diff gene_identification.py @ 0:64d74ba01a7c draft

"planemo upload commit 78d1fae87dbcf490e49a9f99e7a06de7328e16d4"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/gene_identification.py	Wed Oct 27 12:34:47 2021 +0000
@@ -0,0 +1,226 @@
+import re
+import argparse
+import time
+starttime= int(time.time() * 1000)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input", help="The 1_Summary file from an IMGT zip file")
+parser.add_argument("--output", help="The annotated output file to be merged back with the summary file")
+
+args = parser.parse_args()
+
+infile = args.input
+#infile = "test_VH-Ca_Cg_25nt/1_Summary_test_VH-Ca_Cg_25nt_241013.txt"
+output = args.output
+#outfile = "identified.txt"
+
+dic = dict()
+total = 0
+
+
+first = True
+IDIndex = 0
+seqIndex = 0
+
+with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence
+    for line in f:
+        total += 1
+        linesplt = line.split("\t")
+        if first:
+            print("linesplt", linesplt)
+            IDIndex = linesplt.index("Sequence ID")
+            seqIndex = linesplt.index("Sequence")
+            first = False
+            continue
+        
+        ID = linesplt[IDIndex]
+        if len(linesplt) < 28: #weird rows without a sequence
+            dic[ID] = ""
+        else:
+            dic[ID] = linesplt[seqIndex]
+            
+print("Number of input sequences:", len(dic))
+
+#old cm sequence: gggagtgcatccgccccaacccttttccccctcgtctcctgtgagaattccc
+#old cg sequence: ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggccctgggctgcctggtcaaggactacttccccgaaccggtgacggtgtcgtggaactcaggcgccctgaccag
+
+#lambda/kappa reference sequence
+searchstrings = {"ca": "catccccgaccagccccaaggtcttcccgctgagcctctgcagcacccagccagatgggaacgtggtcatcgcctgcctgg",
+                 "cg": "ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggcc",
+                 "ce": "gcctccacacagagcccatccgtcttccccttgacccgctgctgcaaaaacattccctcc",
+                 "cm": "gggagtgcatccgccccaacc"} #new (shorter) cm sequence
+
+compiledregex = {"ca": [],
+                 "cg": [],
+                 "ce": [],
+                 "cm": []}
+
+#lambda/kappa reference sequence variable nucleotides
+ca1 = {38: 't', 39: 'g', 48: 'a', 49: 'g', 51: 'c', 68: 'a', 73: 'c'}
+ca2 = {38: 'g', 39: 'a', 48: 'c', 49: 'c', 51: 'a', 68: 'g', 73: 'a'}
+cg1 = {0: 'c', 33: 'a', 38: 'c', 44: 'a', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'}
+cg2 = {0: 'c', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'g', 132: 't'}
+cg3 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'}
+cg4 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'c', 132: 'c'}
+
+#remove last snp for shorter cg sequence --- note, also change varsInCG
+del cg1[132]
+del cg2[132]
+del cg3[132]
+del cg4[132]
+
+#reference sequences are cut into smaller parts of 'chunklength' length, and with 'chunklength' / 2 overlap
+chunklength = 8
+
+#create the chunks of the reference sequence with regular expressions for the variable nucleotides
+for i in range(0, len(searchstrings["ca"]) - chunklength, chunklength // 2):
+  pos = i
+  chunk = searchstrings["ca"][i:i+chunklength]
+  result = ""
+  varsInResult = 0
+  for c in chunk:
+    if pos in list(ca1.keys()):
+      varsInResult += 1
+      result += "[" + ca1[pos] + ca2[pos] + "]"
+    else:
+      result += c
+    pos += 1
+  compiledregex["ca"].append((re.compile(result), varsInResult))
+
+for i in range(0, len(searchstrings["cg"]) - chunklength, chunklength // 2):
+  pos = i
+  chunk = searchstrings["cg"][i:i+chunklength]
+  result = ""
+  varsInResult = 0
+  for c in chunk:
+    if pos in list(cg1.keys()):
+      varsInResult += 1
+      result += "[" + "".join(set([cg1[pos], cg2[pos], cg3[pos], cg4[pos]])) + "]"
+    else:
+      result += c
+    pos += 1
+  compiledregex["cg"].append((re.compile(result), varsInResult))
+
+for i in range(0, len(searchstrings["cm"]) - chunklength, chunklength // 2):
+  compiledregex["cm"].append((re.compile(searchstrings["cm"][i:i+chunklength]), False))
+
+for i in range(0, len(searchstrings["ce"]) - chunklength + 1, chunklength // 2):
+  compiledregex["ce"].append((re.compile(searchstrings["ce"][i:i+chunklength]), False))
+
+def removeAndReturnMaxIndex(x): #simplifies a list comprehension
+  m = max(x)
+  index = x.index(m)
+  x[index] = 0
+  return index
+  
+
+start_location = dict()
+hits = dict()
+alltotal = 0
+for key in compiledregex: #for ca/cg/cm/ce
+    regularexpressions = compiledregex[key]  # get the compiled regular expressions
+    for ID in list(dic.keys())[0:]: #for every ID
+        if ID not in list(hits.keys()): #ensure that the dictionairy that keeps track of the hits for every gene exists
+            hits[ID] = {"ca_hits": 0, "cg_hits": 0, "cm_hits": 0, "ce_hits": 0, "ca1": 0, "ca2": 0, "cg1": 0, "cg2": 0, "cg3": 0, "cg4": 0}
+        currentIDHits = hits[ID]
+        seq = dic[ID]
+        lastindex = 0
+        start_zero = len(searchstrings[key]) #allows the reference sequence to start before search sequence (start_locations of < 0)
+        start = [0] * (len(seq) + start_zero)
+        for i, regexp in enumerate(regularexpressions): #for every regular expression
+            relativeStartLocation = lastindex - (chunklength // 2) * i
+            if relativeStartLocation >= len(seq):
+                break
+            regex, hasVar = regexp
+            matches = regex.finditer(seq[lastindex:])
+            for match in matches: #for every match with the current regex, only uses the first hit because of the break at the end of this loop
+                lastindex += match.start()
+                start[relativeStartLocation + start_zero] += 1
+                if hasVar: #if the regex has a variable nt in it
+                    chunkstart = chunklength // 2 * i #where in the reference does this chunk start
+                    chunkend = chunklength // 2 * i + chunklength #where in the reference does this chunk end
+                    if key == "ca": #just calculate the variable nt score for 'ca', cheaper
+                        currentIDHits["ca1"] += len([1 for x in ca1 if chunkstart <= x < chunkend and ca1[x] == seq[lastindex + x - chunkstart]])
+                        currentIDHits["ca2"] += len([1 for x in ca2 if chunkstart <= x < chunkend and ca2[x] == seq[lastindex + x - chunkstart]])
+                    elif key == "cg": #just calculate the variable nt score for 'cg', cheaper
+                        currentIDHits["cg1"] += len([1 for x in cg1 if chunkstart <= x < chunkend and cg1[x] == seq[lastindex + x - chunkstart]])
+                        currentIDHits["cg2"] += len([1 for x in cg2 if chunkstart <= x < chunkend and cg2[x] == seq[lastindex + x - chunkstart]])
+                        currentIDHits["cg3"] += len([1 for x in cg3 if chunkstart <= x < chunkend and cg3[x] == seq[lastindex + x - chunkstart]])
+                        currentIDHits["cg4"] += len([1 for x in cg4 if chunkstart <= x < chunkend and cg4[x] == seq[lastindex + x - chunkstart]])
+                    else: #key == "cm" #no variable regions in 'cm' or 'ce'
+                        pass
+                break #this only breaks when there was a match with the regex, breaking means the 'else:' clause is skipped
+            else: #only runs if there were no hits
+                continue
+            #print "found ", regex.pattern , "at", lastindex, "adding one to", (lastindex - chunklength / 2 * i), "to the start array of", ID, "gene", key, "it's now:", start[lastindex - chunklength / 2 * i]
+            currentIDHits[key + "_hits"] += 1
+        start_location[ID + "_" + key] = str([(removeAndReturnMaxIndex(start) + 1 - start_zero) for x in range(5) if len(start) > 0 and max(start) > 1])
+        #start_location[ID + "_" + key] = str(start.index(max(start)))
+
+
+varsInCA = float(len(list(ca1.keys())) * 2)
+varsInCG = float(len(list(cg1.keys())) * 2) - 2 # -2 because the sliding window doesn't hit the first and last nt twice
+varsInCM = 0
+varsInCE = 0
+
+def round_int(val):
+    return int(round(val))
+
+first = True
+seq_write_count=0
+with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence
+    with open(output, 'w') as o:
+        for line in f:
+            total += 1
+            if first:
+                o.write("Sequence ID\tbest_match\tnt_hit_percentage\tchunk_hit_percentage\tstart_locations\n")
+                first = False
+                continue
+            linesplt = line.split("\t")
+            if linesplt[2] == "No results":
+                pass
+            ID = linesplt[1]
+            currentIDHits = hits[ID]
+            possibleca = float(len(compiledregex["ca"]))
+            possiblecg = float(len(compiledregex["cg"]))
+            possiblecm = float(len(compiledregex["cm"]))
+            possiblece = float(len(compiledregex["ce"]))
+            cahits = currentIDHits["ca_hits"]
+            cghits = currentIDHits["cg_hits"]
+            cmhits = currentIDHits["cm_hits"]
+            cehits = currentIDHits["ce_hits"]
+            if cahits >= cghits and cahits >= cmhits and cahits >= cehits: #its a ca gene
+                ca1hits = currentIDHits["ca1"]
+                ca2hits = currentIDHits["ca2"]
+                if ca1hits >= ca2hits:
+                    o.write(ID + "\tIGA1\t" + str(round_int(ca1hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n")
+                else:
+                    o.write(ID + "\tIGA2\t" + str(round_int(ca2hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n")
+            elif cghits >= cahits and cghits >= cmhits and cghits >= cehits: #its a cg gene
+                cg1hits = currentIDHits["cg1"]
+                cg2hits = currentIDHits["cg2"]
+                cg3hits = currentIDHits["cg3"]
+                cg4hits = currentIDHits["cg4"]
+                if cg1hits >= cg2hits and cg1hits >= cg3hits and cg1hits >= cg4hits: #cg1 gene
+                    o.write(ID + "\tIGG1\t" + str(round_int(cg1hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
+                elif cg2hits >= cg1hits and cg2hits >= cg3hits and cg2hits >= cg4hits: #cg2 gene
+                    o.write(ID + "\tIGG2\t" + str(round_int(cg2hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
+                elif cg3hits >= cg1hits and cg3hits >= cg2hits and cg3hits >= cg4hits: #cg3 gene
+                    o.write(ID + "\tIGG3\t" + str(round_int(cg3hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
+                else: #cg4 gene
+                    o.write(ID + "\tIGG4\t" + str(round_int(cg4hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
+            else: #its a cm or ce gene
+                if cmhits >= cehits:
+                    o.write(ID + "\tIGM\t100\t" + str(round_int(cmhits / possiblecm * 100)) + "\t" + start_location[ID + "_cm"] + "\n")
+                else:
+                    o.write(ID + "\tIGE\t100\t" + str(round_int(cehits / possiblece * 100)) + "\t" + start_location[ID + "_ce"] + "\n")
+            seq_write_count += 1
+
+print("Time: %i" % (int(time.time() * 1000) - starttime))
+
+print("Number of sequences written to file:", seq_write_count)
+
+
+
+
+