Mercurial > repos > drosofff > parse_srbowtie_alignment

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/smRtools.py	Mon Jun 23 05:25:28 2014 -0400
@@ -0,0 +1,653 @@
+#!/usr/bin/python
+# version 1 7-5-2012 unification of the SmRNAwindow class
+
+import sys, subprocess
+from collections import defaultdict, OrderedDict
+from numpy import mean, median, std
+from scipy import stats
+
+def get_fasta (index="/home/galaxy/galaxy-dist/bowtie/5.37_Dmel/5.37_Dmel"):
+  '''This function will return a dictionary containing fasta identifiers as keys and the
+  sequence as values. Index must be the path to a fasta file.'''
+  p = subprocess.Popen(args=["bowtie-inspect","-a", "0", index], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) # bowtie-inspect outputs sequences on single lines
+  outputlines = p.stdout.readlines()
+  p.wait()
+  item_dic = {}
+  for line in outputlines:
+    if (line[0] == ">"):
+      try:
+        item_dic[current_item] = "".join(stringlist) # to dump the sequence of the previous item - try because of the keyerror of the first item
+      except: pass
+      current_item = line[1:].rstrip().split()[0] #take the first word before space because bowtie splits headers !
+      item_dic[current_item] = ""
+      stringlist=[]
+    else:
+      stringlist.append(line.rstrip() )
+  item_dic[current_item] = "".join(stringlist) # for the last item
+  return item_dic
+
+def get_fasta_headers (index):
+  p = subprocess.Popen(args=["bowtie-inspect","-n", index], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) # bowtie-inspect outputs sequences on single lines
+  outputlines = p.stdout.readlines()
+  p.wait()
+  item_dic = {}
+  for line in outputlines:
+    header = line.rstrip().split()[0] #take the first word before space because bowtie splits headers !
+    item_dic[header] = 1
+  return item_dic
+
+
+def get_file_sample (file, numberoflines):
+  '''import random to use this function'''
+  F=open(file)
+  fullfile = F.read().splitlines()
+  F.close()
+  if len(fullfile) < numberoflines:
+    return "sample size exceeds file size"
+  return random.sample(fullfile, numberoflines)
+
+def get_fasta_from_history (file):
+  F = open (file, "r")
+  item_dic = {}
+  for line in F:
+    if (line[0] == ">"):
+      try:
+        item_dic[current_item] = "".join(stringlist) # to dump the sequence of the previous item - try because of the keyerror of the first item
+      except: pass
+      current_item = line[1:-1].split()[0] #take the first word before space because bowtie splits headers !
+      item_dic[current_item] = ""
+      stringlist=[]
+    else:
+      stringlist.append(line[:-1])
+  item_dic[current_item] = "".join(stringlist) # for the last item
+  return item_dic
+
+def antipara (sequence):
+    antidict = {"A":"T", "T":"A", "G":"C", "C":"G", "N":"N"}
+    revseq = sequence[::-1]
+    return "".join([antidict[i] for i in revseq])
+
+def RNAtranslate (sequence):
+    return "".join([i if i in "AGCN" else "U" for i in sequence])
+def DNAtranslate (sequence):
+    return "".join([i if i in "AGCN" else "T" for i in sequence])
+
+def RNAfold (sequence_list):
+  thestring= "\n".join(sequence_list)
+  p = subprocess.Popen(args=["RNAfold","--noPS"], stdin= subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+  output=p.communicate(thestring)[0]
+  p.wait()
+  output=output.split("\n")
+  if not output[-1]: output = output[:-1] # nasty patch to remove last empty line
+  buffer=[]
+  for line in output:
+    if line[0] in ["N","A","T","U","G","C"]:
+      buffer.append(DNAtranslate(line))
+    if line[0] in ["(",".",")"]:
+      fields=line.split("(")
+      energy= fields[-1]
+      energy = energy[:-1] # remove the ) parenthesis
+      energy=float(energy)
+      buffer.append(str(energy))
+  return dict(zip(buffer[::2], buffer[1::2]))
+
+def extractsubinstance (start, end, instance):
+  ''' Testing whether this can be an function external to the class to save memory'''
+  subinstance = SmRNAwindow (instance.gene, instance.sequence[start-1:end], start)
+  subinstance.gene = "%s %s %s" % (subinstance.gene, subinstance.windowoffset, subinstance.windowoffset + subinstance.size - 1)
+  upcoordinate = [i for i in range(start,end+1) if instance.readDict[i] ]
+  downcoordinate = [-i for i in range(start,end+1) if instance.readDict[-i] ]
+  for i in upcoordinate:
+    subinstance.readDict[i]=instance.readDict[i]
+  for i in downcoordinate:
+    subinstance.readDict[i]=instance.readDict[i]
+  return subinstance
+
+class HandleSmRNAwindows:
+  def __init__(self, alignmentFile="~", alignmentFileFormat="tabular", genomeRefFile="~", genomeRefFormat="bowtieIndex", biosample="undetermined"):
+    self.biosample = biosample
+    self.alignmentFile = alignmentFile
+    self.alignmentFileFormat = alignmentFileFormat # can be "tabular" or "sam"
+    self.genomeRefFile = genomeRefFile
+    self.genomeRefFormat = genomeRefFormat # can be "bowtieIndex" or "fastaSource"
+    self.alignedReads = 0
+    self.instanceDict = {}
+    if genomeRefFormat == "bowtieIndex":
+      self.itemDict = get_fasta (genomeRefFile)
+    elif genomeRefFormat == "fastaSource":
+      self.itemDict = get_fasta_from_history (genomeRefFile)
+    for item in self.itemDict:
+      self.instanceDict[item] = SmRNAwindow(item, sequence=self.itemDict[item], windowoffset=1, biosample=self.biosample) # create as many instances as there is items
+    self.readfile()
+
+  def readfile (self) :
+    if self.alignmentFileFormat == "tabular":
+      F = open (self.alignmentFile, "r")
+      for line in F:
+        fields = line.split()
+        polarity = fields[1]
+        gene = fields[2]
+        offset = int(fields[3])
+        size = len (fields[4])
+        self.instanceDict[gene].addread (polarity, offset+1, size) # to correct to 1-based coordinates of SmRNAwindow
+        self.alignedReads += 1
+      F.close()
+    elif self.alignmentFileFormat == "sam":
+      F = open (self.alignmentFile, "r")
+      dict = {"0":"+", "16":"-"}
+      for line in F:
+        if line[0]=='@':
+            continue
+        fields = line.split()
+        if fields[2] == "*": continue
+        polarity = dict[fields[1]]
+        gene = fields[2]
+        offset = int(fields[3])
+        size = len (fields[9])
+        self.instanceDict[gene].addread (polarity, offset, size) # sam format is already 1-based coordinates
+        self.alignedReads += 1
+      F.close()
+    elif self.alignmentFileFormat == "bam":
+      import pysam
+      samfile = pysam.Samfile(self.alignmentFile)
+      for read in samfile:
+        if read.tid == -1:
+          continue # filter out unaligned reads
+        if read.is_reverse:
+          polarity="-"
+        else:
+          polarity="+"
+        gene = samfile.getrname(read.tid)
+        offset = read.pos
+        size = read.qlen
+        self.instanceDict[gene].addread (polarity, offset+1, size) # pysam converts coordinates to 0-based (https://media.readthedocs.org/pdf/pysam/latest/pysam.pdf)
+        self.alignedReads += 1
+      return
+
+  def CountFeatures (self, GFF3="path/to/file"):
+    featureDict = defaultdict(int)
+    F  = open (GFF3, "r")
+    for line in F:
+      if line[0] ==  "#": continue
+      fields = line[:-1].split()
+      chrom, feature, leftcoord, rightcoord, polarity = fields[0], fields[2], fields[3], fields[4], fields[6]
+      featureDict[feature] += self.instanceDict[chrom].readcount(upstream_coord=int(leftcoord), downstream_coord=int(rightcoord), polarity="both", method="destructive")
+    F.close()
+    return featureDict
+
+class SmRNAwindow:
+
+  def __init__(self, gene, sequence="ATGC", windowoffset=1, biosample="Undetermined"):
+    self.biosample = biosample
+    self.sequence = sequence
+    self.gene = gene
+    self.windowoffset = windowoffset
+    self.size = len(sequence)
+    self.readDict = defaultdict(list) # with a {+/-offset:[size1, size2, ...], ...}
+    self.matchedreadsUp = 0
+    self.matchedreadsDown = 0
+
+  def addread (self, polarity, offset, size):
+    '''ATTENTION ATTENTION ATTENTION'''
+    ''' We removed the conversion from 0 to 1 based offset, as we do this now during readparsing.'''
+    if polarity == "+":
+      self.readDict[offset].append(size)
+      self.matchedreadsUp += 1
+    else:
+      self.readDict[-(offset + size -1)].append(size)
+      self.matchedreadsDown += 1
+    return
+
+  def barycenter (self, upstream_coord=None, downstream_coord=None):
+    '''refactored 24-12-2013 to save memory and introduce offset filtering see readcount method for further discussion on that
+    In this version, attempt to replace the dictionary structure by a list of tupple to save memory too'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    window_size = downstream_coord - upstream_coord +1
+    def weigthAverage (TuppleList):
+      weightSum = 0
+      PonderWeightSum = 0
+      for tuple in TuppleList:
+        PonderWeightSum += tuple[0] * tuple[1]
+        weightSum += tuple[1]
+      if weightSum > 0:
+        return PonderWeightSum / float(weightSum)
+      else:
+        return 0
+    forwardTuppleList = [(k, len(self.readDict[k])) for k in self.readDict.keys() if (k > 0 and abs(k) >= upstream_coord and abs(k) <= downstream_coord)] # both forward and in the proper offset window
+    reverseTuppleList = [(-k, len(self.readDict[k])) for k in self.readDict.keys() if (k < 0 and abs(k) >= upstream_coord and abs(k) <= downstream_coord)] # both reverse and in the proper offset window
+    Fbarycenter = (weigthAverage (forwardTuppleList) - upstream_coord) / window_size
+    Rbarycenter = (weigthAverage (reverseTuppleList) - upstream_coord) / window_size
+    return Fbarycenter, Rbarycenter
+
+  def correlation_mapper (self, reference, window_size):
+    '''to map correlation with a sliding window 26-2-2013'''
+    if window_size > self.size:
+      return []
+    F=open(reference, "r")
+    reference_forward = []
+    reference_reverse = []
+    for line in F:
+      fields=line.split()
+      reference_forward.append(int(float(fields[1])))
+      reference_reverse.append(int(float(fields[2])))
+    F.close()
+    local_object_forward=[]
+    local_object_reverse=[]
+    ## Dict to list for the local object
+    for i in range(1, self.size+1):
+      local_object_forward.append(len(self.readDict[i]))
+      local_object_reverse.append(len(self.readDict[-i]))
+    ## start compiling results by slides
+    results=[]
+    for coordinate in range(self.size - window_size):
+      local_forward=local_object_forward[coordinate:coordinate + window_size]
+      local_reverse=local_object_reverse[coordinate:coordinate + window_size]
+      if sum(local_forward) == 0 or sum(local_reverse) == 0:
+        continue
+      try:
+        reference_to_local_cor_forward = stats.spearmanr(local_forward, reference_forward)
+        reference_to_local_cor_reverse = stats.spearmanr(local_reverse, reference_reverse)
+        if (reference_to_local_cor_forward[0] > 0.2 or  reference_to_local_cor_reverse[0]>0.2):
+          results.append([coordinate+1, reference_to_local_cor_forward[0], reference_to_local_cor_reverse[0]])
+      except:
+        pass
+    return results
+
+  def readcount (self, size_inf=0, size_sup=1000, upstream_coord=None, downstream_coord=None, polarity="both", method="conservative"):
+    '''refactored 24-12-2013 to save memory and introduce offset filtering
+    take a look at the defaut parameters that cannot be defined relatively to the instance are they are defined before instanciation
+    the trick is to pass None and then test
+    polarity parameter can take "both", "forward" or "reverse" as value'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    if upstream_coord == 1 and downstream_coord == self.windowoffset+self.size-1 and polarity == "both":
+      return self.matchedreadsUp +  self.matchedreadsDown
+    if upstream_coord == 1 and downstream_coord == self.windowoffset+self.size-1 and polarity == "forward":
+      return self.matchedreadsUp
+    if upstream_coord == 1 and downstream_coord == self.windowoffset+self.size-1 and polarity == "reverse":
+      return self.matchedreadsDown
+    n=0
+    if polarity == "both":
+      for offset in xrange(upstream_coord, downstream_coord+1):
+        if self.readDict.has_key(offset):
+          for read in self.readDict[offset]:
+            if (read>=size_inf and read<= size_sup):
+              n += 1
+          if method != "conservative":
+            del self.readDict[offset] ## Carefull ! precludes re-use on the self.readDict dictionary !!!!!! TEST
+        if self.readDict.has_key(-offset):
+          for read in self.readDict[-offset]:
+            if (read>=size_inf and read<= size_sup):
+              n += 1
+          if method != "conservative":
+            del self.readDict[-offset]
+      return n
+    elif polarity == "forward":
+      for offset in xrange(upstream_coord, downstream_coord+1):
+        if self.readDict.has_key(offset):
+          for read in self.readDict[offset]:
+            if (read>=size_inf and read<= size_sup):
+              n += 1
+      return n
+    elif polarity == "reverse":
+      for offset in xrange(upstream_coord, downstream_coord+1):
+        if self.readDict.has_key(-offset):
+          for read in self.readDict[-offset]:
+            if (read>=size_inf and read<= size_sup):
+              n += 1
+      return n
+
+  def readsizes (self):
+    '''return a dictionary of number of reads by size (the keys)'''
+    dicsize = {}
+    for offset in self.readDict:
+      for size in self.readDict[offset]:
+        dicsize[size] = dicsize.get(size, 0) + 1
+    for offset in range (min(dicsize.keys()), max(dicsize.keys())+1):
+      dicsize[size] = dicsize.get(size, 0) # to fill offsets with null values
+    return dicsize
+
+  def statsizes (self, upstream_coord=None, downstream_coord=None):
+    ''' migration to memory saving by specifying possible subcoordinates
+    see the readcount method for further discussion'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    L = []
+    for offset in self.readDict:
+      if (abs(offset) < upstream_coord or abs(offset) > downstream_coord): continue
+      for size in self.readDict[offset]:
+        L.append(size)
+    meansize = mean(L)
+    stdv = std(L)
+    mediansize = median(L)
+    return meansize, mediansize, stdv
+
+  def foldEnergy (self, upstream_coord=None, downstream_coord=None):
+    ''' migration to memory saving by specifying possible subcoordinates
+    see the readcount method for further discussion'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    Energy = RNAfold ([self.sequence[upstream_coord-1:downstream_coord] ])
+    return float(Energy[self.sequence[upstream_coord-1:downstream_coord]])
+
+  def Ufreq (self, size_scope, upstream_coord=None, downstream_coord=None):
+    ''' migration to memory saving by specifying possible subcoordinates
+    see the readcount method for further discussion. size_scope must be an interable'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    freqDic = {"A":0,"T":0,"G":0,"C":0, "N":0}
+    convertDic = {"A":"T","T":"A","G":"C","C":"G","N":"N"}
+    for offset in self.readDict:
+      if (abs(offset) < upstream_coord or abs(offset) > downstream_coord): continue
+      for size in self.readDict[offset]:
+        if size in size_scope:
+          startbase = self.sequence[abs(offset)-self.windowoffset]
+          if offset < 0:
+            startbase = convertDic[startbase]
+          freqDic[startbase] += 1
+    base_sum = float ( sum( freqDic.values()) )
+    if base_sum == 0:
+      return "."
+    else:
+      return freqDic["T"] / base_sum * 100
+
+  def Ufreq_stranded (self, size_scope, upstream_coord=None, downstream_coord=None):
+    ''' migration to memory saving by specifying possible subcoordinates
+    see the readcount method for further discussion. size_scope must be an interable
+    This method is similar to the Ufreq method but take strandness into account'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    freqDic = {"Afor":0,"Tfor":0,"Gfor":0,"Cfor":0, "Nfor":0,"Arev":0,"Trev":0,"Grev":0,"Crev":0, "Nrev":0}
+    convertDic = {"A":"T","T":"A","G":"C","C":"G","N":"N"}
+    for offset in self.readDict:
+      if (abs(offset) < upstream_coord or abs(offset) > downstream_coord): continue
+      for size in self.readDict[offset]:
+        if size in size_scope:
+          startbase = self.sequence[abs(offset)-self.windowoffset]
+          if offset < 0:
+            startbase = convertDic[startbase]
+            freqDic[startbase+"rev"] += 1
+          else:
+            freqDic[startbase+"for"] += 1
+    forward_sum = float ( freqDic["Afor"]+freqDic["Tfor"]+freqDic["Gfor"]+freqDic["Cfor"]+freqDic["Nfor"])
+    reverse_sum = float ( freqDic["Arev"]+freqDic["Trev"]+freqDic["Grev"]+freqDic["Crev"]+freqDic["Nrev"])
+    if forward_sum == 0 and reverse_sum == 0:
+      return ". | ."
+    elif reverse_sum == 0:
+      return "%s | ." % (freqDic["Tfor"] / forward_sum * 100)
+    elif forward_sum == 0:
+      return ". | %s" % (freqDic["Trev"] / reverse_sum * 100)
+    else:
+      return "%s | %s" % (freqDic["Tfor"] / forward_sum * 100, freqDic["Trev"] / reverse_sum * 100)
+
+
+  def readplot (self):
+    readmap = {}
+    for offset in self.readDict.keys():
+      readmap[abs(offset)] = ( len(self.readDict[-abs(offset)]) , len(self.readDict[abs(offset)]) )
+    mylist = []
+    for offset in sorted(readmap):
+      if readmap[offset][1] != 0:
+        mylist.append("%s\t%s\t%s\t%s" % (self.gene, offset, readmap[offset][1], "F") )
+      if readmap[offset][0] != 0:
+        mylist.append("%s\t%s\t%s\t%s" % (self.gene, offset, -readmap[offset][0], "R") )
+    return mylist
+
+  def readcoverage (self, upstream_coord=None, downstream_coord=None, windowName=None):
+    '''Use by MirParser tool'''
+    upstream_coord = upstream_coord or 1
+    downstream_coord = downstream_coord or self.size
+    windowName = windowName or "%s_%s_%s" % (self.gene, upstream_coord, downstream_coord)
+    forORrev_coverage = dict ([(i,0) for i in xrange(1, downstream_coord-upstream_coord+1)])
+    totalforward = self.readcount(upstream_coord=upstream_coord, downstream_coord=downstream_coord, polarity="forward")
+    totalreverse = self.readcount(upstream_coord=upstream_coord, downstream_coord=downstream_coord, polarity="reverse")
+    if totalforward > totalreverse:
+      majorcoverage = "forward"
+      for offset in self.readDict.keys():
+        if (offset > 0) and ((offset-upstream_coord+1) in forORrev_coverage.keys() ):
+          for read in self.readDict[offset]:
+            for i in xrange(read):
+              try:
+                forORrev_coverage[offset-upstream_coord+1+i] += 1
+              except KeyError:
+                continue # a sense read may span over the downstream limit
+    else:
+      majorcoverage = "reverse"
+      for offset in self.readDict.keys():
+        if (offset < 0) and (-offset-upstream_coord+1 in forORrev_coverage.keys() ):
+          for read in self.readDict[offset]:
+            for i in xrange(read):
+              try:
+                forORrev_coverage[-offset-upstream_coord-i] += 1 ## positive coordinates in the instance, with + for forward coverage and - for reverse coverage
+              except KeyError:
+                continue # an antisense read may span over the upstream limit
+    output_list = []
+    maximum = max (forORrev_coverage.values()) or 1
+    for n in sorted (forORrev_coverage):
+      output_list.append("%s\t%s\t%s\t%s\t%s\t%s\t%s" % (self.biosample, windowName, n, float(n)/(downstream_coord-upstream_coord+1), forORrev_coverage[n], float(forORrev_coverage[n])/maximum, majorcoverage))
+    return "\n".join(output_list)
+
+
+  def signature (self, minquery, maxquery, mintarget, maxtarget, scope, zscore="no", upstream_coord=None, downstream_coord=None):
+    ''' migration to memory saving by specifying possible subcoordinates
+    see the readcount method for further discussion
+    scope must be a python iterable; scope define the *relative* offset range to be computed'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    query_range = range (minquery, maxquery+1)
+    target_range = range (mintarget, maxtarget+1)
+    Query_table = {}
+    Target_table = {}
+    frequency_table = dict ([(i, 0) for i in scope])
+    for offset in self.readDict:
+      if (abs(offset) < upstream_coord or abs(offset) > downstream_coord): continue
+      for size in self.readDict[offset]:
+        if size in query_range:
+          Query_table[offset] = Query_table.get(offset, 0) + 1
+        if size in target_range:
+          Target_table[offset] = Target_table.get(offset, 0) + 1
+    for offset in Query_table:
+      for i in scope:
+        frequency_table[i] += min(Query_table[offset], Target_table.get(-offset -i +1, 0))
+    if minquery==mintarget and maxquery==maxtarget: ## added to incorporate the division by 2 in the method (26/11/2013), see signature_options.py and lattice_signature.py
+      frequency_table = dict([(i,frequency_table[i]/2) for i in frequency_table])
+    if zscore == "yes":
+      z_mean = mean(frequency_table.values() )
+      z_std = std(frequency_table.values() )
+      if z_std == 0:
+        frequency_table = dict([(i,0) for i in frequency_table] )
+      else:
+        frequency_table = dict([(i, (frequency_table[i]- z_mean)/z_std) for i in frequency_table] )
+    return frequency_table
+
+  def hannon_signature (self, minquery, maxquery, mintarget, maxtarget, scope, upstream_coord=None, downstream_coord=None):
+    ''' migration to memory saving by specifying possible subcoordinates see the readcount method for further discussion
+    note that scope must be an iterable (a list or a tuple), which specifies the relative offsets that will be computed'''
+    upstream_coord = upstream_coord or self.windowoffset
+    downstream_coord = downstream_coord or self.windowoffset+self.size-1
+    query_range = range (minquery, maxquery+1)
+    target_range = range (mintarget, maxtarget+1)
+    Query_table = {}
+    Target_table = {}
+    Total_Query_Numb = 0
+    general_frequency_table = dict ([(i,0) for i in scope])
+    ## filtering the appropriate reads for the study
+    for offset in self.readDict:
+      if (abs(offset) < upstream_coord or abs(offset) > downstream_coord): continue
+      for size in self.readDict[offset]:
+        if size in query_range:
+          Query_table[offset] = Query_table.get(offset, 0) + 1
+          Total_Query_Numb += 1
+        if size in target_range:
+          Target_table[offset] = Target_table.get(offset, 0) + 1
+    for offset in Query_table:
+      frequency_table = dict ([(i,0) for i in scope])
+      number_of_targets = 0
+      for i in scope:
+        frequency_table[i] += Query_table[offset] *  Target_table.get(-offset -i +1, 0)
+        number_of_targets += Target_table.get(-offset -i +1, 0)
+      for i in scope:
+        try:
+          general_frequency_table[i] += (1. / number_of_targets / Total_Query_Numb) * frequency_table[i]
+        except ZeroDivisionError :
+          continue
+    return general_frequency_table
+
+  def phasing (self, size_range, scope):
+    ''' to calculate autocorelation like signal - scope must be an python iterable'''
+    read_table = {}
+    total_read_number = 0
+    general_frequency_table = dict ([(i, 0) for i in scope])
+    ## read input filtering
+    for offset in self.readDict:
+      for size in self.readDict[offset]:
+        if size in size_range:
+          read_table[offset] = read_table.get(offset, 0) + 1
+          total_read_number += 1
+    ## per offset read phasing computing
+    for offset in read_table:
+      frequency_table = dict ([(i, 0) for i in scope]) # local frequency table
+      number_of_targets = 0
+      for i in scope:
+        if offset > 0:
+          frequency_table[i] += read_table[offset] *  read_table.get(offset + i, 0)
+          number_of_targets += read_table.get(offset + i, 0)
+        else:
+          frequency_table[i] += read_table[offset] *  read_table.get(offset - i, 0)
+          number_of_targets += read_table.get(offset - i, 0)
+    ## inclusion of local frequency table in the general frequency table (all offsets average)
+      for i in scope:
+        try:
+          general_frequency_table[i] += (1. / number_of_targets / total_read_number) * frequency_table[i]
+        except ZeroDivisionError :
+          continue
+    return general_frequency_table
+
+
+
+  def z_signature (self, minquery, maxquery, mintarget, maxtarget, scope):
+    '''Must do: from numpy import mean, std, to use this method; scope must be a python iterable and defines the relative offsets to compute'''
+    frequency_table = self.signature (minquery, maxquery, mintarget, maxtarget, scope)
+    z_table = {}
+    frequency_list = [frequency_table[i] for i in sorted (frequency_table)]
+    if std(frequency_list):
+      meanlist = mean(frequency_list)
+      stdlist = std(frequency_list)
+      z_list = [(i-meanlist)/stdlist for i in frequency_list]
+      return dict (zip (sorted(frequency_table), z_list) )
+    else:
+      return dict (zip (sorted(frequency_table), [0 for i in frequency_table]) )
+
+  def percent_signature (self, minquery, maxquery, mintarget, maxtarget, scope):
+    frequency_table = self.signature (minquery, maxquery, mintarget, maxtarget, scope)
+    total = float(sum ([self.readsizes().get(i,0) for i in set(range(minquery,maxquery)+range(mintarget,maxtarget))]) )
+    if total == 0:
+      return dict( [(i,0) for i in scope])
+    return dict( [(i, frequency_table[i]/total*100) for i in scope])
+
+  def pairer (self, overlap, minquery, maxquery, mintarget, maxtarget):
+    queryhash = defaultdict(list)
+    targethash = defaultdict(list)
+    query_range = range (int(minquery), int(maxquery)+1)
+    target_range = range (int(mintarget), int(maxtarget)+1)
+    paired_sequences = []
+    for offset in self.readDict: # selection of data
+      for size in self.readDict[offset]:
+        if size in query_range:
+          queryhash[offset].append(size)
+        if size in target_range:
+          targethash[offset].append(size)
+    for offset in queryhash:
+      if offset >= 0: matched_offset = -offset - overlap + 1
+      else: matched_offset = -offset - overlap + 1
+      if targethash[matched_offset]:
+        paired = min ( len(queryhash[offset]), len(targethash[matched_offset]) )
+        if offset >= 0:
+          for i in range (paired):
+            paired_sequences.append("+%s" % RNAtranslate ( self.sequence[offset:offset+queryhash[offset][i]]) )
+            paired_sequences.append("-%s" % RNAtranslate (antipara (self.sequence[-matched_offset-targethash[matched_offset][i]+1:-matched_offset+1]) ) )
+        if offset < 0:
+          for i in range (paired):
+            paired_sequences.append("-%s" % RNAtranslate (antipara (self.sequence[-offset-queryhash[offset][i]+1:-offset+1]) ) )
+            paired_sequences.append("+%s" % RNAtranslate (self.sequence[matched_offset:matched_offset+targethash[matched_offset][i]] ) )
+    return paired_sequences
+
+  def pairable (self, overlap, minquery, maxquery, mintarget, maxtarget):
+    queryhash = defaultdict(list)
+    targethash = defaultdict(list)
+    query_range = range (int(minquery), int(maxquery)+1)
+    target_range = range (int(mintarget), int(maxtarget)+1)
+    paired_sequences = []
+
+    for offset in self.readDict: # selection of data
+      for size in self.readDict[offset]:
+        if size in query_range:
+          queryhash[offset].append(size)
+        if size in target_range:
+          targethash[offset].append(size)
+
+    for offset in queryhash:
+      matched_offset = -offset - overlap + 1
+      if targethash[matched_offset]:
+        if offset >= 0:
+          for i in queryhash[offset]:
+            paired_sequences.append("+%s" % RNAtranslate (self.sequence[offset:offset+i]) )
+          for i in targethash[matched_offset]:
+            paired_sequences.append( "-%s" % RNAtranslate (antipara (self.sequence[-matched_offset-i+1:-matched_offset+1]) ) )
+        if offset < 0:
+          for i in queryhash[offset]:
+            paired_sequences.append("-%s" %  RNAtranslate (antipara (self.sequence[-offset-i+1:-offset+1]) ) )
+          for i in targethash[matched_offset]:
+            paired_sequences.append("+%s" %  RNAtranslate (self.sequence[matched_offset:matched_offset+i] ) )
+    return paired_sequences
+
+  def newpairable_bowtie (self, overlap, minquery, maxquery, mintarget, maxtarget):
+    ''' revision of pairable on 3-12-2012, with focus on the offset shift problem (bowtie is 1-based cooordinates whereas python strings are 0-based coordinates'''
+    queryhash = defaultdict(list)
+    targethash = defaultdict(list)
+    query_range = range (int(minquery), int(maxquery)+1)
+    target_range = range (int(mintarget), int(maxtarget)+1)
+    bowtie_output = []
+
+    for offset in self.readDict: # selection of data
+      for size in self.readDict[offset]:
+        if size in query_range:
+          queryhash[offset].append(size)
+        if size in target_range:
+          targethash[offset].append(size)
+    counter = 0
+    for offset in queryhash:
+      matched_offset = -offset - overlap + 1
+      if targethash[matched_offset]:
+        if offset >= 0:
+          for i in queryhash[offset]:
+            counter += 1
+            bowtie_output.append("%s\t%s\t%s\t%s\t%s" % (counter, "+", self.gene, offset-1, self.sequence[offset-1:offset-1+i]) ) # attention a la base 1-0 de l'offset
+        if offset < 0:
+          for i in queryhash[offset]:
+            counter += 1
+            bowtie_output.append("%s\t%s\t%s\t%s\t%s" % (counter, "-", self.gene, -offset-i, self.sequence[-offset-i:-offset])) # attention a la base 1-0 de l'offset
+    return bowtie_output
+
+
+def __main__(bowtie_index_path, bowtie_output_path):
+  sequenceDic = get_fasta (bowtie_index_path)
+  objDic = {}
+  F = open (bowtie_output_path, "r") # F is the bowtie output taken as input
+  for line in F:
+    fields = line.split()
+    polarity = fields[1]
+    gene = fields[2]
+    offset = int(fields[3])
+    size = len (fields[4])
+    try:
+      objDic[gene].addread (polarity, offset, size)
+    except KeyError:
+      objDic[gene] = SmRNAwindow(gene, sequenceDic[gene])
+      objDic[gene].addread (polarity, offset, size)
+  F.close()
+  for gene in objDic:
+    print gene, objDic[gene].pairer(19,19,23,19,23)
+
+if __name__ == "__main__" : __main__(sys.argv[1], sys.argv[2])