245598938	1	0	1	0	0	1	0	1
-1700007J10Rik	0.31576310409063	0.642271288008478	0.0573291362326076	0.810768265517386	0.863759655420483	0.825778513949122	7	0	0.612579053628728	1.59768585513386	0	0	0	0.741834401735823	0.725352245598938	0	1	3	0	0	0	2	1
-Gm10548	-0.219560840499605	0.703615641790463	0.0554878370826657	0.813775229792994	0.866154419024073	0.0594854790099508	8	0.376410018525741	0.612579053628728	0.532561951711286	0.539638866040004	0	0.773404698088987	0.741834401735823	0.725352245598938	1	1	1	1	0	1	2	1
-C730002L08Rik	0.281569792976704	0.50929338696675	0.0545302073589191	0.815359940278172	0.867032331647152	0.336468455814993	5	0.376410018525741	0	1.06512390342257	0	0	0	0.370917200867911	0.725352245598938	1	0	2	0	0	0	1	1
-Thap6	-0.228642515349319	0.700752433660892	0.049311618623209	0.824265466830191	0.875686124444364	0.270900876411097	8	0.752820037051481	0.612579053628728	0.532561951711286	0	0	1.54680939617797	0.741834401735823	0	2	1	1	0	0	2	2	0
-Mir423	-0.0744500249085479	8.84921671367008	0.0477299944992389	0.827061408335607	0.877839132010165	0.110378535571433	6997	452.821252286466	622.380318486788	186.929245050661	530.465005317324	495.3601170258	328.696996687819	675.811139981334	385.887394658635	1203	1016	351	983	665	425	1822	532
-AF357426	0.301433033904942	0.627241496694233	0.0452689227626708	0.83151030578685	0.880712988936908	1.1339324847852	7	1.12923005557722	0.612579053628728	0	0	0	0	1.11275160260373	0	3	1	0	0	0	0	3	0
-Gm7367	0.248315611787722	0.506240872372134	0.0451945925355544	0.8316466170932	0.880712988936908	0.282626853737893	5	0.752820037051481	0	0.532561951711286	0	0.744902431617745	0	0.370917200867911	0	2	0	1	0	1	0	1	0
-Mir5129	-0.221316550567267	0.712411651231399	0.0446696870571808	0.83261257374908	0.880712988936908	0.320432296498945	8	0	0.612579053628728	0.532561951711286	1.07927773208001	0	0.773404698088987	0.370917200867911	1.45070449119788	0	1	1	2	0	1	1	2
-Lmf1	-0.225487960478568	0.97883425965457	0.0445376198782759	0.832856542561721	0.880712988936908	0.558630351034666	13	1.50564007410296	0	1.59768585513386	0	0.744902431617745	2.32021409426696	0.370917200867911	0.725352245598938	4	0	3	0	1	3	1	1
-Rhno1	-0.289809107838437	0.448195063662935	0.0427642768364294	0.836169763254006	0.883397870252612	0.662984547299375	4	0	0	1.06512390342257	0	0	1.54680939617797	0	0	0	0	2	0	0	2	0	0
-Mdp1	-0.246852882614077	0.579048510757911	0.0401952105453862	0.841099385969895	0.887184784708128	0.578850144736575	6	0.376410018525741	0	1.06512390342257	0	0	0	0.370917200867911	1.45070449119788	1	0	2	0	0	0	1	2
-4930452A19Rik	-0.277343300032949	0.435635447316297	0.0395376746856142	0.842387257879236	0.887184784708128	0.663381744779537	4	0.752820037051481	0	0	0	0	0.773404698088987	0.370917200867911	0	2	0	0	0	0	1	1	0
-Mir20b	-0.277343300032949	0.435635447316297	0.0395376746856142	0.842387257879236	0.887184784708128	0.663381744779537	4	0.752820037051481	0	0	0	0	0.773404698088987	0.370917200867911	0	2	0	0	0	0	1	1	0
-4930555B11Rik	0.136762824649001	2.39033698550999	0.0392954843817712	0.842864422983007	0.887184784708128	0.336389297324335	61	6.77538033346333	1.83773716088618	5.32561951711286	3.23783319624003	2.97960972647098	5.41383288662291	1.48366880347165	6.52817021039044	18	3	10	6	4	7	4	9
-A430071A18Rik	-0.269112364005017	0.447392391552775	0.0376139194800551	0.846220448676115	0.889076917071314	0.663009931310973	4	0	0	1.06512390342257	0	0	0	0	1.45070449119788	0	0	2	0	0	0	0	2
-Cd22	-0.269112364005017	0.447392391552775	0.0376139194800551	0.846220448676115	0.889076917071314	0.663009931310973	4	0	0	1.06512390342257	0	0	0	0	1.45070449119788	0	0	2	0	0	0	0	2
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-Rbmx	0.0986438999749744	4.04533361891745	0.0351385203591095	0.851305694435457	0.892775549035714	0.231893714397037	240	18.0676808892356	20.215108769748	17.5745444064724	6.47566639248005	12.6633413375017	6.96064228280088	27.8187900650933	9.42957919278619	48	33	33	12	17	9	75	13
-2610002J02Rik	-0.186800122590262	0.574544504406253	0.0317659246695001	0.858542368435858	0.898715157487363	0.156379445413877	6	0.376410018525741	0.612579053628728	0.532561951711286	0	0	0	0.741834401735823	0.725352245598938	1	1	1	0	0	0	2	1
-4930527F14Rik	-0.24428103506993	0.448591927059054	0.0317648588182369	0.858544716618077	0.898715157487363	0.662971996634328	4	0	0.612579053628728	0	0.539638866040004	0	0	0	1.45070449119788	0	1	0	1	0	0	0	2
-Mir376b	-0.158192220319779	1.94229985404655	0.0291620904822585	0.864405315323923	0.904020591003296	0.661049460546989	42	6.02256029641185	3.06289526814364	0.532561951711286	0	1.48980486323549	3.09361879235595	4.08008920954702	2.17605673679681	16	5	1	0	2	4	11	3
-Btbd19	-0.227998019592194	0.449745032994211	0.0281794627865608	0.866687599979336	0.904817325469295	0.662935529585305	4	0	1.22515810725746	0	0	0.744902431617745	0	0	0.725352245598938	0	2	0	0	1	0	0	1
-Mir1966	-0.168385030472092	0.694856549428838	0.0281514991045873	0.866753143503891	0.904817325469295	0.282676636878928	8	0.752820037051481	0.612579053628728	0.532561951711286	0	0	0.773404698088987	1.11275160260373	0	2	1	1	0	0	1	3	0
-Mir541	-0.0771686464735477	6.66411334271104	0.0276499777686858	0.867934381831919	0.905222239186673	0.201614530593495	1553	181.806038947933	115.164862082201	25.5629736821417	66.3755805229205	90.1331942257471	95.9021825630344	118.693504277732	105.901427857445	483	188	48	123	121	124	320	146
-2010320M18Rik	-0.153440953029659	0.817265649192632	0.0272545555970316	0.868873507395072	0.905374129623541	0.260143808082556	10	0.376410018525741	1.22515810725746	0.532561951711286	0.539638866040004	0	0	1.11275160260373	1.45070449119788	1	2	1	1	0	0	3	2
-Mir598	0.117106193593944	2.01449875553177	0.0265919621605635	0.870462988759268	0.90572735890301	0.318163899000955	44	3.01128014820593	4.90063242902982	1.06512390342257	3.23783319624003	1.48980486323549	1.54680939617797	4.08008920954702	3.62676122799469	8	8	2	6	2	2	11	5
-Mir29b-1	-0.208341414352939	0.834692809796765	0.0264525099116115	0.870800098787556	0.90572735890301	1.08109841309526	10	0	2.45031621451491	0.532561951711286	0	0.744902431617745	2.32021409426696	0.370917200867911	0	0	4	1	0	1	3	1	0
-Gm15545	-0.217022286505151	0.43887938549888	0.0257098809174789	0.872610879912088	0.906784165737425	0.663279148717022	4	0.376410018525741	0	0.532561951711286	0	0	0.773404698088987	0.370917200867911	0	1	0	1	0	0	1	1	0
-Mir147	-0.214870858659419	0.438986983809201	0.0252765020853651	0.873680018168807	0.907069063449144	0.663275745839563	4	0.376410018525741	0	0	0.539638866040004	0	0.773404698088987	0.370917200867911	0	1	0	0	1	0	1	1	0
-Gm5095	-0.211230984542268	0.438648247953698	0.0245845189775671	0.875406798183938	0.907639457476068	0.663286458644096	4	0.376410018525741	0	0.532561951711286	0	0.744902431617745	0	0.370917200867911	0	1	0	1	0	1	0	1	0
-Mir18	-0.09904618111702	2.43255748654297	0.0244202462369061	0.875820370447985	0.907639457476068	0.262434880797944	62	2.63487012968019	5.51321148265855	2.13024780684514	7.01530525852006	5.21431702132421	5.41383288662291	2.96733760694329	5.07746571919256	7	9	4	13	7	7	8	7
-Gm10785	0.246751251689773	0.623026703715629	0.021644951920698	0.883035540149543	0.913456047408021	2.19651377696506	7	1.50564007410296	0	0	0	0	0	1.11275160260373	0	4	0	0	0	0	0	3	0
-Plscr3	-0.194781611758839	0.439965845826999	0.0214241887193065	0.883629276882342	0.913456047408021	0.663244788948983	4	0.376410018525741	0.612579053628728	0	0	0	0.773404698088987	0.370917200867911	0	1	1	0	0	0	1	1	0
-Ftx	0.0442562859858771	6.90323954909954	0.0213480502831525	0.883834773302765	0.913456047408021	0.0833237482571728	1746	143.788627076833	106.588755331399	88.4052839840735	142.464660634561	117.694584195604	100.542610751568	97.1803066273928	152.323971575777	382	174	166	264	158	130	262	210
-1810034E14Rik	-0.0921399384667942	2.43383344256362	0.0208468626407154	0.88519691373511	0.913699658868852	0.275109743703655	64	4.14051020378315	6.12579053628728	2.13024780684514	4.31711092832003	6.7041218845597	1.54680939617797	5.56375801301867	3.62676122799469	11	10	4	8	9	2	15	5
-Snord61	0.0765324626137025	4.00249776431419	0.0206734656726466	0.885672061971035	0.913699658868852	0.236868929008775	233	17.3148608521841	18.9899506624906	17.0419824547612	6.47566639248005	11.9184389058839	6.18723758471189	27.8187900650933	9.42957919278619	46	31	32	12	16	8	75	13
-1110019D14Rik	0.167902116965802	0.515697023285334	0.0202799335549235	0.886758034631578	0.913993602090904	0.549541191684745	5	0.376410018525741	0	0	1.07927773208001	0.744902431617745	0.773404698088987	0	0	1	0	0	2	1	1	0	0
-Mirlet7g	0.0421573852939418	11.2520713569692	0.0161689790359105	0.898815900346968	0.925585687992681	0.105394399266193	38152	2846.03615007313	2901.17439798566	1421.94041106913	2723.5573569039	2055.93071126498	1812.0872076225	3890.17960270265	1847.47216954049	7561	4736	2670	5047	2760	2343	10488	2547
-Zmynd8	-0.0949628221617816	1.65780389373413	0.0137976813222194	0.906492741283485	0.932316202413436	0.385874123779875	30	1.12923005557722	1.83773716088618	4.26049561369029	1.07927773208001	3.72451215808872	3.09361879235595	1.48366880347165	0.725352245598938	3	3	8	2	5	4	4	1
-2810001G20Rik	-0.147420769233856	0.429608186028715	0.0136518455727499	0.906985963785201	0.932316202413436	0.66357239009262	4	0.752820037051481	0	0	0	0	0	0.741834401735823	0	2	0	0	0	0	0	2	0
-Mir10a	-0.0474697256380123	14.9476035526803	0.0130438853993837	0.909071459233041	0.933618843370747	0.165973497988853	435679	26662.2508422338	36481.5329598053	22924.1292114123	38247.9839083174	26200.4532272909	53305.3720063872	15001.3752719018	33968.2456613983	70833	59554	43045	70877	35173	68923	40444	46830
-4930547E14Rik	-0.141532389694495	0.441943990602939	0.0125841052378952	0.910681560216746	0.93443134910729	0.663182230938337	4	0	0	0	1.07927773208001	0	0	0.370917200867911	0.725352245598938	0	0	0	2	0	0	1	1
-Pfkfb2	0.105273545930285	1.33195915147488	0.0121131136690922	0.912362114767682	0.935314620799573	0.577808434849009	21	1.12923005557722	0	4.26049561369029	0.539638866040004	0.744902431617745	2.32021409426696	1.11275160260373	1.45070449119788	3	0	8	1	1	3	3	2
-Gm15441	0.0780434196844491	2.02230388105404	0.010169282238599	0.919675203562429	0.941269119470948	0.417333073299858	43	2.25846011115444	0.612579053628728	5.85818146882415	3.77747206228003	4.46941458970647	2.32021409426696	1.11275160260373	4.35211347359363	6	1	11	7	6	3	3	6
-2310001K24Rik	-0.123279260083545	0.442816530704177	0.0101324286488609	0.919820392822179	0.941269119470948	0.663154637691565	4	0	0.612579053628728	0.532561951711286	0	0	0	0.370917200867911	0.725352245598938	0	1	1	0	0	0	1	1
-A930015D03Rik	0.0859151926929213	1.36572130390349	0.00942686840160789	0.922653285101637	0.943322041488322	0.492017118766544	22	2.63487012968019	0.612579053628728	1.59768585513386	1.07927773208001	3.72451215808872	1.54680939617797	0.741834401735823	0	7	1	3	2	5	2	2	0
-Zc3h7a	-0.0668358501012109	5.69031492491042	0.00701387167050171	0.933256108339025	0.953308164382119	0.60189041106342	754	126.473766224649	10.4138439116884	52.7236332194173	7.55494412456006	110.245559879426	46.4042818853392	7.41834401735823	43.5211347359363	336	17	99	14	148	60	20	60
-Gm15713	0.0969082615946592	0.644365940186003	0.00606852116740964	0.937907031828168	0.957202078100125	0.87570585131856	7	0.376410018525741	0	1.59768585513386	0	0	1.54680939617797	0.370917200867911	0	1	0	3	0	0	2	1	0
-Mir101a	0.0187463621453821	10.33617221496	0.00533099108497481	0.941795266280249	0.960311348369763	0.0629120870617446	19692	1149.17978655909	1264.36316668969	1605.67428440953	1180.72983889553	1087.55755016191	1068.07188806089	1844.94215711699	1129.37344639755	3053	2064	3015	2188	1460	1381	4974	1557
-E130102H24Rik	0.0184637983551309	10.3369640511302	0.00517084903614773	0.942674633331049	0.960349782706006	0.062920227106964	19702	1149.93260659614	1264.36316668969	1607.27197026466	1180.72983889553	1089.79225745676	1068.07188806089	1845.31307431786	1130.09879864315	3055	2064	3018	2188	1463	1381	4975	1558
-Mir17	0.0263848006973678	5.19330849849068	0.00482973042491786	0.9445946079939	0.961447321785049	0.122797096744261	534	36.1353617784711	38.5924803786099	27.1606595372756	41.0125538190403	40.969633738976	27.8425691312035	45.2518985058852	25.3873285959628	96	63	51	76	55	36	122	35
-Mir1940	0.0461015892262148	0.765417696099668	0.00275503640587133	0.95813950563393	0.974364684428088	0.0575915210815779	9	0.752820037051481	0.612579053628728	0.532561951711286	0.539638866040004	0.744902431617745	0.773404698088987	0.370917200867911	0.725352245598938	2	1	1	1	1	1	1	1
-Srsf9	0.0560106917056318	1.03618949071648	0.00198539947999343	0.964459762605131	0.979600222217201	1.24067995254529	14	0.376410018525741	0	3.727933661979	0	1.48980486323549	0	0.370917200867911	2.17605673679681	1	0	7	0	2	0	1	3
-1500017E21Rik	0.0442484285848243	1.30258841717713	0.00181010011044869	0.96606402311823	0.979600222217201	0.889719238393225	20	1.50564007410296	0	3.727933661979	0.539638866040004	0.744902431617745	2.32021409426696	0	2.90140898239575	4	0	7	1	1	3	0	4
-Dio3os	-0.0496676415204486	0.773498972596457	0.00166750535258586	0.967427357317129	0.979600222217201	0.367416862910588	9	0.376410018525741	0	1.06512390342257	1.07927773208001	0.744902431617745	1.54680939617797	0	0.725352245598938	1	0	2	2	1	2	0	1
-6330549D23Rik	0.0365504895754985	0.766481265149971	0.00165505138189159	0.967549154353767	0.979600222217201	0.144795356612801	9	0.752820037051481	1.22515810725746	0	0.539638866040004	0.744902431617745	0.773404698088987	0.370917200867911	0.725352245598938	2	2	0	1	1	1	1	1
-Gm16291	-0.0497984136905431	1.04049827004916	0.00165184486677905	0.96758058758877	0.979600222217201	0.760533853439203	14	0.752820037051481	0.612579053628728	2.66280975855643	0	2.23470729485323	1.54680939617797	0	0.725352245598938	2	1	5	0	3	2	0	1
-Gm4890	-0.0259416163380367	0.436081268353407	0.00151936462515234	0.968907109122493	0.98007359176309	0.663367644323302	4	0	0	1.06512390342257	0	0	0	0.741834401735823	0	0	0	2	0	0	0	2	0
-5430402O13Rik	-0.0215659699049333	0.436296133284018	0.00131072419039668	0.971119756290488	0.980573134449068	0.66336084861507	4	0	0	0	1.07927773208001	0	0	0.741834401735823	0	0	0	0	2	0	0	2	0
-Gm6297	-0.0215659699049333	0.436296133284018	0.00131072419039668	0.971119756290488	0.980573134449068	0.66336084861507	4	0	0	0	1.07927773208001	0	0	0.741834401735823	0	0	0	0	2	0	0	2	0
-G730013B05Rik	-0.0385365765720963	1.01651726367811	0.000926357662148547	0.975719235170337	0.983633409630645	0.703663961483763	14	2.25846011115444	0	1.06512390342257	0	1.48980486323549	1.54680939617797	0.741834401735823	0	6	0	2	0	2	2	2	0
-Fut8	-0.0301535765572007	0.983272857012977	0.000914530335813346	0.975874688608142	0.983633409630645	0.461988777734778	13	0.376410018525741	0.612579053628728	2.66280975855643	0	0.744902431617745	1.54680939617797	0.741834401735823	0.725352245598938	1	1	5	0	1	2	2	1
-Mir451	0.00783986907310383	10.6424503554937	0.000668429572201035	0.979373786499111	0.986289047127525	0.0880041428130113	23834	1259.46792198713	1282.12795924493	1240.8693474873	2624.26380555254	1712.5306902892	1143.86554847361	1957.70098618084	1556.60591905532	3346	2093	2330	4863	2299	1479	5278	2146
-Mir3103	-0.0011092316730465	0.437273490728585	0.00054275250380531	0.981413325432899	0.987471432380016	0.663329937374097	4	0	0.612579053628728	0	0.539638866040004	0	0	0.741834401735823	0	0	1	0	1	0	0	2	0
-Mir877	0.0169238059397901	2.07117778204749	0.000493226392977775	0.982281477037185	0.987474154448835	0.411290532451564	45	2.25846011115444	4.2880533754011	0.532561951711286	5.93602752644005	2.23470729485323	3.09361879235595	1.85458600433956	5.8028179647915	6	7	1	11	3	4	5	8
-Mirlet7a-2	0.00692268187132084	3.52904314853656	0.000229292382525781	0.987918571764677	0.991638607573432	0.145210168895582	158	13.1743506484009	10.4138439116884	7.98842927566929	9.71349958872008	8.93882917941294	9.28085637706784	13.7239364321127	8.70422694718725	35	17	15	18	12	12	37	12
-Adarb1	-0.0208372088988242	1.01574105327976	0.000220136486121625	0.988162223322517	0.991638607573432	0.682420434692774	14	2.25846011115444	0	1.06512390342257	0	1.48980486323549	0	0.741834401735823	1.45070449119788	6	0	2	0	2	0	2	2
-Zfp821	0.0194908209613808	0.438251386312134	0.000105716899237329	0.991796397330917	0.994410974828274	0.663299009834018	4	0	1.22515810725746	0	0	0	0	0.741834401735823	0	0	2	0	0	0	0	2	0
-Snord7	-0.00363412912961608	0.808396104681926	2.65747690626483e-05	0.99588686572452	0.997635569615169	1.23423123101037	10	2.25846011115444	0	0	0	0.744902431617745	0	0.370917200867911	1.45070449119788	6	0	0	0	1	0	1	2
-D330022K07Rik	8.83246306215062e-05	2.3059219632559	9.35429808279764e-07	0.999228305160102	0.999852120027929	0.587915295555401	56	4.89333024083463	0.612579053628728	9.05355317909186	1.07927773208001	4.46941458970647	7.73404698088987	1.11275160260373	2.90140898239575	13	1	17	2	6	10	3	4
-AA465934	-0.00551213095838715	1.05434721413031	3.43509380940077e-08	0.999852120027929	0.999852120027929	1.50064027311007	15	3.38769016673167	0	0	0	0.744902431617745	0	0.741834401735823	2.17605673679681	9	0	0	0	1	0	2	3
--- a/test-data/gentestdata.sh	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,9 +0,0 @@
-#!/bin/bash
-# generate test data for rgGSEA
-# ross lazarus June 2013
-# adjust gseajar_path !
-GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar
-python ../rgGSEA.py --input_tab "gsea_test_DGE.xls"  --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe"
---title "GSEA test" --builtin_gmt "gseatestdata.gmt"
-
-
--- a/test-data/test_bams2mx.xls	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,3243 +0,0 @@
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-Mir671	169	83	61	161	157	137	149	56
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-Mir676	188	146	100	364	136	143	148	138
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-Mir872	182	72	66	164	124	65	118	68
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-Mir874	77	45	46	39	66	36	87	43
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-Mir92-1	32320	7428	6690	16161	24182	24855	25904	7910
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-Mir98	542	407	354	1512	256	305	374	390
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-Mirlet7a-1	83	29	24	89	58	36	68	32
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-Mirlet7e	114	233	160	648	76	64	85	196
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-Mirlet7g	7561	2760	2343	10488	4736	2670	5047	2547
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-Myeov2	3	0	0	1	0	1	0	0
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-Scarna9	21	10	5	3	7	8	8	5
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-Snhg6	390	145	138	191	236	256	215	191
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-Snora28	25	3	5	5	7	13	8	5
-Snora2b	1	0	0	1	0	0	0	0
-Snora3	14	0	2	5	20	13	9	6
-Snora30	0	0	0	0	0	0	0	1
-Snora31	57	38	38	36	10	81	25	35
-Snora33	2	0	0	0	0	0	0	0
-Snora34	3	2	3	2	5	7	1	3
-Snora35	0	0	0	0	0	0	0	0
-Snora36b	76	79	63	156	53	38	70	98
-Snora41	90	32	20	85	38	25	85	19
-Snora43	1	1	0	3	2	0	0	0
-Snora44	159	76	73	81	82	107	88	58
-Snora47	4	1	1	2	1	2	1	1
-Snora52	24	4	4	13	16	23	13	8
-Snora5c	0	0	0	0	0	0	0	0
-Snora61	50	10	5	23	21	24	20	11
-Snora62	5	0	0	2	0	4	6	0
-Snora64	18	0	0	5	7	15	1	1
-Snora65	24	3	6	2	11	17	14	6
-Snora68	37	10	5	48	23	38	38	14
-Snora69	6	1	0	7	4	10	3	2
-Snora70	1	0	0	0	0	1	2	0
-Snora74a	7	2	5	11	0	2	2	7
-Snora75	4	0	0	3	0	2	0	1
-Snora78	5	2	0	5	0	4	2	4
-Snora7a	30	1	4	7	2	26	9	1
-Snora81	11	0	1	5	0	3	0	2
-Snord100	197	69	56	160	134	160	155	75
-Snord104	9351	886	720	2493	5610	7452	6017	929
-Snord11	150	8	14	47	59	53	54	14
-Snord110	443	266	226	297	338	266	373	262
-Snord111	23	6	4	13	14	10	6	6
-Snord116	0	0	0	0	0	0	0	0
-Snord116l1	0	0	0	0	0	0	0	0
-Snord116l2	0	0	0	0	0	0	0	0
-Snord118	0	0	0	0	0	0	0	0
-Snord12	534	115	104	602	380	354	356	121
-Snord123	10	16	11	42	2	6	6	9
-Snord14a	0	0	0	0	0	0	0	0
-Snord14c	0	0	0	0	0	0	0	0
-Snord14d	0	0	0	0	0	0	0	0
-Snord15a	20	4	3	5	10	9	4	2
-Snord15b	63	26	16	24	24	25	34	7
-Snord16a	136	5	5	29	24	130	30	9
-Snord17	326	116	78	148	228	85	269	110
-Snord19	366	148	118	333	314	465	338	121
-Snord1a	62	7	9	27	54	32	31	8
-Snord1b	14	3	4	4	2	5	3	2
-Snord1c	0	0	0	0	0	0	0	0
-Snord2	914	320	248	479	582	504	625	282
-Snord22	15	9	2	19	6	6	5	7
-Snord23	28	2	0	7	4	36	6	0
-Snord32a	505	164	116	501	394	378	418	170
-Snord33	649	189	195	310	598	709	602	190
-Snord34	34	4	3	13	21	15	19	1
-Snord35a	25	8	6	7	23	39	18	5
-Snord35b	5	3	1	0	1	2	3	0
-Snord37	134	39	32	89	113	74	80	44
-Snord38a	54	9	11	37	42	34	53	15
-Snord42a	61	93	100	30	31	20	35	68
-Snord42b	227	70	66	88	229	137	195	83
-Snord43	666	121	120	542	444	504	438	140
-Snord45b	620	98	72	138	458	244	499	70
-Snord45c	323	57	49	190	212	322	212	62
-Snord47	120	5	8	29	31	49	25	10
-Snord49a	232	84	51	256	151	120	145	75
-Snord49b	393	62	57	216	245	123	294	95
-Snord4a	324	50	39	155	196	165	191	31
-Snord52	4584	654	591	753	3716	3171	3941	649
-Snord53	65	21	11	57	50	23	51	12
-Snord55	462	124	101	450	309	255	250	119
-Snord57	1737	785	744	2031	1351	1445	1388	787
-Snord58b	46	85	81	3	44	29	49	57
-Snord61	46	16	8	75	31	32	12	13
-Snord64	24	22	19	48	13	19	35	17
-Snord65	29	74	90	8	17	19	15	68
-Snord66	884	267	281	340	693	807	696	244
-Snord67	39	4	1	17	18	13	11	5
-Snord68	1644	460	353	1060	1295	805	1310	376
-Snord69	440	157	105	401	341	407	333	117
-Snord7	6	1	0	1	0	0	0	2
-Snord70	23	6	11	16	10	6	14	6
-Snord71	93	20	20	38	51	26	64	29
-Snord72	159	21	24	57	102	123	101	16
-Snord73a	29	3	3	7	6	9	2	5
-Snord8	10	1	3	4	4	6	2	2
-Snord82	47	17	14	19	19	40	18	17
-Snord83b	48	22	12	35	22	34	32	16
-Snord85	3804	911	737	1422	3047	1133	3156	882
-Snord87	390	145	138	190	235	255	215	191
-Snord88a	96	69	73	77	62	101	59	52
-Snord88c	74	29	22	35	41	65	52	23
-Snord89	13	2	2	3	2	8	3	3
-Snord90	43	2	3	46	14	48	10	5
-Snord91a	363	40	29	81	256	358	285	25
-Snord92	23	9	5	17	8	29	7	8
-Snord93	45	54	50	161	29	34	29	33
-Snord95	708	217	195	326	483	675	444	210
-Snord96a	145	16	22	56	71	106	59	12
-Snord98	74	44	24	150	77	96	56	32
-Snord99	931	126	80	847	655	668	555	108
-Sox2ot	0	1	0	1	0	0	0	0
-Speer1-ps1	0	0	0	0	0	0	0	0
-Speer5-ps1	0	0	0	0	0	0	0	0
-Speer6-ps1	0	0	0	0	0	0	0	0
-Speer7-ps1	0	0	0	0	0	0	0	0
-Speer8-ps1	0	0	0	0	0	0	0	0
-Speer9-ps1	1	0	0	0	0	0	0	0
-Spn-ps	0	0	0	0	0	0	0	0
-Spns1	5	0	0	1	1	1	0	1
-Sprr2g	0	0	0	0	0	0	0	0
-Sprr2j-ps	0	0	0	0	0	0	0	0
-Sqrdl	35	1	0	1	4	58	3	0
-Sra1	7	0	0	0	2	4	6	0
-Srsf3	7	2	8	2	11	9	14	4
-Srsf7	1	1	0	0	0	5	0	1
-Srsf9	1	2	0	1	0	7	0	3
-St18	0	0	0	0	0	0	0	0
-St5	7	3	0	1	2	7	2	2
-St7l	8	1	1	1	3	12	4	2
-Stap2	10	0	2	2	1	6	4	0
-Stmn1-rs1	0	0	0	0	0	0	0	0
-Stoml1	0	0	0	0	0	0	0	0
-Stxbp3b	0	0	0	0	0	0	0	0
-Surf1	3	0	0	2	3	7	5	0
-Suv39h2	0	0	0	0	0	0	0	0
-Svip	1	0	2	0	0	1	0	0
-Syce2	6	0	0	0	0	19	1	0
-Sycp1-ps1	0	0	0	0	0	0	0	0
-Szrd1	40	4	0	2	16	40	4	2
-Taf1b	1	1	1	0	0	0	0	0
-Taf1d	357	53	56	156	174	341	230	54
-Tardbp	18	0	1	3	7	20	3	0
-Tbrg3	1	1	0	0	0	0	0	0
-Tcp10a	0	0	0	0	0	0	0	0
-Terc	22	1	0	0	18	10	15	0
-Thap6	2	0	2	2	1	1	0	0
-Tk2	1	2	0	2	1	1	3	0
-Tmem161b	0	0	0	0	2	2	4	0
-Tmem179b	8	0	0	0	0	8	0	0
-Tmem181b-ps	0	0	0	0	0	0	0	0
-Tmem181c-ps	0	0	0	0	0	0	0	0
-Tmem194b	0	0	1	0	1	0	0	0
-Tmem205	35	2	0	1	4	57	6	1
-Tmem41a	7	0	0	0	0	5	2	0
-Tmem51as1	1	0	0	0	0	2	0	0
-Tmem80	0	0	0	1	1	0	0	0
-Tor2a	2	0	0	0	0	11	3	0
-Tpsab1	0	0	0	0	0	0	0	0
-Trappc13	1	0	0	1	0	0	3	1
-Trim30e-ps1	0	0	0	0	0	0	0	0
-Trim52	0	0	0	0	0	0	0	0
-Trmt61b	11	0	0	0	2	5	0	0
-Trpc4	1	4	1	1	0	0	1	0
-Trpt1	2	0	0	0	0	2	0	0
-Trub2	0	0	0	0	0	0	0	0
-Tsix	0	0	0	0	0	4	0	0
-Tslp	0	0	0	1	0	0	0	0
-Tspy-ps	0	0	0	0	0	0	0	0
-Tsr2	0	0	1	0	0	2	0	0
-Tubb2a-ps2	0	0	0	0	0	0	0	0
-Tubgcp4	2	2	0	1	0	7	1	0
-Tug1	18	3	0	16	2	27	6	6
-Tyms	0	0	0	0	0	0	1	1
-Tyms-ps	0	0	0	0	0	0	0	0
-Tysnd1	2	0	0	0	1	7	3	0
-Tyw5	1	1	0	0	0	0	0	1
-U05342	20	0	1	3	3	24	3	0
-U90926	0	0	0	0	0	0	0	0
-Ube2w	3	1	2	1	1	3	1	0
-Ubl4	3	1	0	1	0	5	3	2
-Ubxn11	0	0	0	0	0	0	0	0
-Uchl1as	0	0	0	0	0	0	0	1
-Ufd1l	0	0	1	0	0	6	0	0
-Uqcc	5	0	2	1	0	8	1	1
-Vash2	0	0	0	0	0	0	0	0
-Vaultrc5	243	258	161	383	159	262	286	270
-Vax2os	0	0	0	0	0	0	0	0
-Vmn1r-ps79	0	0	0	0	0	0	0	0
-Vmn2r-ps11	0	0	0	1	0	0	0	0
-Vmn2r-ps129	0	0	0	0	0	0	0	0
-Vmn2r-ps159	0	0	0	0	0	0	0	0
-Vmn2r-ps54	0	0	1	0	0	0	0	0
-Vmn2r-ps60	0	0	0	0	0	0	0	0
-Vmn2r29	2	0	0	0	0	0	0	0
-Vps39	3	1	0	0	0	8	0	0
-Vsig8	194	50	35	100	99	74	109	50
-Wac	15	0	0	0	2	15	2	2
-Wbscr25	0	2	1	0	0	1	0	0
-Wdr13	2	2	1	3	0	1	0	3
-Wdr73	4	0	0	0	0	2	1	0
-Wiz	10	0	1	6	1	3	3	2
-Wls	1	1	0	3	2	0	0	1
-Wwp1	40	0	2	2	4	56	10	4
-Xist	0	0	0	0	0	8	0	0
-Yaf2	2	0	0	0	0	6	0	2
-Yars2	1	0	0	0	1	1	0	0
-Yipf2	1	0	0	1	0	10	0	0
-Ythdf3	16	4	1	4	3	19	3	2
-Zbtb24	1	0	0	1	0	0	0	0
-Zc3h14	11	1	0	0	1	2	2	2
-Zc3h7a	336	148	60	20	17	99	14	60
-Zf12	0	0	0	0	0	0	0	0
-Zfa-ps	0	0	0	0	0	0	0	0
-Zfhx2as	0	0	0	0	0	1	0	0
-Zfp133-ps	0	0	0	0	0	0	0	0
-Zfp207	5	0	2	0	0	9	1	2
-Zfp326	7	2	1	1	0	4	4	0
-Zfp389	0	0	0	0	0	0	0	0
-Zfp410	10	0	2	0	0	6	2	0
-Zfp414	1	0	0	0	0	4	0	0
-Zfp57	0	0	0	0	0	0	0	0
-Zfp572	0	0	0	0	0	0	0	0
-Zfp672	0	0	0	0	0	2	0	0
-Zfp783	0	0	0	0	0	0	0	0
-Zfp809	7	1	0	0	0	4	2	0
-Zfp821	0	0	0	2	2	0	0	0
-Zfp862	4	0	0	0	0	10	2	0
-Zim3	0	0	0	0	0	0	0	0
-Zmynd8	3	5	4	4	3	8	2	1
-Znf41-ps	0	0	0	0	0	0	0	0
-Zp4-ps	0	0	0	0	0	0	0	0
-Zscan4a	0	0	0	0	0	0	0	0
-Zxda	0	0	0	0	0	0	0	0
--- a/tool_dependencies.xml	Thu Jan 01 21:53:07 2015 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,79 +0,0 @@
-<?xml version="1.0"?>
-<tool_dependency>
-    <package name="R_3_1_1" version="3.1.1">
-        <repository changeset_revision="63cdb9b2234c" name="package_r_3_1_1" owner="fubar" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
-    </package>
-    <package name="graphicsmagick" version="1.3.18">
-        <repository changeset_revision="bff3f66adff2" name="package_graphicsmagick_1_3" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
-    </package>
-    <package name="ghostscript" version="9.10">
-        <repository changeset_revision="9345d2740f0c" name="package_ghostscript_9_10" owner="devteam" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
-    </package>
-
-    <package name="biocbasics" version="2.14">
-        <install version="1.0">
-            <actions>
-                <action type="setup_r_environment">
-                    <repository changeset_revision="63cdb9b2234c" name="package_r_3_1_1" owner="fubar" toolshed="https://testtoolshed.g2.bx.psu.edu">
-                        <package name="R_3_1_1" version="3.1.1" />
-                    </repository>
-
-
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/Rcpp_0.11.3.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/RcppArmadillo_0.4.450.1.0.tar.gz?raw=true</package>
-
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/stringr_0.6.2.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/locfit_1.5-9.1.tar.gz?raw=true</package>
-
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/xtable_1.7-4.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/BiocGenerics_0.10.0.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/IRanges_1.22.10.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/GenomeInfoDb_1.0.2.tar.gz?raw=true</package>
-
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/Biobase_2.24.0.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/XVector_0.4.0.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/GenomicRanges_1.16.4.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/RColorBrewer_1.0-5.tar.gz?raw=true</package>
-                    <!--
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/getopt_1.20.0.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/digest_0.6.4.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/labeling_0.3.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/colorspace_1.2-4.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/munsell_0.4.2.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/dichromat_2.0-0.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/reshape2_1.4.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/scales_0.2.4.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gtable_0.1.2.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/plyr_1.8.1.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/statmod_1.4.20.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/proto_0.3-10.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/rjson_0.2.14.tar.gz?raw=true</package>
-                     -->
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/DBI_0.3.1.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/RSQLite_0.11.4.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/XML_3.98-1.1.tar.gz?raw=true</package>
-
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/AnnotationDbi_1.26.1.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/annotate_1.42.1.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/geneplotter_1.42.0.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/genefilter_1.46.1.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/limma_3.20.9.tar.gz?raw=true</package>
-
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/DESeq2_1.4.5.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/edgeR_3.6.8.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/ggplot2_1.0.0.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/bitops_1.0-6.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gtools_3.4.1.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gdata_2.13.3.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/caTools_1.17.1.tar.gz?raw=true</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gplots_2.14.2.tar.gz?raw=true</package>
-                </action>
-            </actions>
-        </install>
-        <readme>
-        Differential gene expression analysis
-        You may need libxml2-dev for XML to compile
-        Ubuntu has a bug with libgfortran. To fix that create a symlink like: sudo ln -s /usr/lib/x86_64-linux-gnu/libgfortran.so.3 /usr/lib/x86_64-linux-gnu/libgfortran.so
-        </readme>
-    </package>
-</tool_dependency>