Mercurial > repos > davidvanzessen > shm_csr

annotate gene_identification.py @ 83:729738462297 draft

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"planemo upload commit c0ffc68aec5836d5b20b543106493056a87edf57"
author rhpvorderman
date Wed, 15 Sep 2021 12:24:06 +0000
parents b6f9a640e098
children cf8ad181628f
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81
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davidvanzessen
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1 import re
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2 import argparse
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3 import time
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4 starttime= int(time.time() * 1000)
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5
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6 parser = argparse.ArgumentParser()
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7 parser.add_argument("--input", help="The 1_Summary file from an IMGT zip file")
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8 parser.add_argument("--output", help="The annotated output file to be merged back with the summary file")
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9
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10 args = parser.parse_args()
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11
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12 infile = args.input
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13 #infile = "test_VH-Ca_Cg_25nt/1_Summary_test_VH-Ca_Cg_25nt_241013.txt"
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14 output = args.output
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15 #outfile = "identified.txt"
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16
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17 dic = dict()
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18 total = 0
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19
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20
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21 first = True
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22 IDIndex = 0
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23 seqIndex = 0
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24
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25 with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence
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26 for line in f:
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27 total += 1
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28 linesplt = line.split("\t")
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29 if first:
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30 print("linesplt", linesplt)
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31 IDIndex = linesplt.index("Sequence ID")
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32 seqIndex = linesplt.index("Sequence")
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33 first = False
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34 continue
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35
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36 ID = linesplt[IDIndex]
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37 if len(linesplt) < 28: #weird rows without a sequence
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38 dic[ID] = ""
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39 else:
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40 dic[ID] = linesplt[seqIndex]
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41
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42 print("Number of input sequences:", len(dic))
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43
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44 #old cm sequence: gggagtgcatccgccccaacccttttccccctcgtctcctgtgagaattccc
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45 #old cg sequence: ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggccctgggctgcctggtcaaggactacttccccgaaccggtgacggtgtcgtggaactcaggcgccctgaccag
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46
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47 #lambda/kappa reference sequence
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48 searchstrings = {"ca": "catccccgaccagccccaaggtcttcccgctgagcctctgcagcacccagccagatgggaacgtggtcatcgcctgcctgg",
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49 "cg": "ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggcc",
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50 "ce": "gcctccacacagagcccatccgtcttccccttgacccgctgctgcaaaaacattccctcc",
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51 "cm": "gggagtgcatccgccccaacc"} #new (shorter) cm sequence
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52
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53 compiledregex = {"ca": [],
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54 "cg": [],
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55 "ce": [],
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56 "cm": []}
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57
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58 #lambda/kappa reference sequence variable nucleotides
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59 ca1 = {38: 't', 39: 'g', 48: 'a', 49: 'g', 51: 'c', 68: 'a', 73: 'c'}
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60 ca2 = {38: 'g', 39: 'a', 48: 'c', 49: 'c', 51: 'a', 68: 'g', 73: 'a'}
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61 cg1 = {0: 'c', 33: 'a', 38: 'c', 44: 'a', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'}
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62 cg2 = {0: 'c', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'g', 132: 't'}
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63 cg3 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'}
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64 cg4 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'c', 132: 'c'}
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65
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66 #remove last snp for shorter cg sequence --- note, also change varsInCG
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67 del cg1[132]
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68 del cg2[132]
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69 del cg3[132]
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70 del cg4[132]
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71
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72 #reference sequences are cut into smaller parts of 'chunklength' length, and with 'chunklength' / 2 overlap
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73 chunklength = 8
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74
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75 #create the chunks of the reference sequence with regular expressions for the variable nucleotides
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76 for i in range(0, len(searchstrings["ca"]) - chunklength, chunklength // 2):
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77 pos = i
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78 chunk = searchstrings["ca"][i:i+chunklength]
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79 result = ""
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80 varsInResult = 0
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81 for c in chunk:
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82 if pos in list(ca1.keys()):
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83 varsInResult += 1
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84 result += "[" + ca1[pos] + ca2[pos] + "]"
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85 else:
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86 result += c
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87 pos += 1
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88 compiledregex["ca"].append((re.compile(result), varsInResult))
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89
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90 for i in range(0, len(searchstrings["cg"]) - chunklength, chunklength // 2):
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91 pos = i
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92 chunk = searchstrings["cg"][i:i+chunklength]
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93 result = ""
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94 varsInResult = 0
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95 for c in chunk:
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96 if pos in list(cg1.keys()):
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97 varsInResult += 1
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98 result += "[" + "".join(set([cg1[pos], cg2[pos], cg3[pos], cg4[pos]])) + "]"
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99 else:
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100 result += c
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101 pos += 1
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102 compiledregex["cg"].append((re.compile(result), varsInResult))
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103
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104 for i in range(0, len(searchstrings["cm"]) - chunklength, chunklength // 2):
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105 compiledregex["cm"].append((re.compile(searchstrings["cm"][i:i+chunklength]), False))
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106
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107 for i in range(0, len(searchstrings["ce"]) - chunklength + 1, chunklength // 2):
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108 compiledregex["ce"].append((re.compile(searchstrings["ce"][i:i+chunklength]), False))
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109
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110 def removeAndReturnMaxIndex(x): #simplifies a list comprehension
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111 m = max(x)
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112 index = x.index(m)
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113 x[index] = 0
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114 return index
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115
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116
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117 start_location = dict()
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118 hits = dict()
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119 alltotal = 0
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120 for key in compiledregex: #for ca/cg/cm/ce
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121 regularexpressions = compiledregex[key] # get the compiled regular expressions
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122 for ID in list(dic.keys())[0:]: #for every ID
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123 if ID not in list(hits.keys()): #ensure that the dictionairy that keeps track of the hits for every gene exists
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124 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}
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125 currentIDHits = hits[ID]
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126 seq = dic[ID]
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127 lastindex = 0
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128 start_zero = len(searchstrings[key]) #allows the reference sequence to start before search sequence (start_locations of < 0)
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129 start = [0] * (len(seq) + start_zero)
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130 for i, regexp in enumerate(regularexpressions): #for every regular expression
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131 relativeStartLocation = lastindex - (chunklength // 2) * i
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132 if relativeStartLocation >= len(seq):
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133 break
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134 regex, hasVar = regexp
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135 matches = regex.finditer(seq[lastindex:])
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136 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
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137 lastindex += match.start()
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138 start[relativeStartLocation + start_zero] += 1
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139 if hasVar: #if the regex has a variable nt in it
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140 chunkstart = chunklength // 2 * i #where in the reference does this chunk start
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141 chunkend = chunklength // 2 * i + chunklength #where in the reference does this chunk end
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142 if key == "ca": #just calculate the variable nt score for 'ca', cheaper
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143 currentIDHits["ca1"] += len([1 for x in ca1 if chunkstart <= x < chunkend and ca1[x] == seq[lastindex + x - chunkstart]])
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144 currentIDHits["ca2"] += len([1 for x in ca2 if chunkstart <= x < chunkend and ca2[x] == seq[lastindex + x - chunkstart]])
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145 elif key == "cg": #just calculate the variable nt score for 'cg', cheaper
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146 currentIDHits["cg1"] += len([1 for x in cg1 if chunkstart <= x < chunkend and cg1[x] == seq[lastindex + x - chunkstart]])
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147 currentIDHits["cg2"] += len([1 for x in cg2 if chunkstart <= x < chunkend and cg2[x] == seq[lastindex + x - chunkstart]])
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148 currentIDHits["cg3"] += len([1 for x in cg3 if chunkstart <= x < chunkend and cg3[x] == seq[lastindex + x - chunkstart]])
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149 currentIDHits["cg4"] += len([1 for x in cg4 if chunkstart <= x < chunkend and cg4[x] == seq[lastindex + x - chunkstart]])
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150 else: #key == "cm" #no variable regions in 'cm' or 'ce'
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151 pass
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152 break #this only breaks when there was a match with the regex, breaking means the 'else:' clause is skipped
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153 else: #only runs if there were no hits
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154 continue
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155 #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]
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156 currentIDHits[key + "_hits"] += 1
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157 start_location[ID + "_" + key] = str([(removeAndReturnMaxIndex(start) + 1 - start_zero) for x in range(5) if len(start) > 0 and max(start) > 1])
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158 #start_location[ID + "_" + key] = str(start.index(max(start)))
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159
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160
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161 varsInCA = float(len(list(ca1.keys())) * 2)
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162 varsInCG = float(len(list(cg1.keys())) * 2) - 2 # -2 because the sliding window doesn't hit the first and last nt twice
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163 varsInCM = 0
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164 varsInCE = 0
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165
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166 def round_int(val):
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167 return int(round(val))
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168
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169 first = True
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170 seq_write_count=0
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171 with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence
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172 with open(output, 'w') as o:
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173 for line in f:
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174 total += 1
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175 if first:
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176 o.write("Sequence ID\tbest_match\tnt_hit_percentage\tchunk_hit_percentage\tstart_locations\n")
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177 first = False
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178 continue
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179 linesplt = line.split("\t")
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180 if linesplt[2] == "No results":
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181 pass
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182 ID = linesplt[1]
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183 currentIDHits = hits[ID]
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184 possibleca = float(len(compiledregex["ca"]))
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185 possiblecg = float(len(compiledregex["cg"]))
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186 possiblecm = float(len(compiledregex["cm"]))
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187 possiblece = float(len(compiledregex["ce"]))
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188 cahits = currentIDHits["ca_hits"]
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189 cghits = currentIDHits["cg_hits"]
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190 cmhits = currentIDHits["cm_hits"]
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191 cehits = currentIDHits["ce_hits"]
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192 if cahits >= cghits and cahits >= cmhits and cahits >= cehits: #its a ca gene
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193 ca1hits = currentIDHits["ca1"]
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194 ca2hits = currentIDHits["ca2"]
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195 if ca1hits >= ca2hits:
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196 o.write(ID + "\tIGA1\t" + str(round_int(ca1hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n")
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197 else:
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198 o.write(ID + "\tIGA2\t" + str(round_int(ca2hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n")
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199 elif cghits >= cahits and cghits >= cmhits and cghits >= cehits: #its a cg gene
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200 cg1hits = currentIDHits["cg1"]
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201 cg2hits = currentIDHits["cg2"]
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202 cg3hits = currentIDHits["cg3"]
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203 cg4hits = currentIDHits["cg4"]
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204 if cg1hits >= cg2hits and cg1hits >= cg3hits and cg1hits >= cg4hits: #cg1 gene
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205 o.write(ID + "\tIGG1\t" + str(round_int(cg1hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
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206 elif cg2hits >= cg1hits and cg2hits >= cg3hits and cg2hits >= cg4hits: #cg2 gene
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207 o.write(ID + "\tIGG2\t" + str(round_int(cg2hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
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208 elif cg3hits >= cg1hits and cg3hits >= cg2hits and cg3hits >= cg4hits: #cg3 gene
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209 o.write(ID + "\tIGG3\t" + str(round_int(cg3hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
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210 else: #cg4 gene
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211 o.write(ID + "\tIGG4\t" + str(round_int(cg4hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n")
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212 else: #its a cm or ce gene
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213 if cmhits >= cehits:
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214 o.write(ID + "\tIGM\t100\t" + str(round_int(cmhits / possiblecm * 100)) + "\t" + start_location[ID + "_cm"] + "\n")
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215 else:
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216 o.write(ID + "\tIGE\t100\t" + str(round_int(cehits / possiblece * 100)) + "\t" + start_location[ID + "_ce"] + "\n")
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217 seq_write_count += 1
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218
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219 print("Time: %i" % (int(time.time() * 1000) - starttime))
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220
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221 print("Number of sequences written to file:", seq_write_count)
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