--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mirbase_functions.py	Tue Oct 17 08:22:13 2023 +0000
@@ -0,0 +1,766 @@
+import itertools
+import re
+import urllib.request
+import gzip
+import copy
+from collections import OrderedDict
+
+
+
+# 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
+
+# Write a list to a txt file
+def write(path, list):
+    with open(path,'w') as fp:
+        for x in list:
+            fp.write(str("\t".join(x[1:-1])))
+    fp.close()
+
+
+#################################################################################################################>
+
+# Detect the longest common substring sequence between two mirnas
+def longestSubstring(str1, str2):
+
+    from difflib import SequenceMatcher
+    # initialize SequenceMatcher object with
+    # input string
+    seqMatch = SequenceMatcher(None, str1, str2)
+
+    # find match of longest sub-string
+    # output will be like Match(a=0, b=0, size=5)
+    match = seqMatch.find_longest_match(0, len(str1), 0, len(str2))
+
+    # print longest substring
+    if (match.size != 0):
+        return str1[match.a: match.a + match.size]
+    else:
+        print('No longest common sub-string found')
+
+#################################################################################################################################################################################################################
+
+"""
+
+This function concatenates miRNAs which are generated from different chromosomes
+and eliminates the duplications of miRNAs on every sample
+
+input:  detected miRNAs
+output: collpased miRNAs without duplicates
+
+"""
+
+
+def remove_duplicates(mirnas):
+
+ # Detection of canonical mirRNAs whicha are generated from different chromosomes
+ dupes=[[x[9],x[0],x[2]] for x in mirnas]
+
+ for x in mirnas:
+     for y in dupes:
+         if x[9] == y[0] and x[0] == y[1] and x[2].split("_")[0] == y[2].split("_")[0] and x[2] != y[2]:
+            y.append(x[2])
+
+ # Detection of different chromosomes for every miRNA
+ chr_order = []
+ for x in dupes:
+     temp = []
+     for i in range(2,len(x)):
+         if x[i].split("chr")[1].split("(")[0].isdigit():
+            temp.append(int(x[i].split("chr")[1].split("(")[1][0]+x[i].split("chr")[1].split("(")[0]))
+         else:
+            temp.append(x[i].split("chr")[1][0:4])
+
+     for z in temp:
+         if 'X(-)'==z or 'Y(-)'==z or 'X(+)'==z or 'Y(+)'==z:
+             temp = [str(j) for j in temp]
+     temp = list(set(temp))
+     temp.sort()
+     chr_order.append(temp)
+
+ # Collapsing the miRNAs with the same sequence from different chromosomes
+ collapsed_dupes=[]
+ for i in range(len(dupes)):
+     collapsed_dupes.append([dupes[i][0],dupes[i][2].split("_")[0],dupes[i][1]])
+     for x in chr_order[i]:
+         chr_check = re.match("[-+]?\d+$", str(x))	  # check if chromosome is 'X' or 'Y'
+         if chr_check is not None:
+            if int(x)<0:                 # Check the strand (+) or (-)
+               collapsed_dupes[i][1]= collapsed_dupes[i][1]+"_chr"+str(abs(int(x)))+"(-)"
+            else:
+               collapsed_dupes[i][1] = collapsed_dupes[i][1] + "_chr" + str(abs(int(x)))+"(+)"
+         else:
+            collapsed_dupes[i][1] = collapsed_dupes[i][1] + "_chr" + str(x)
+
+ # Remove duplicates from collapsed_dupes
+ collapsed_dupes.sort()
+ collapsed_dupes = list(collapsed_dupes for collapsed_dupes,_ in itertools.groupby(collapsed_dupes))
+
+ for i in range(len(mirnas)):
+     for x in collapsed_dupes:
+
+         # Naming of template isomirs (adding positions in the names)
+         if mirnas[i][9] == x[0] and mirnas[i][0] == x[2] and len(mirnas[i][2].split("_")) >3 and mirnas[i][2].split("_")[0]==x[1].split("_")[0]:
+            gg=str("_t_"+mirnas[i][2].split("_")[-2]+"_"+mirnas[i][2].split("_")[-1])
+            mirnas[i][2] = x[1]+gg
+            break
+
+         # Naming of canonical miRNAs (collpsed names)
+         if mirnas[i][9]==x[0] and mirnas[i][0]== x[2] and len(mirnas[i][2].split("_"))==3 and mirnas[i][2].split("_")[0]==x[1].split("_")[0]:
+            mirnas[i][2] = x[1]
+            break
+
+ # Remove duplicates
+ mirnas.sort()
+ mirnas=list(mirnas for mirnas,_ in itertools.groupby(mirnas))
+
+ return mirnas
+
+#############################################################################################################################################################################################################
+
+"""
+
+This function indentifies and classifies the miRNAs which are detected from the alignment tool.
+
+"""
+
+def sam_edit(mature_mirnas,path,file,case,l,samples,data,file_order,unmap_seq,names_n_seqs,deseq,mirna_names,ini_sample,unmap_counts):
+
+    # read the sam file
+    ini_sam=read(path,0)
+    main_sam = [x.rstrip("\n").split("\t") for x in ini_sam if "@" not in x.split("\t")[0]]     # remove introduction
+    unique_seq = [x for x in main_sam if x[1] == '0' and len(x[9])>=18 and len(x[9])<=26]   # keeps only the functional miRNAs
+    filter_sam = [[x[0],x[1],x[2],len(x[9])] for x in main_sam]                             # keeps only the necessary info of miRNAs from sam files (name, sequence, counts, etc)
+
+    sorted_uni_arms = []
+
+    for i in range(0,len(mature_mirnas,),2):
+        tmp_count_reads = 0   # calculate the total number of reads
+        tmp_count_seq = 0     # calculate the total number of sequences
+        for j in range(len(unique_seq)):
+
+            if "{" in unique_seq[j][2].split("_")[0]:           # checks if a miRNA is generated from two different locis on the same chromosome
+                mirna=unique_seq[j][2].split("_")[0][:-4]
+            else:
+                mirna=unique_seq[j][2].split("_")[0]
+
+            # Detection of differences between the canonical miRNA and the detected miRNA
+            if mature_mirnas[i].split(" ")[0][1:] == mirna:
+
+                temp_mature = mature_mirnas[i+1].strip().replace("U", "T")
+                off_part = longestSubstring(temp_mature, unique_seq[j][9])
+
+                mat_diff = temp_mature.split(off_part)
+                mat_diff = [len(mat_diff[0]), len(mat_diff[1])]
+
+                unique_diff = unique_seq[j][9].split(off_part)
+                unique_diff = [len(unique_diff[0]), len(unique_diff[1])]
+
+                # Handling of some special mirnas like (hsa-miR-8485)
+                if mat_diff[1]!=0 and unique_diff[1]!=0:
+                    unique_seq[j]=1
+                    pre_pos = 0
+                    post_pos = 0
+
+                elif mat_diff[0]!=0 and unique_diff[0]!=0:
+                    unique_seq[j]=1
+                    pre_pos = 0
+                    post_pos = 0
+
+                else:
+                   # Keep the findings
+                   pre_pos = mat_diff[0]-unique_diff[0]
+                   post_pos = unique_diff[1]-mat_diff[1]
+                   tmp_count_reads = tmp_count_reads + int(unique_seq[j][0].split("-")[1])
+                   tmp_count_seq = tmp_count_seq+1
+
+                # Store the detected miRNAs with new names according to the findings
+                if pre_pos != 0 or post_pos != 0:
+                    if pre_pos == 0:
+                        unique_seq[j][2] = unique_seq[j][2].split("_")[0]+"_"+unique_seq[j][2].split("_")[2]+ "_t_" +str(pre_pos) + "_" + '{:+d}'.format(post_pos)
+                    elif post_pos == 0:
+                        unique_seq[j][2] = unique_seq[j][2].split("_")[0]+"_"+unique_seq[j][2].split("_")[2] + "_t_" + '{:+d}'.format(pre_pos) + "_" + str(post_pos)
+                    else:
+                        unique_seq[j][2] = unique_seq[j][2].split("_")[0]+"_"+unique_seq[j][2].split("_")[2]+"_t_"+'{:+d}'.format(pre_pos)+"_"+'{:+d}'.format(post_pos)
+
+        # Remove the values "1" from the handling of special mirnas (hsa-miR-8485)
+        for x in range(unique_seq.count(1)):
+            unique_seq.remove(1)
+
+        # metrics for the production of database
+        if tmp_count_reads != 0 and tmp_count_seq != 0:
+           sorted_uni_arms.append([mature_mirnas[i].split(" ")[0][1:], tmp_count_seq, tmp_count_reads])
+
+    # Sorting of the metrics for database
+    sorted_uni_arms = sorted(sorted_uni_arms, key=lambda x: x[1], reverse=True)
+
+    # Collapsing of miRNAs and removing of duplicates
+    collapsed_mirnas = remove_duplicates(unique_seq)
+
+    # Correction of metrics due to the collapsing and removing of duplicates for the production of Database
+    for y in sorted_uni_arms:
+       counts=0
+       seqs=0
+       for x in collapsed_mirnas:
+           if y[0] in x[2].split("_")[0]:
+              counts+=int(x[0].split("-")[1])
+              seqs+=1
+
+       y[1]=seqs
+       y[2]=counts
+
+
+    # Output variables
+    temp_mirna_names=[]
+
+    l.acquire()
+
+    if case == "c" or case == "t":
+       temp_mirna_names.extend(z[2] for z in collapsed_mirnas)
+       names_n_seqs.extend([[y[2],y[9]] for y in collapsed_mirnas])
+       deseq.append([[x[2], x[0].split('-')[1], x[9]] for x in collapsed_mirnas])
+       mirna_names.extend(temp_mirna_names)
+       unmap_seq.value += sum([1 for x in main_sam if x[1] == '4'])     # Keeps the unmap unique sequences for the production of a graph
+       unmap_counts.value += sum([int(x[0].split("-")[1]) for x in main_sam if x[1] == '4'])    # Keeps the unmap counts of sequences for the production of a graph
+       file_order.append(file)    #Keeps the names of SAM files with the order of reading by the fuction (avoid problems due to multiprocesssing)
+       samples.append(collapsed_mirnas)         # return the processed detected miRNAs
+       data.append([case,file,collapsed_mirnas,sorted_uni_arms])
+       ini_sample.append(filter_sam)    # returns the filtered sam file
+
+    l.release()
+
+
+######################################################################################################################################
+
+
+"""
+
+Read a sam file from Bowtie and do the followings:
+
+1) Remove reverse stranded mapped reads
+2) Remove unmapped reads 
+3) Remove all sequences with reads less than 11 reads
+4) Sort the arms with the most sequences in decreading rate
+5) Sort the sequences of every arm with the most reads in decreasing rate
+6) Calculate total number of sequences of every arm
+7) Calculate total number of reads of sequences of every arm.
+8) Store all the informations in a txt file 
+
+"""
+
+def non_sam_edit(mature_mirnas,path,file,case,l,data,file_order,n_deseq,names_n_seqs):
+
+    # read the sam file
+    ini_sam=read(path,0)
+    main_sam = [x.rstrip("\n").split("\t") for x in ini_sam if "@" not in x.split("\t")[0]]
+    unique_seq=[]
+    unique_seq = [x for x in main_sam if x[1] == '4' and len(x[9])>=18 and len(x[9])<=26]
+
+    uni_seq=[]
+
+    # Calculate the shifted positions for every non template mirna and add them to the name of it
+    sorted_uni_arms = []
+    for i in range(1,len(mature_mirnas),2):
+        tmp_count_reads = 0   # calculate the total number of reads
+        tmp_count_seq = 0     # calculate the total number of sequences
+
+        for j in range(len(unique_seq)):
+
+            temp_mature = mature_mirnas[i].strip().replace("U", "T")
+
+            # Detection of differences between the canonical miRNA and the detected non template miRNA
+            if temp_mature in unique_seq[j][9]:
+
+                off_part = longestSubstring(temp_mature, unique_seq[j][9])
+
+                mat_diff = temp_mature.split(off_part)
+                mat_diff = [len(mat_diff[0]), len(mat_diff[1])]
+
+                unique_diff = unique_seq[j][9].split(off_part)
+                if len(unique_diff)<=2:
+                   unique_diff = [len(unique_diff[0]), len(unique_diff[1])]
+
+                   pre_pos = mat_diff[0]-unique_diff[0]
+                   post_pos = unique_diff[1]-mat_diff[1]
+
+                   lengthofmir = len(off_part) + post_pos
+                   if pre_pos == 0 and post_pos<4:
+                      tmp_count_reads = tmp_count_reads + int(unique_seq[j][0].split("-")[1])
+                      tmp_count_seq = tmp_count_seq + 1
+
+                      t_name=unique_seq[j].copy()
+                      t_name[2]=mature_mirnas[i - 1].split(" ")[0][1:] + "_nont_" + str(pre_pos) + "_" + '{:+d}'.format(post_pos) + "_" + str(unique_seq[j][9][len(off_part):])
+                      uni_seq.append(t_name)
+        # metrics for the production of database
+        if tmp_count_reads != 0 and tmp_count_seq != 0:
+            sorted_uni_arms.append([mature_mirnas[i-1].split(" ")[0][1:], tmp_count_seq, tmp_count_reads])
+
+    sorted_uni_arms = sorted(sorted_uni_arms, key=lambda x: x[1], reverse=True)
+    unique_seq = list(map(list, OrderedDict.fromkeys(map(tuple,uni_seq))))
+
+    # Output variables
+    l.acquire()
+    if case=="c" or case=="t":
+       names_n_seqs.extend([[y[2],y[9]] for y in unique_seq if y[2]!="*"])
+       n_deseq.append([[x[2], x[0].split('-')[1], x[9]] for x in unique_seq if x[2]!="*"])
+       file_order.append(file)
+       data.append([case,file,unique_seq,sorted_uni_arms])
+    l.release()
+
+#################################################################################################################################################################################################################
+
+def black_white(mirna_names_1,mirna_names_2,group,manager):
+
+    add_names = [x for x in mirna_names_1 if x not in mirna_names_2]
+    add_names.sort()
+    add_names = list(add_names for add_names,_ in itertools.groupby(add_names))
+
+    group.sort()
+    group = list(group for group,_ in itertools.groupby(group))
+
+    zeros=["0"]*(len(group[0])-2)
+    [add_names[i].extend(zeros) for i,_ in enumerate(add_names)]
+    group=group+add_names
+
+    manager.extend(group)
+
+################################################################################################################################################################################################################################
+
+def merging_dupes(group,f_dupes):
+
+    dupes=[]
+    final_mat =[]
+
+    for num,_ in enumerate(group):
+
+        if group[num][1] not in final_mat and group[num][0] not in final_mat:
+           final_mat.append(group[num][1])
+           final_mat.append(group[num][0])
+        else:
+           dupes.append(group[num][1])
+
+
+    dupes=list(set(dupes))
+
+    dupes=[[x] for x in dupes]
+
+    for x in group:
+        for y in dupes:
+            if x[1]==y[0]:
+               fl=0
+               if len(y)==1:
+                  y.append(x[0])
+               else:
+                  for i in range(1,len(y)):
+                      if y[i].split("_")[0]==x[0].split("_")[0]:
+                         fl=1
+                         if len(x[0])<len(y[i]):
+                            del y[i]
+                            y.append(x[0])
+                            break
+
+                  if fl==0:
+                     y.append((x[0]))
+
+    for y in dupes:
+        if len(y)>2:
+           for i in range(len(y)-1,1,-1):
+               y[1]=y[1]+"/"+y[i]
+               del y[i]
+
+    f_dupes.extend(dupes)
+
+##########################################################################################################################################################################################################################################
+
+def apply_merging_dupes(group,dupes,managger):
+
+    for x in group:
+     for y in dupes:
+         if x[1]==y[0]:
+            x[0]=y[1]
+
+    group.sort()
+    group=list(group for group,_ in itertools.groupby(group))
+    managger.extend(group)
+
+###############################################################################################################################################################################################################################
+
+
+def filter_low_counts(c_group,t_group,fil_c_group,fil_t_group,per,counts):
+
+    t_group_new=[]
+    c_group_new=[]
+
+    percent=int(per)/100
+    c_col_filter=round(percent*(len(c_group[1])-2))
+    t_col_filter=round(percent*(len(t_group[1])-2))
+
+    for i, _ in enumerate(c_group):
+        c_cols=0
+        t_cols=0
+
+        c_cols=sum([1 for j in range(len(c_group[i])-2) if int(c_group[i][j+2])>=int(counts)])
+        t_cols=sum([1 for j in range(len(t_group[i])-2) if int(t_group[i][j+2])>=int(counts)])
+
+        if c_cols>=c_col_filter or t_cols>=t_col_filter:
+           t_group_new.append(t_group[i])
+           c_group_new.append(c_group[i])
+
+    fil_c_group.extend(c_group_new)
+    fil_t_group.extend(t_group_new)
+
+##################################################################################################################################################################################################################
+
+
+def write_main(raw_con, raw_tre, fil_con, fil_tre, con_file_order, tre_file_order, flag, group_name1, group_name2, per):
+
+ if flag == 1 and int(per)!=-1:
+    fp = open('Counts/Filtered '+group_name2 +' Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in tre_file_order:
+       fp.write("\t"+y)
+
+    for x in fil_tre:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+    fp = open('Counts/Filtered '+group_name1+' Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in con_file_order:
+       fp.write("\t"+y)
+
+    for x in fil_con:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+
+ if flag == 2 and int(per)!=-1:
+    fp = open('Counts/Filtered '+group_name2+' Non-Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in tre_file_order:
+       fp.write("\t"+y)
+
+
+    for x in fil_tre:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+    fp = open('Counts/Filtered '+group_name1+' Non-Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in con_file_order:
+       fp.write("\t"+y)
+
+    for x in fil_con:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+
+ if flag == 1:
+    fp = open('Counts/Raw '+group_name2+' Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in tre_file_order:
+       fp.write("\t"+y)
+
+    for x in raw_tre:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+    fp = open('Counts/Raw '+group_name1+' Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in con_file_order:
+       fp.write("\t"+y)
+
+    for x in raw_con:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+
+ if flag == 2:
+    fp = open('Counts/Raw '+group_name2+' Non-Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in tre_file_order:
+       fp.write("\t"+y)
+
+
+    for x in raw_tre:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+    fp = open('Counts/Raw '+group_name1+' Non-Templated Counts', 'w')
+    fp.write("Name\t")
+    fp.write("Sequence")
+    for y in con_file_order:
+       fp.write("\t"+y)
+
+    for x in raw_con:
+        fp.write("\n%s" % "\t".join(x))
+    fp.close()
+
+
+#########################################################################################################################################
+
+def temp_counts_to_diff(names,samp,folder):
+
+    for i in range(2,len(samp[0])):
+
+       fp = open(folder+names[i-2]+'.txt','w')
+       fp.write("miRNA id"+"\t"+names[i-2]+"\n")
+
+       for x in samp:
+           fp.write("%s" % "\t".join([x[0],x[i]])+"\n")
+       fp.close()
+
+##################################################################################################################
+
+def DB_write(con,name,unique_seq,sorted_uni_arms,f):
+
+ if f==1:
+    # Write a txt file with all the information
+    if con=="c":
+       fp = open('split1/'+name, 'w')
+
+       fp.write("%s\t%-42s\t%s\n\n" % ("Number of Reads","Name of isomir","Sequence"))
+    if con=="t":
+       fp = open('split2/'+name, 'w')
+       fp.write("%s\t%-42s\t%s\n\n" % ("Number of Reads","Name of isomir","Sequence"))
+
+
+    for i in range(len(sorted_uni_arms)):
+        temp = []
+        for j in range(len(unique_seq)):
+
+            if sorted_uni_arms[i][0] in unique_seq[j][2].split("_")[0]:
+
+                temp.append(unique_seq[j])
+
+        temp = sorted(temp, key=lambda x: int(x[0].split('-')[1]), reverse=True)
+        fp.write("*********************************************************************************************************\n")
+        fp.write("%-8s\t%-22s\t%-25s\t%-30s\t%s\n" % ("|",str(sorted_uni_arms[i][0]),"Sequence count = "+str(sorted_uni_arms[i][1]),"Total reads = "+str(sorted_uni_arms[i][2]),"|"))
+        fp.write("*********************************************************************************************************\n\n")
+        [fp.write("%-8s\t%-40s\t%s\n" % (x[0].split("-")[1], x[2],x[9])) for x in temp]
+        fp.write("\n" + "\n")
+    fp.close()
+
+ if f==2:
+
+    if con=="c":
+       fp = open('split3/'+name, 'w')
+       fp.write("%s\t%-42s\t%s\n\n" % ("Number of Reads","Name of isomir","Sequence"))
+    if con=="t":
+       fp = open('split4/'+name, 'w')
+       fp.write("%s\t%-42s\t%s\n\n" % ("Number of Reads","Name of isomir","Sequence"))
+
+
+    for i in range(len(sorted_uni_arms)):
+        temp = []
+        for j in range(len(unique_seq)):
+               if sorted_uni_arms[i][0]==unique_seq[j][2].split("_nont_")[0]:
+                  temp.append(unique_seq[j])
+        if temp!=[]:
+           temp = sorted(temp, key=lambda x: int(x[0].split('-')[1]), reverse=True)
+           fp.write("*********************************************************************************************************\n")
+           fp.write("%-8s\t%-22s\t%-25s\t%-30s\t%s\n" % ("|",str(sorted_uni_arms[i][0]),"Sequence count = "+str(sorted_uni_arms[i][1]),"Total reads = "+str(sorted_uni_arms[i][2]),"|"))
+           fp.write("*********************************************************************************************************\n\n")
+           [fp.write("%-8s\t%-40s\t%s\n" % (x[0].split("-")[1], x[2],x[9])) for x in temp]
+           fp.write("\n" + "\n")
+    fp.close()
+
+
+##########################################################################################################################
+
+def new_mat_seq(pre_unique_seq,mat_mirnas,l):
+
+    unique_iso = []
+    for x in pre_unique_seq:
+       if len(x[2].split("_"))==3:
+          for y in pre_unique_seq:
+              if x[2] in y[2] and int(x[0].split("-")[1])<int(y[0].split("-")[1]):
+                 if any(y[2] in lst2 for lst2 in unique_iso)==False:
+                    y[2]=">"+y[2]
+                    unique_iso.append(y)
+    l.acquire()
+    for x in unique_iso:
+        mat_mirnas.append(x[2])
+        mat_mirnas.append(x[9])
+    l.release()
+
+#########################################################################################################################
+
+def merging_names(ini_mat,new):
+
+    dupes=[]
+    final_mat =[]
+
+    for num in range(len(ini_mat)):
+
+        if ini_mat[num][1] not in final_mat and ini_mat[num][0] not in final_mat:
+           final_mat.append(ini_mat[num][1])
+           final_mat.append(ini_mat[num][0])
+        else:
+           dupes.append(ini_mat[num][1])
+
+    dupes=list(set(dupes))
+
+    for i in range(len(dupes)):
+        dupes[i]=[dupes[i]]
+
+    for x in ini_mat:
+        for y in dupes:
+            if x[1]==y[0]:
+               fl=0
+               if len(y)==1:
+                  y.append(x[0])
+               else:
+                  for i in range(1,len(y)):
+                      if y[i].split("_")[0]==x[0].split("_")[0]:
+                         fl=1
+                         if len(x[0])<len(y[i]):
+                            del y[i]
+                            y.append(x[0])
+                            break
+
+                  if fl==0:
+                     y.append((x[0]))
+
+    for y in dupes:
+        if len(y)>2:
+           for i in range(len(y)-1,1,-1):
+               y[1]=y[1]+"/"+y[i]
+               del y[i]
+
+
+    for x in ini_mat:
+        for y in dupes:
+            if x[1]==y[0]:
+               x[0]=y[1]
+
+    ini_mat.sort()
+    ini_mat=list(ini_mat for ini_mat,_ in itertools.groupby(ini_mat))
+
+    new.extend(ini_mat)
+
+
+######################################################################################################################################################
+
+def nontemp_counts_to_diff(tem_names,tem_samp,non_names,non_samp,folder):
+
+    for i in range(2,len(tem_samp[0])):
+
+       fp = open(folder+tem_names[i-2]+'.txt','w')
+       fp.write("miRNA id"+"\t"+tem_names[i-2]+"\n")
+
+       for x in tem_samp:
+           fp.write("%s" % "\t".join([x[0],x[i]])+"\n")
+
+       for j in range(len(non_names)):
+           if non_names[j]==tem_names[i-2]:
+              for x in non_samp:
+                  fp.write("%s" % "\t".join([x[0],x[j+2]])+"\n")
+       fp.close()
+
+###################################################################################################################################################################################################################
+
+"""
+
+This function downloads all the miRNAs of all the species from MirBase
+and filters them by the requested organism
+
+input : Organism
+output: A list with the miRNA sequences in fasta format
+
+"""
+
+def download_matures(matures,org_name):
+
+    url = 'https://mirbase.org/download/CURRENT/mature.fa'
+    data = urllib.request.urlopen(url).read().decode('utf-8')
+    file_mirna = data.split("<br>")
+    file_mirna = list(map(lambda x: x.replace('&gt;', ''), file_mirna))
+
+    for i in range(0,len(file_mirna)-1,2):
+
+        if org_name in file_mirna[i]:
+           matures.append(file_mirna[i])
+           matures.append(file_mirna[i+1])
+
+###################################################################################################################################################################################################################
+
+
+"""
+
+This function keeps all mirna isoforms which are detected on SAM files from the first part of the analysis
+These isoforms will be used as refence sequences with canonical (ref) mirnas for the detection of non-template
+mirnas
+
+"""
+
+
+def non_template_ref(c_samples,t_samples,all_isoforms):
+
+  pre_uni_seq_con = list(c_samples)
+  pre_uni_seq_tre = list(t_samples)
+
+  for x in pre_uni_seq_con:
+      for y in x:
+          #if ">"+y[2] not in all_isoforms and ")_" in y[2] :
+           if ">"+y[2] not in all_isoforms and "_t_" in y[2] :
+             all_isoforms.append(">"+y[2])
+             all_isoforms.append(y[9])
+
+  for x in pre_uni_seq_tre:
+      for y in x:
+          #if ">"+y[2] not in all_isoforms and ")_" in y[2]:
+           if ">"+y[2] not in all_isoforms and "_t_" in y[2] :
+             all_isoforms.append(">"+y[2])
+             all_isoforms.append(y[9])
+
+################################################################################################################################################################################################
+
+"""
+
+This function adds the uncommon detected miRNAs among samples.
+As a result all samples will have the same length.
+
+"""
+
+def uncommon_mirnas(sample,mir_names,l,new_d,sample_name,sample_order):
+
+    for y in mir_names:
+        flag=0
+        for x in sample:
+            if y[0]==x[0]: # check if miRNA exists in the sample
+               flag=1
+               break
+        if flag==0:
+           sample.append([y[0],"0",y[1]]) # add the name of mirna to the sample with zero counts and its sequence
+
+    # sorting and remove duplicates
+    sample.sort(key=lambda x: x[0])
+    sample=list(sample for sample,_ in itertools.groupby(sample))
+
+    # Return the updated sample
+    l.acquire()
+    new_d.append(sample)
+    sample_order.append(sample_name)
+    l.release()
+
+###############################################################################################################################################################################################
+