# HG changeset patch
# User glogobyte
# Date 1603871374 0
# Node ID d77dace80d5ab50f55d2a2c3b9f1fbea529a7085
# Parent  a09d238416babf8a613584709fa5312bf427868f
Deleted selected files

diff -r a09d238416ba -r d77dace80d5a viz.xml
--- a/viz.xml	Wed Oct 28 07:42:50 2020 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,54 +0,0 @@
-<tool id="viz_tool" name="Viz_tool: After Deseq2" version="0.1.0">
-  <description>for each sequence in a file</description>
-  <requirements>
-    <requirement type="package" version="1.7">fpdf</requirement>
-    <requirement type="package" version="3.7.4">python</requirement>
-    <requirement type="package" version="1.17.3">numpy</requirement>
-    <requirement type="package" version="3.1.2">matplotlib</requirement>
-    <requirement type="package" version="1.0.3">pandas</requirement>
-  </requirements>
-  <command>
-    #if $stats.choice == "1":
-      python $__tool_directory__/viz_ultra.py -in $input_file -p_value "$stats.pvalue" -fc $log2fc -top $top_mirna -tool_dir $__tool_directory__ -statistic "$stats.choice" -diff_tool "$tool"
-    #else:
-      python $__tool_directory__/viz_ultra.py -in $input_file -p_value "$stats.padj" -fc $log2fc -top $top_mirna -tool_dir $__tool_directory__ -statistic "$stats.choice" -diff_tool "$tool"
-    #end if
-  </command>
-  <inputs>
-    <param name="tool" type="select" label="File comes from" help="Choose the tool which generates the input file.">
-      <option value="1" selected="true">Deseq2</option>
-      <option value="2">EdgeR</option>
-    </param>
-    <param name="input_file" type="data" format="tabular" label="Input file" help="File from Deseq2 or EdgeR"/>
-    <param name="top_mirna" type="select" label="Choose the top differentially miRNAs of the analysis" help="Choose the number of top differentially expressed miRNAs.">
-      <option value="10" selected="true">Top 10</option>
-      <option value="20">Top 20</option>
-      <option value="30">Top 30</option>
-      <option value="40">Top 40</option>
-      <option value="50">Top 50</option>
-    </param>
-    <conditional name="stats">
-     <param name="choice" type="select" label="Choose p-value or p-adj" help="asdadadasd">
-      <option value="1" selected="true">Pvalue</option>
-      <option value="2">Padj</option>
-     </param>
-     <when value="1">
-      <param name="pvalue" type="float" min="0" max="1" value="0.05" label="P-value (max value)" help="p-value threshold" />
-     </when>
-     <when value="2">
-      <param name="padj" type="float" min="0" max="1" value="0.05" label="P-adjustment (max value)" help="p-adjustment threshold" />
-     </when>
-    </conditional>
-    <param name="log2fc" type="float" min="0" max="10" value="1" label="Log2FC (Absolute value)" help="Log2FC threshold" />
-  </inputs>
-  <outputs>
-    <!--data name="TOP_temp" format="png" label="TOP_$top_mirna Differentially Expressed miRNAs" from_work_dir="$__tool_directory__/tem.png" /-->
-    <!--data name="TOP_non" format="png" label="TOP_$top_mirna Differentially Expressed miRNAs" from_work_dir="$__tool_directory__/non.png" /-->
-    <data name="Scatter" format="png" label="Scatter Differentially Expressed miRNAs" from_work_dir="$__tool_directory__/a3.png" />
-    <data name="File" format="pdf" label="PDF" from_work_dir="$__tool_directory__/report2.pdf" />
-    <data name="File1" format="txt" label="txt" from_work_dir="$__tool_directory__/demo.txt" />
-  </outputs>
-  <help>
-
-  </help>
-</tool>
diff -r a09d238416ba -r d77dace80d5a viz_graphs.py
--- a/viz_graphs.py	Wed Oct 28 07:42:50 2020 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,210 +0,0 @@
-import sys
-
-# Read a file and return it as a list
-def read(path, flag):
-    if flag == 0:
-        with open(path) as fp:
-            file=fp.readlines()
-        fp.close()
-        return file
-
-    if flag == 1:
-        with open(path) as fp:
-            file = fp.read().splitlines()
-        fp.close()
-        return file
-
-
-#################################################################################################
-def pdf_before_DE(analysis):
-
-  # Import FPDF class
-  from fpdf import FPDF, fpdf
-
-  # Import glob module to find all the files matching a pattern
-  import glob
-
-  # Image extensions
-  if analysis=="2":
-     image_extensions = ("c_hist_red.png","t_hist_red.png","pie_non.png","spider_red.png","spider_non_red.png","c_logo.png","t_logo.png","c_bar.png","t_bar.png")
-  else:
-     image_extensions = ("c_hist_red.png","t_hist_red.png","pie_tem.png","spider_red.png","spider_non_red.png")
-  # This list will hold the images file names
-  images = []
-
-  # Build the image list by merging the glob results (a list of files)
-  # for each extension. We are taking images from current folder.
-  for extension in image_extensions:
-      images.extend(glob.glob(extension))
-  #sys.exit(images)
-  # Create instance of FPDF class
-  pdf = FPDF('P', 'in', 'A4')
-  # Add new page. Without this you cannot create the document.
-  pdf.add_page()
-  # Set font to Arial, 'B'old, 16 pts
-  pdf.set_font('Arial', 'B', 20.0)
-
-  # Page header
-  pdf.cell(pdf.w-0.5, 0.5, 'IsomiR Profile Report',align='C')
-  pdf.ln(0.7)
-  pdf.set_font('Arial','', 16.0)
-  pdf.cell(pdf.w-0.5, 0.5, 'sRNA Length Distribution',align='C')
-
-  # Smaller font for image captions
-  pdf.set_font('Arial', '', 11.0)
-
-  # Image caption
-  pdf.ln(0.5)
-
-  yh=FPDF.get_y(pdf)
-  pdf.image(images[0],x=0.3,w=4, h=3)
-  pdf.image(images[1],x=4,y=yh, w=4, h=3)
-  pdf.ln(0.3)
-
-  # Image caption
-  pdf.cell(0.2)
-  pdf.cell(3.0, 0.0, "  Mapped and unmapped reads to custom precussor arm reference DB (5p and 3p arms) in Control (left)")
-  pdf.ln(0.2)
-  pdf.cell(0.2)
-  pdf.cell(3.0, 0.0, "  and Treated (right) groups")
-
-
-  pdf.ln(0.5)
-  h1=FPDF.get_y(pdf)
-  pdf.image(images[2],x=1, w=6.5, h=5)
-  h2=FPDF.get_y(pdf)
-  FPDF.set_y(pdf,h1+0.2)
-  pdf.set_font('Arial','', 14.0)
-  pdf.cell(pdf.w-0.5, 0.5, 'Template and non-template IsomiRs',align='C')
-  pdf.set_font('Arial', '', 11.0)
-  FPDF.set_y(pdf,h2)
-  FPDF.set_y(pdf,9.5)
-  # Image caption
-  pdf.cell(0.2)
-  if analysis=="2":
-     pdf.cell(3.0, 0.0, "  Template, non-template, miRNA reference and unmapped sequences as percentage of total sRNA")
-  else:
-     pdf.cell(3.0, 0.0, "  Template, miRNA reference and unmapped sequences as percentage of total sRNA")
-  pdf.ln(0.2)
-  pdf.cell(0.2)
-  pdf.cell(3.0, 0.0, "  reads in Control (left) and treated (right) groups")
-
-
-
-  pdf.add_page()
-  pdf.set_font('Arial', 'B', 16.0)
-  pdf.cell(pdf.w-0.5, 0.5, "Reference form and isomiR among total miRNA reads",align='C')
-  pdf.ln(0.7)
-  pdf.set_font('Arial', 'B', 12.0)
-  pdf.cell(pdf.w-0.5, 0.5, "Template isomiR profile (redundant)",align='C')
-  pdf.ln(0.5)
-  pdf.image(images[3],x=1.5, w=5.5, h=4)
-  pdf.ln(0.6)
-  pdf.cell(pdf.w-0.5, 0.0, "Template isomiR profile (non-redundant)",align='C')
-  pdf.set_font('Arial', '', 12.0)
-  pdf.ln(0.2)
-  pdf.image(images[4],x=1.5, w=5.5, h=4)
-  pdf.ln(0.3)
-  pdf.set_font('Arial', '', 11.0)
-  pdf.cell(0.2)
-  pdf.cell(3.0, 0.0, "  * IsomiRs potentialy initiated from multiple loci")
-
-
-  if analysis=="2":
-     pdf.add_page('L')
-
-     pdf.set_font('Arial', 'B', 16.0)
-     pdf.cell(pdf.w-0.5, 0.5, "Non-template IsomiRs",align='C')
-     pdf.ln(0.5)
-     pdf.set_font('Arial', 'B', 12.0)
-     pdf.cell(pdf.w-0.5, 0.5, "3' Additions of reference of isomiR sequence",align='C')
-     pdf.ln(0.7)
-
-     yh=FPDF.get_y(pdf)
-     pdf.image(images[5],x=1.5,w=3.65, h=2.65)
-     pdf.image(images[7],x=6.5,y=yh, w=3.65, h=2.65)
-     pdf.ln(0.5)
-     yh=FPDF.get_y(pdf)
-     pdf.image(images[6],x=1.5,w=3.65, h=2.65)
-     pdf.image(images[8],x=6.5,y=yh, w=3.65, h=2.65)
-
-  pdf.close()
-  pdf.output('report1.pdf','F')
-
-
-
-
-#############################################################################################################################################################3
-
-def pdf_after_DE(analysis,top):
-
-  # Import FPDF class
-  from fpdf import FPDF
-
-  # Import glob module to find all the files matching a pattern
-  import glob
-
-  # Image extensions
-  if analysis=="2":
-     image_extensions = ("tem.png","a2.png","non.png")
-  else:
-    image_extensions = ("tem.png","a2.png")
- 
-  # This list will hold the images file names
-  images = []
-
-  # Build the image list by merging the glob results (a list of files)
-  # for each extension. We are taking images from current folder.
-  for extension in image_extensions:
-      images.extend(glob.glob(extension))
-  #sys.exit(images)
-  # Create instance of FPDF class
-  pdf = FPDF('P', 'in', 'letter')
-  # Add new page. Without this you cannot create the document.
-  pdf.add_page()
-  # Set font to Arial, 'B'old, 16 pts
-  pdf.set_font('Arial', 'B', 16.0)
-
-  # Page header
-  pdf.cell(pdf.w-0.5, 0.5, 'Differential expression of miRNAs and Isoforms',align='C')
-  #pdf.ln(0.25)
-
-  pdf.ln(0.7)
-  pdf.set_font('Arial','B', 12.0)
-  if "tem.png" in images:
-     pdf.cell(pdf.w-0.5, 0.5, 'Top '+top+' most differentially expressed miRNA and template isoforms',align='C')
-     # Smaller font for image captions
-     pdf.set_font('Arial', '', 10.0)
-     # Image caption 
-     pdf.ln(0.4)
-     pdf.image(images[images.index("tem.png")],x=0.8, w=7, h=8)
-     pdf.ln(0.3)
-     pdf.set_font('Arial','B', 12.0)
-  else:
-     print("WARNING: There aren't miRNAs which fullfiled these criteria" )
-
-  if "non.png" in images and analysis=="2":
-     if "tem.png" in images: pdf.add_page()
-     pdf.ln(0.7)
-     pdf.cell(pdf.w-0.5, 0.5, 'Top '+top+' most differentially expressed non-template isomiRs',align='C')
-     pdf.ln(0.4)
-     pdf.image(images[images.index("non.png")],x=0.5, w=7.5, h=6.5)
-  else:
-     print("WARNING: There aren't non-template miRNAs which fullfiled these criteria" )
-
-
-  if "a2.png" in images:
-     if len(images)>=2: pdf.add_page()
-     pdf.ln(0.5)
-     pdf.cell(pdf.w-0.5, 0.5, 'Top '+top+' most differentially expressed miRNAs and isomiRs grouped by arm',align='C')
-     pdf.ln(0.4)
-     pdf.image(images[images.index("a2.png")],x=0.8, w=7, h=8)
-     pdf.ln(0.3)
-  else:
-     print("WARNING: There aren't non-template miRNAs which fullfiled these criteria" )
-
-
-  pdf.output('report2.pdf', 'F')
-
-
-
diff -r a09d238416ba -r d77dace80d5a viz_ultra.py
--- a/viz_ultra.py	Wed Oct 28 07:42:50 2020 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,270 +0,0 @@
-import argparse
-from viz_graphs import *
-import sys
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-import matplotlib.font_manager as font_manager
-import time
-from multiprocessing import Process, Queue, Lock, Pool, Manager, Value
-
-
-##################################################################################################################################################################################################################
-
-def top_diff(miRNA_info, number,flag,l):
-
-    Kind=[]
-
-    miRNA_info.sort(key = lambda x: abs(x[1]),reverse=True)
-    miRNA_info = miRNA_info[:number]
-    miRNA_info.sort(key = lambda x: x[0])
-
-    for x in miRNA_info:
-        if x[1] > 0:
-           Kind.append(True)
-        elif x[1] < 0:
-           Kind.append(False)
-        else:
-           Kind.append("Zero")
-
-    top_miRNA = {"Names": [x[0] for x in miRNA_info],
-                  "Log2FC": [x[1] for x in miRNA_info],
-                  "Kind": Kind};
-
-    df_miRNA = pd.DataFrame(data=top_miRNA)
-    df_miRNA = df_miRNA.sort_values(by=['Names'])
-    if df_miRNA.empty==False:
-     h1=df_miRNA.plot.barh(x= 'Names',y='Log2FC',color=df_miRNA.Kind.map({True: 'g', False: 'r', 'Zero':'k'})) 
-     figure = plt.gcf()  # get current figure
-     figure.set_size_inches(5, 12) # set figure's size manually to your full screen (32x18)
-     up_reg = mpatches.Patch(color='green', label='Upregulated')
-     down_reg = mpatches.Patch(color='red', label='Downregulated')
-     font = font_manager.FontProperties(weight='bold', style='normal')
-     l3 = plt.legend(handles=[up_reg,down_reg],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
-     h1.set_ylabel(" ", fontsize=3, fontweight='bold')
-     h1.set_xlabel("Log2FC", fontsize=12, fontweight='bold')
-     plt.axvline(x=0, color="k")
-
-     plt.grid(axis='y', linewidth=0.2)
-     plt.grid(axis='x', linewidth=0.2)
-     if flag=='t':
-        plt.savefig('tem.png', bbox_inches='tight', dpi=300)
-     if flag=='nt':
-        plt.savefig('non.png', bbox_inches='tight', dpi=300)
-
-####################################################################################################################################################################################################################
-
-def unique(sequence):
-    seen = set()
-    return [x for x in sequence if not (x in seen or seen.add(x))]
-
-###########################################################################################################################################################################################################################################################################
-
-def top_scatter_non(matures,isoforms,non_temp,uni_names,number):
-
-    mat_names=[]
-    mat_log2fc=[]
-
-    iso_names=[]
-    iso_log2fc=[]
-
-    non_temp_names=[]
-    non_temp_log2fc=[]
-
-    count=0
-    for x in uni_names:
-        flag = False
-        if count<number:
-          for y in matures:
-            if x in y[0]:
-               mat_log2fc.append(y[1])
-               mat_names.append(x)
-               flag=True
-          for y in isoforms:
-            if x in y[0]:
-               iso_log2fc.append(y[1])
-               iso_names.append(x)
-               flag=True
-          for y in non_temp:
-            if x in y[0]:
-               non_temp_log2fc.append(y[1])
-               non_temp_names.append(x)
-               flag=True
-          if flag==True:
-             count+=1
-
-    mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
-    iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
-    non_df = pd.DataFrame(dict(names=non_temp_names, log2fc= non_temp_log2fc))
-
-    iso_df.sort_values(by=['names'])
-    mat_df.sort_values(by=['names'])
-    non_df.sort_values(by=['names'])
-
-    fig, ax = plt.subplots()
-
-    h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red')
-    h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green')
-    h2=ax.scatter(non_df['log2fc'],non_df['names'],edgecolors='k',linewidth=1, marker='o', c='blue')
-
-    l3 = plt.legend([h1,h2,h3],["Reference miRNA","Non-template","Template isomiRs"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
-    plt.axvline(x=0, color="k")
-    plt.grid(axis='y', linewidth=0.2)
-    plt.grid(axis='x', linewidth=0.2)
-    plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
-    plt.yticks(rotation=0,ha="right", fontsize=10)
-    plt.xticks(rotation=0,ha="right", fontsize=10)
-    plt.tight_layout()
-    figure = plt.gcf()  # get current figure
-    figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
-    plt.savefig('a2.png', bbox_inches='tight', dpi=300)
-
-#########################################################################################################################################################################################################################################
-def top_scatter_tem(matures,isoforms,uni_names,number):
-
-    mat_names=[]
-    mat_log2fc=[]
-
-    iso_names=[]
-    iso_log2fc=[]
-
-    count=0
-    for x in uni_names:
-        flag = False
-        if count<number:
-          for y in matures:
-            if x in y[0]:
-               mat_log2fc.append(y[1])
-               mat_names.append(x)
-               flag=True
-          for y in isoforms:
-            if x in y[0]:
-               iso_log2fc.append(y[1])
-               iso_names.append(x)
-               flag=True
-          if flag==True:
-             count+=1
-
-    mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
-    iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
-
-    iso_df.sort_values(by=['names'])
-    mat_df.sort_values(by=['names'])
-
-    fig, ax = plt.subplots()
-
-    h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red')
-    h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green')
-
-    l3 = plt.legend([h1,h3],["Reference miRNA","Template isomiRs"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
-    plt.axvline(x=0, color="k")
-    plt.grid(axis='y', linewidth=0.2)
-    plt.grid(axis='x', linewidth=0.2)
-    plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
-    plt.yticks(rotation=0,ha="right", fontsize=10)
-    plt.xticks(rotation=0,ha="right", fontsize=10)
-    plt.tight_layout()
-    figure = plt.gcf()  # get current figure
-    figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
-    plt.savefig('a2.png', bbox_inches='tight', dpi=300)
-
-
-##############################################################################################################################################################################################################################################
-def preproccess(non_templated,matures,isoforms,log2fc,pval):
-
-       non_temp = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>log2fc and float(x[2])<pval]
-       mat = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>log2fc and float(x[2])<pval]
-       iso = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>log2fc and float(x[2])<pval]
-       mat_iso = mat+iso
-
-       if not non_temp and not mat and not iso:
-          sys.exit("There aren't entries which meet these criteria")
-
-       mat.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       iso.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       non_temp.sort(key = lambda x: abs(float(x[1])),reverse=True)
-
-       all=mat+iso+non_temp
-       all.sort(key = lambda x: abs(float(x[1])), reverse=True)
-       names=[x[0].split("_")[0] for x in all]
-       uni_names=unique(names)
-
-       diff_non_templated = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
-       diff_matures = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
-       diff_isoforms = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
-
-       diff_matures.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       diff_isoforms.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       diff_non_templated.sort(key = lambda x: abs(float(x[1])),reverse=True)
-
-       return diff_matures,diff_isoforms,diff_non_templated,uni_names,non_temp,mat_iso
-
-##################################################################################################################################################################################################################################################################
-starttime = time.time()
-
-parser = argparse.ArgumentParser()
-parser.add_argument("-in", "--input", help="choose type of analysis", action="store")
-parser.add_argument("-p_value", "--pval", help="choose type of analysis", action="store")
-parser.add_argument("-fc", "--log2fc", help="choose type of analysis", action="store")
-parser.add_argument("-top", "--top_mirnas", help="choose type of analysis", action="store")
-parser.add_argument("-tool_dir", "--tool_directory", help="tool directory path", action="store")
-parser.add_argument("-statistic", "--stat", help="tool directory path", action="store")
-parser.add_argument("-diff_tool", "--tool", help="tool directory path", action="store")
-
-args = parser.parse_args()
-
-l=Lock()
-number = int(args.top_mirnas)
-log2fc = float(args.log2fc)
-pval = float(args.pval)
-
-if args.tool=="2":
-
-   raw_EdgeR = read(args.input,0)
-   EdgeR = [x.rstrip("\n").split("\t") for x in raw_EdgeR]
-   del EdgeR[0]
-
-   if args.stat=="1":
-      non_templated = [[x[0],x[1],x[4]] for x in EdgeR if "__" in x[0] and x[1]!="NA" and x[4]!="NA"]
-      matures = [[x[0],x[1],x[4]] for x in EdgeR if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[4]!="NA"]
-      isoforms = [[x[0],x[1],x[4]] for x in EdgeR if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[4]!="NA"]
-   else:
-      non_templated = [[x[0],x[1],x[5]] for x in EdgeR if "__" in x[0] and x[1]!="NA" and x[5]!="NA"]
-      matures = [[x[0],x[1],x[5]] for x in EdgeR if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[5]!="NA"]
-      isoforms = [[x[0],x[1],x[5]] for x in EdgeR if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[5]!="NA"]
-
-if args.tool=="1":
-
-   raw_Deseq = read(args.input,0)
-   Deseq = [x.rstrip("\n").split("\t") for x in raw_Deseq]
-
-   if args.stat=="1":
-      non_templated = [[x[0],x[2],x[5]] for x in Deseq if "__" in x[0] and x[2]!="NA" and x[5]!="NA"] 
-      matures = [[x[0],x[2],x[5]] for x in Deseq if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[5]!="NA"]
-      isoforms = [[x[0],x[2],x[5]] for x in Deseq if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[5]!="NA"]
-   else:
-      non_templated = [[x[0],x[2],x[6]] for x in Deseq if "__" in x[0] and x[2]!="NA" and x[6]!="NA"] 
-      matures = [[x[0],x[2],x[6]] for x in Deseq if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[6]!="NA"]
-      isoforms = [[x[0],x[2],x[6]] for x in Deseq if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[6]!="NA"]
-
-
-diff_matures,diff_isoforms,diff_non_templated,names,non_temp,mat_iso = preproccess(non_templated,matures,isoforms,log2fc,pval)
-
-if non_templated!=[]:
-   analysis="2"
-   p=[Process(target=top_diff,args=(non_temp,number,"nt",l))]
-   p.extend([Process(target=top_diff,args=(mat_iso,number,"t",l))])
-   p.extend([Process(target=top_scatter_non,args=(diff_matures,diff_isoforms,diff_non_templated,names,number))])
-
-else:
-   analysis="1"
-   p=[Process(target=top_diff,args=(mat_iso,number,"t"))]
-   p.extend([Process(target=top_scatter_tem,args=(diff_matures,diff_isoforms,names,number))])
-
-[x.start() for x in p]
-[x.join() for x in p]
-
-pdf_after_DE(analysis,args.top_mirnas)
-
-print('That took {} seconds'.format(time.time() - starttime))
-