Mercurial > repos > davidvanzessen > shm_csr

annotate gene_identification.py @ 97:fbc6307dd83b draft

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planemo upload commit 91fd26f19cb20fc2ee4d87d7edbc9ce6099752b0
author rhpvorderman
date Mon, 08 Jan 2024 08:50:07 +0000
parents cf8ad181628f
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 #!/usr/bin/env python3
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3 import argparse
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4 import re
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5 from typing import Dict, Iterator, List, Tuple
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8 def generate_sequence_and_id_from_summary(summary_file: str
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9 ) -> Iterator[Tuple[str, str]]:
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10 with open(summary_file, "rt") as summary:
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11 header = next(summary)
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12 column_names = header.strip("\n").split("\t")
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13 id_column = column_names.index("Sequence ID")
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14 sequence_column = column_names.index("Sequence")
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15 for line in summary:
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16 values = line.strip("\n").split("\t")
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17 id = values[id_column]
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18 try:
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19 sequence = values[sequence_column]
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20 except IndexError: # weird rows without a sequence
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21 sequence = ""
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22 yield id, sequence
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25 # old cm sequence: gggagtgcatccgccccaacccttttccccctcgtctcctgtgagaattccc
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26 # old cg sequence: ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctg
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27 # ggggcacagcggccctgggctgcctggtcaaggactacttccccgaaccggtgacggtgtcgtggaactcagg
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28 # cgccctgaccag
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29 SEARCHSTRINGS = {"ca": "catccccgaccagccccaaggtcttcccgctgagcctctgcagcacccagccag"
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30 "atgggaacgtggtcatcgcctgcctgg",
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31 "cg": "ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctc"
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32 "tgggggcacagcggcc",
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33 "ce": "gcctccacacagagcccatccgtcttccccttgacccgctgctgcaaaaacatt"
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34 "ccctcc",
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35 "cm": "gggagtgcatccgccccaacc"} #new (shorter) cm sequence
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37 #lambda/kappa referesearchstringsnce sequence variable nucleotides
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38 CA1_MUTATIONS = {38: 't', 39: 'g', 48: 'a', 49: 'g', 51: 'c', 68: 'a', 73: 'c'}
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39 CA2_MUTATIONS = {38: 'g', 39: 'a', 48: 'c', 49: 'c', 51: 'a', 68: 'g', 73: 'a'}
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40 CG1_MUTATIONS = {0: 'c', 33: 'a', 38: 'c', 44: 'a', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'}
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41 CG2_MUTATIONS = {0: 'c', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'g', 132: 't'}
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42 CG3_MUTATIONS = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'}
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43 CG4_MUTATIONS = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'c', 132: 'c'}
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45 #remove last snp for shorter cg sequence --- note, also change varsInCG
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46 del CG1_MUTATIONS[132]
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47 del CG2_MUTATIONS[132]
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48 del CG3_MUTATIONS[132]
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49 del CG4_MUTATIONS[132]
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50
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51 # reference sequences are cut into smaller parts of 'chunklength' length,
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52 # and with 'chunklength' / 2 overlap
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53 CHUNK_LENGTH = 8
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56 def create_compiled_regexes() -> Dict[str, List[Tuple[re.Pattern, int]]]:
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57
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58 compiledregex: Dict[str, List[Tuple[re.Pattern, int]]] = {
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59 "ca": [],
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60 "cg": [],
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61 "ce": [],
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62 "cm": []
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63 }
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65 for i in range(0, len(SEARCHSTRINGS["ca"]) - CHUNK_LENGTH, CHUNK_LENGTH // 2):
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66 pos = i
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67 chunk = SEARCHSTRINGS["ca"][i:i + CHUNK_LENGTH]
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68 result = ""
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69 varsInResult = 0
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70 for c in chunk:
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71 if pos in list(CA1_MUTATIONS.keys()):
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72 varsInResult += 1
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73 result += "[" + CA1_MUTATIONS[pos] + CA2_MUTATIONS[pos] + "]"
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74 else:
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75 result += c
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76 pos += 1
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77 compiledregex["ca"].append((re.compile(result), varsInResult))
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79 for i in range(0, len(SEARCHSTRINGS["cg"]) - CHUNK_LENGTH, CHUNK_LENGTH // 2):
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80 pos = i
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81 chunk = SEARCHSTRINGS["cg"][i:i + CHUNK_LENGTH]
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82 result = ""
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83 varsInResult = 0
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84 for c in chunk:
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85 if pos in list(CG1_MUTATIONS.keys()):
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86 varsInResult += 1
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87 result += "[" + "".join(set([CG1_MUTATIONS[pos], CG2_MUTATIONS[pos], CG3_MUTATIONS[pos], CG4_MUTATIONS[pos]])) + "]"
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88 else:
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89 result += c
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90 pos += 1
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91 compiledregex["cg"].append((re.compile(result), varsInResult))
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93 for i in range(0, len(SEARCHSTRINGS["cm"]) - CHUNK_LENGTH, CHUNK_LENGTH // 2):
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94 compiledregex["cm"].append((re.compile(SEARCHSTRINGS["cm"][i:i + CHUNK_LENGTH]), 0))
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96 for i in range(0, len(SEARCHSTRINGS["ce"]) - CHUNK_LENGTH + 1, CHUNK_LENGTH // 2):
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97 compiledregex["ce"].append((re.compile(SEARCHSTRINGS["ce"][i:i + CHUNK_LENGTH]), 0))
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98
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99 return compiledregex
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100
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102 def removeAndReturnMaxIndex(x): #simplifies a list comprehension
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103 m = max(x)
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104 index = x.index(m)
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105 x[index] = 0
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106 return index
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107
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109 def match_sequence(seq, compiledregex):
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110 currentIDHits = {"ca_hits": 0, "cg_hits": 0, "cm_hits": 0, "ce_hits": 0,
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111 "ca1": 0, "ca2": 0, "cg1": 0, "cg2": 0, "cg3": 0, "cg4": 0}
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112 alltotal = 0
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113 start_location = dict()
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114 for key in compiledregex: # for ca/cg/cm/ce
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115 regularexpressions = compiledregex[key]
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116 lastindex = 0
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117 start_zero = len(SEARCHSTRINGS[key]) #allows the reference sequence to start before search sequence (start_locations of < 0)
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118 start = [0] * (len(seq) + start_zero)
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119 for i, regexp in enumerate(regularexpressions): #for every regular expression
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120 relativeStartLocation = lastindex - (CHUNK_LENGTH // 2) * i
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121 if relativeStartLocation >= len(seq):
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122 break
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123 regex, hasVar = regexp
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124 matches = regex.finditer(seq[lastindex:])
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125 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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126 lastindex += match.start()
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127 start[relativeStartLocation + start_zero] += 1
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128 if hasVar: #if the regex has a variable nt in it
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129 chunkstart = CHUNK_LENGTH // 2 * i #where in the reference does this chunk start
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130 chunkend = CHUNK_LENGTH // 2 * i + CHUNK_LENGTH #where in the reference does this chunk end
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131 if key == "ca": #just calculate the variable nt score for 'ca', cheaper
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132 currentIDHits["ca1"] += len([1 for x in CA1_MUTATIONS if chunkstart <= x < chunkend and CA1_MUTATIONS[x] == seq[lastindex + x - chunkstart]])
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133 currentIDHits["ca2"] += len([1 for x in CA2_MUTATIONS if chunkstart <= x < chunkend and CA2_MUTATIONS[x] == seq[lastindex + x - chunkstart]])
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134 elif key == "cg": #just calculate the variable nt score for 'cg', cheaper
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135 currentIDHits["cg1"] += len([1 for x in CG1_MUTATIONS if chunkstart <= x < chunkend and CG1_MUTATIONS[x] == seq[lastindex + x - chunkstart]])
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136 currentIDHits["cg2"] += len([1 for x in CG2_MUTATIONS if chunkstart <= x < chunkend and CG2_MUTATIONS[x] == seq[lastindex + x - chunkstart]])
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137 currentIDHits["cg3"] += len([1 for x in CG3_MUTATIONS if chunkstart <= x < chunkend and CG3_MUTATIONS[x] == seq[lastindex + x - chunkstart]])
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138 currentIDHits["cg4"] += len([1 for x in CG4_MUTATIONS if chunkstart <= x < chunkend and CG4_MUTATIONS[x] == seq[lastindex + x - chunkstart]])
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139 else: #key == "cm" #no variable regions in 'cm' or 'ce'
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140 pass
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141 break #this only breaks when there was a match with the regex, breaking means the 'else:' clause is skipped
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142 else: #only runs if there were no hits
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143 continue
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144 #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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145 currentIDHits[key + "_hits"] += 1
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146 start_location[key] = str([(removeAndReturnMaxIndex(start) + 1 - start_zero) for x in range(5) if len(start) > 0 and max(start) > 1])
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147
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148 cahits = currentIDHits["ca_hits"]
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149 cghits = currentIDHits["cg_hits"]
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150 cmhits = currentIDHits["cm_hits"]
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151 cehits = currentIDHits["ce_hits"]
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152 if cahits >= cghits and cahits >= cmhits and cahits >= cehits: # its a ca gene
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153 ca1hits = currentIDHits["ca1"]
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154 ca2hits = currentIDHits["ca2"]
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155 if ca1hits >= ca2hits:
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156 # TODO: All variants with 0 matched are matched to IGA1 with 0 hits
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157 # TODO: these are later turned into unmatched by the merge_and_filter.R
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158 # TODO: script
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159 return "IGA1", ca1hits, cahits, start_location["ca"]
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160 else:
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161 return "IGA2", ca2hits, cahits, start_location["ca"]
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162 elif cghits >= cahits and cghits >= cmhits and cghits >= cehits: # its a cg gene
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163 cg1hits = currentIDHits["cg1"]
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164 cg2hits = currentIDHits["cg2"]
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165 cg3hits = currentIDHits["cg3"]
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166 cg4hits = currentIDHits["cg4"]
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167 if cg1hits >= cg2hits and cg1hits >= cg3hits and cg1hits >= cg4hits: # cg1 gene
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168 return "IGG1", cg1hits, cghits, start_location["cg"]
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169 elif cg2hits >= cg1hits and cg2hits >= cg3hits and cg2hits >= cg4hits: # cg2 gene
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170 return "IGG2", cg2hits, cghits, start_location["cg"]
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171 elif cg3hits >= cg1hits and cg3hits >= cg2hits and cg3hits >= cg4hits: # cg3 gene
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172 return "IGG3", cg3hits, cghits, start_location["cg"]
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173 else: # cg4 gene
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174 return "IGG4", cg4hits, cghits, start_location["cg"]
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175 else: # its a cm or ce gene
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176 if cmhits >= cehits:
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177 return "IGM", 0, cmhits, start_location["cm"]
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178 else:
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179 return "IGE", 0, cehits, start_location["ce"]
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180
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181
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182 def main():
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183 parser = argparse.ArgumentParser()
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184 parser.add_argument("--input",
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185 help="The 1_Summary file from an IMGT zip file")
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186 parser.add_argument("--output",
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187 help="The annotated output file to be merged back "
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188 "with the summary file")
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189 args = parser.parse_args()
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190 varsInCA = float(len(list(CA1_MUTATIONS.keys())) * 2)
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191 varsInCG = float(len(list(
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192 CG1_MUTATIONS.keys())) * 2) - 2 # -2 because the sliding window doesn't hit the first and last nt twice
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193 subclass_vars = {
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194 "IGA1": varsInCA, "IGA2": varsInCA,
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195 "IGG1": varsInCG, "IGG2": varsInCG, "IGG3": varsInCG, "IGG4": varsInCG,
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196 "IGE": 0,
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197 "IGM": 0,
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198 }
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199 compiledregex = create_compiled_regexes()
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200 possibleca = float(len(compiledregex["ca"]))
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201 possiblecg = float(len(compiledregex["cg"]))
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202 possiblecm = float(len(compiledregex["cm"]))
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203 possiblece = float(len(compiledregex["ce"]))
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204 class_chunks = {
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205 "IGA1": possibleca, "IGA2": possibleca,
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206 "IGE": possiblece,
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207 "IGG1": possiblecg, "IGG2": possiblecg, "IGG3": possiblecg,
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208 "IGG4": possiblecg,
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209 "IGM": possiblecm
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210 }
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211 with open(args.output, "wt") as output:
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212 output.write("Sequence ID\tbest_match\tnt_hit_percentage\t"
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213 "chunk_hit_percentage\tstart_locations\n")
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214 for id, sequence in generate_sequence_and_id_from_summary(args.input):
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215 best_match, subclass_hits, class_hits, start_locations = \
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216 match_sequence(sequence, compiledregex)
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217 variable_nucs = subclass_vars[best_match]
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218 if variable_nucs:
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219 subclass_percentage = round(subclass_hits * 100 /
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220 variable_nucs)
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221 else:
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222 subclass_percentage = 100
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223 class_percentage = round(class_hits * 100 / class_chunks[best_match])
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224 output.write(f"{id}\t{best_match}\t{subclass_percentage}\t"
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225 f"{class_percentage}\t{start_locations}\n")
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226
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227
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228 if __name__ == "__main__":
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229 main()