--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/gene_identification.py	Thu Oct 13 10:52:24 2016 -0400
@@ -0,0 +1,220 @@
+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",
+                 "cm": "gggagtgcatccgccccaacc"} #new (shorter) cm sequence
+
+compiledregex = {"ca": [],
+                 "cg": [],
+                 "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 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 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))
+
+
+
+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.keys(): #for ca/cg/cm
+	regularexpressions = compiledregex[key] #get the compiled regular expressions
+	for ID in dic.keys()[0:]: #for every ID
+		if ID not in 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, "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
+				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'
+						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)))
+
+
+chunksInCA = len(compiledregex["ca"])
+chunksInCG = len(compiledregex["cg"])
+chunksInCM = len(compiledregex["cm"])
+requiredChunkPercentage = 0.7
+varsInCA = float(len(ca1.keys()) * 2)
+varsInCG = float(len(cg1.keys()) * 2) - 2 # -2 because the sliding window doesn't hit the first and last nt twice
+varsInCM = 0
+
+
+
+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"]))
+			cahits = currentIDHits["ca_hits"]
+			cghits = currentIDHits["cg_hits"]
+			cmhits = currentIDHits["cm_hits"]
+			if cahits >= cghits and cahits >= cmhits: #its a ca gene
+				ca1hits = currentIDHits["ca1"]
+				ca2hits = currentIDHits["ca2"]
+				if ca1hits >= ca2hits:
+					o.write(ID + "\tIGA1\t" + str(int(ca1hits / varsInCA * 100)) + "\t" + str(int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n")
+				else:
+					o.write(ID + "\tIGA2\t" + str(int(ca2hits / varsInCA * 100)) + "\t" + str(int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n")
+			elif cghits >= cahits and cghits >= cmhits: #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(int(cg1hits / varsInCG * 100)) + "\t" + str(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(int(cg2hits / varsInCG * 100)) + "\t" + str(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(int(cg3hits / varsInCG * 100)) + "\t" + str(int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
+				else: #cg4 gene
+					o.write(ID + "\tIGG4\t" + str(int(cg4hits / varsInCG * 100)) + "\t" + str(int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
+			else: #its a cm gene
+				o.write(ID + "\tIGM\t100\t" + str(int(cmhits / possiblecm * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
+			seq_write_count += 1
+
+print "Time: %i" % (int(time.time() * 1000) - starttime)
+
+print "Number of sequences written to file:", seq_write_count
+
+
+
+
+