annotate utilities/genMutModel.py @ 10:7d10b55965c9 draft default tip

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author thondeboer
date Wed, 16 May 2018 17:02:51 -0400
parents 6e75a84e9338
children
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1 #!/usr/bin/env python
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2
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3 import sys
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4 import os
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5 import re
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6 import bisect
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7 import pickle
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8 import argparse
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9 import numpy as np
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10 #matplotlib is not used as far as i can see
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11 #import matplotlib.pyplot as mpl
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12
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13 # absolute path to the directory above this script
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14 SIM_PATH = '/'.join(os.path.realpath(__file__).split('/')[:-2])
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15 sys.path.append(SIM_PATH+'/py/')
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16
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17 from refFunc import indexRef
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18
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19 REF_WHITELIST = [str(n) for n in xrange(1,30)] + ['x','y','X','Y','mt','Mt','MT']
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20 REF_WHITELIST += ['chr'+n for n in REF_WHITELIST]
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21 VALID_NUCL = ['A','C','G','T']
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22 VALID_TRINUC = [VALID_NUCL[i]+VALID_NUCL[j]+VALID_NUCL[k] for i in xrange(len(VALID_NUCL)) for j in xrange(len(VALID_NUCL)) for k in xrange(len(VALID_NUCL))]
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23 # if parsing a dbsnp vcf, and no CAF= is found in info tag, use this as default val for population freq
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24 VCF_DEFAULT_POP_FREQ = 0.00001
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25
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26
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27 #########################################################
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28 # VARIOUS HELPER FUNCTIONS #
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29 #########################################################
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30
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31
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32 # given a reference index, grab the sequence string of a specified reference
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33 def getChrFromFasta(refPath,ref_inds,chrName):
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34 for i in xrange(len(ref_inds)):
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35 if ref_inds[i][0] == chrName:
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36 ref_inds_i = ref_inds[i]
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37 break
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38 refFile = open(refPath,'r')
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39 refFile.seek(ref_inds_i[1])
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40 myDat = ''.join(refFile.read(ref_inds_i[2]-ref_inds_i[1]).split('\n'))
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41 return myDat
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42
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43 # cluster a sorted list
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44 def clusterList(l,delta):
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45 outList = [[l[0]]]
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46 prevVal = l[0]
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47 currentInd = 0
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48 for n in l[1:]:
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49 if n-prevVal <= delta:
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50 outList[currentInd].append(n)
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51 else:
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52 currentInd += 1
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53 outList.append([])
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54 outList[currentInd].append(n)
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55 prevVal = n
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56 return outList
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57
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58 def list_2_countDict(l):
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59 cDict = {}
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60 for n in l:
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61 if n not in cDict:
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62 cDict[n] = 0
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63 cDict[n] += 1
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64 return cDict
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65
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66 def getBedTracks(fn):
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67 f = open(fn,'r')
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68 trackDict = {}
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69 for line in f:
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70 splt = line.strip().split('\t')
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71 if splt[0] not in trackDict:
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72 trackDict[splt[0]] = []
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73 trackDict[splt[0]].extend([int(splt[1]),int(splt[2])])
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74 f.close()
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75 return trackDict
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76
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77 def getTrackLen(trackDict):
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78 totSum = 0
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79 for k in trackDict.keys():
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80 for i in xrange(0,len(trackDict[k]),2):
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81 totSum += trackDict[k][i+1] - trackDict[k][i] + 1
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82 return totSum
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83
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84 def isInBed(track,ind):
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85 myInd = bisect.bisect(track,ind)
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86 if myInd&1:
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87 return True
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88 if myInd < len(track):
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89 if track[myInd-1] == ind:
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90 return True
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91 return False
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92
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93 ## return the mean distance to the median of a cluster
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94 #def mean_dist_from_median(c):
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95 # centroid = np.median([n for n in c])
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96 # dists = []
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97 # for n in c:
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98 # dists.append(abs(n-centroid))
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99 # return np.mean(dists)
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100 #
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101 ## get median value from counting dictionary
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102 #def quick_median(countDict):
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103 # midPoint = sum(countDict.values())/2
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104 # mySum = 0
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105 # myInd = 0
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106 # sk = sorted(countDict.keys())
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107 # while mySum < midPoint:
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108 # mySum += countDict[sk[myInd]]
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109 # if mySum >= midPoint:
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110 # break
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111 # myInd += 1
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112 # return myInd
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113 #
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114 ## get median deviation from median of counting dictionary
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115 #def median_deviation_from_median(countDict):
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116 # myMedian = quick_median(countDict)
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117 # deviations = {}
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118 # for k in sorted(countDict.keys()):
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119 # d = abs(k-myMedian)
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120 # deviations[d] = countDict[k]
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121 # return quick_median(deviations)
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122
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123
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124 #################################################
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125 # PARSE INPUT OPTIONS #
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126 #################################################
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127
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128
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129 parser = argparse.ArgumentParser(description='genMutModel.py')
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130 parser.add_argument('-r', type=str, required=True, metavar='<str>', help="* ref.fa")
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131 parser.add_argument('-m', type=str, required=True, metavar='<str>', help="* mutations.tsv [.vcf]")
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132 parser.add_argument('-o', type=str, required=True, metavar='<str>', help="* output.p")
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133 parser.add_argument('-bi', type=str, required=False, metavar='<str>', default=None, help="only_use_these_regions.bed")
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134 parser.add_argument('-be', type=str, required=False, metavar='<str>', default=None, help="exclude_these_regions.bed")
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135 parser.add_argument('--save-trinuc', required=False,action='store_true', default=False, help='save trinuc counts for ref')
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136 parser.add_argument('--no-whitelist',required=False,action='store_true', default=False, help='allow any non-standard ref')
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137 parser.add_argument('--skip-common', required=False,action='store_true', default=False, help='do not save common snps + high mut regions')
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138 args = parser.parse_args()
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139 (REF, TSV, OUT_PICKLE, SAVE_TRINUC, NO_WHITELIST, SKIP_COMMON) = (args.r, args.m, args.o, args.save_trinuc, args.no_whitelist, args.skip_common)
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140
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141 MYBED = None
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142 if args.bi != None:
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143 print 'only considering variants in specified bed regions...'
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144 MYBED = (getBedTracks(args.bi),True)
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145 elif args.be != None:
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146 print 'only considering variants outside of specified bed regions...'
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147 MYBED = (getBedTracks(args.be),False)
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148
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149 if TSV[-4:] == '.vcf':
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150 IS_VCF = True
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151 elif TSV[-4:] == '.tsv':
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152 IS_VCF = False
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153 else:
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154 print '\nError: Unknown format for mutation input.\n'
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155 exit(1)
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156
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157
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158 #####################################
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159 # main() #
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160 #####################################
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161
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162
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163 def main():
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164
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165 ref_inds = indexRef(REF)
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166 refList = [n[0] for n in ref_inds]
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167
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168 # how many times do we observe each trinucleotide in the reference (and input bed region, if present)?
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169 TRINUC_REF_COUNT = {}
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170 TRINUC_BED_COUNT = {}
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171 printBedWarning = True
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172 # [(trinuc_a, trinuc_b)] = # of times we observed a mutation from trinuc_a into trinuc_b
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173 TRINUC_TRANSITION_COUNT = {}
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174 # total count of SNPs
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175 SNP_COUNT = 0
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176 # overall SNP transition probabilities
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177 SNP_TRANSITION_COUNT = {}
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178 # total count of indels, indexed by length
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179 INDEL_COUNT = {}
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180 # tabulate how much non-N reference sequence we've eaten through
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181 TOTAL_REFLEN = 0
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182 # detect variants that occur in a significant percentage of the input samples (pos,ref,alt,pop_fraction)
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183 COMMON_VARIANTS = []
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184 # tabulate how many unique donors we've encountered (this is useful for identifying common variants)
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185 TOTAL_DONORS = {}
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186 # identify regions that have significantly higher local mutation rates than the average
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187 HIGH_MUT_REGIONS = []
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188
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189 # load and process variants in each reference sequence individually, for memory reasons...
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190 for refName in refList:
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191
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192 if (refName not in REF_WHITELIST) and (not NO_WHITELIST):
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193 print refName,'is not in our whitelist, skipping...'
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194 continue
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195
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196 print 'reading reference "'+refName+'"...'
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197 refSequence = getChrFromFasta(REF,ref_inds,refName).upper()
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198 TOTAL_REFLEN += len(refSequence) - refSequence.count('N')
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199
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200 # list to be used for counting variants that occur multiple times in file (i.e. in multiple samples)
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201 VDAT_COMMON = []
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202
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203
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204 """ ##########################################################################
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205 ### COUNT TRINUCLEOTIDES IN REF ###
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206 ########################################################################## """
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207
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208
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209 if MYBED != None:
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210 if printBedWarning:
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211 print "since you're using a bed input, we have to count trinucs in bed region even if you specified a trinuc count file for the reference..."
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212 printBedWarning = False
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213 if refName in MYBED[0]:
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214 refKey = refName
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215 elif ('chr' in refName) and (refName not in MYBED[0]) and (refName[3:] in MYBED[0]):
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216 refKey = refName[3:]
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217 elif ('chr' not in refName) and (refName not in MYBED[0]) and ('chr'+refName in MYBED[0]):
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218 refKey = 'chr'+refName
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219 if refKey in MYBED[0]:
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220 subRegions = [(MYBED[0][refKey][n],MYBED[0][refKey][n+1]) for n in xrange(0,len(MYBED[0][refKey]),2)]
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221 for sr in subRegions:
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222 for i in xrange(sr[0],sr[1]+1-2):
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223 trinuc = refSequence[i:i+3]
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224 if not trinuc in VALID_TRINUC:
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225 continue # skip if trinuc contains invalid characters, or not in specified bed region
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226 if trinuc not in TRINUC_BED_COUNT:
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227 TRINUC_BED_COUNT[trinuc] = 0
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228 TRINUC_BED_COUNT[trinuc] += 1
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229
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230 if not os.path.isfile(REF+'.trinucCounts'):
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231 print 'counting trinucleotides in reference...'
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232 for i in xrange(len(refSequence)-2):
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233 if i%1000000 == 0 and i > 0:
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234 print i,'/',len(refSequence)
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235 #break
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236 trinuc = refSequence[i:i+3]
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237 if not trinuc in VALID_TRINUC:
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238 continue # skip if trinuc contains invalid characters
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239 if trinuc not in TRINUC_REF_COUNT:
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240 TRINUC_REF_COUNT[trinuc] = 0
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241 TRINUC_REF_COUNT[trinuc] += 1
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242 else:
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243 print 'skipping trinuc counts (for whole reference) because we found a file...'
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244
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245
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246 """ ##########################################################################
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247 ### READ INPUT VARIANTS ###
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248 ########################################################################## """
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249
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250
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251 print 'reading input variants...'
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252 f = open(TSV,'r')
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253 isFirst = True
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254 for line in f:
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255
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256 if IS_VCF and line[0] == '#':
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257 continue
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258 if isFirst:
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259 if IS_VCF:
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260 # hard-code index values based on expected columns in vcf
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261 (c1,c2,c3,m1,m2,m3) = (0,1,1,3,3,4)
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262 else:
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263 # determine columns of fields we're interested in
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264 splt = line.strip().split('\t')
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265 (c1,c2,c3) = (splt.index('chromosome'),splt.index('chromosome_start'),splt.index('chromosome_end'))
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266 (m1,m2,m3) = (splt.index('reference_genome_allele'),splt.index('mutated_from_allele'),splt.index('mutated_to_allele'))
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267 (d_id) = (splt.index('icgc_donor_id'))
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268 isFirst = False
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269 continue
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270
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271 splt = line.strip().split('\t')
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272 # we have -1 because tsv/vcf coords are 1-based, and our reference string index is 0-based
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273 [chrName,chrStart,chrEnd] = [splt[c1],int(splt[c2])-1,int(splt[c3])-1]
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274 [allele_ref,allele_normal,allele_tumor] = [splt[m1].upper(),splt[m2].upper(),splt[m3].upper()]
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275 if IS_VCF:
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276 if len(allele_ref) != len(allele_tumor):
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277 # indels in tsv don't include the preserved first nucleotide, so lets trim the vcf alleles
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278 [allele_ref,allele_normal,allele_tumor] = [allele_ref[1:],allele_normal[1:],allele_tumor[1:]]
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279 if not allele_ref: allele_ref = '-'
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280 if not allele_normal: allele_normal = '-'
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281 if not allele_tumor: allele_tumor = '-'
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282 # if alternate alleles are present, lets just ignore this variant. I may come back and improve this later
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283 if ',' in allele_tumor:
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284 continue
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285 vcf_info = ';'+splt[7]+';'
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286 else:
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287 [donor_id] = [splt[d_id]]
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288
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289 # if we encounter a multi-np (i.e. 3 nucl --> 3 different nucl), let's skip it for now...
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290 if ('-' not in allele_normal and '-' not in allele_tumor) and (len(allele_normal) > 1 or len(allele_tumor) > 1):
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291 print 'skipping a complex variant...'
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292 continue
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293
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294 # to deal with '1' vs 'chr1' references, manually change names. this is hacky and bad.
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295 if 'chr' not in chrName:
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296 chrName = 'chr'+chrName
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297 if 'chr' not in refName:
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298 refName = 'chr'+refName
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299 # skip irrelevant variants
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300 if chrName != refName:
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301 continue
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302
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303 # if variant is outside the regions we're interested in (if specified), skip it...
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304 if MYBED != None:
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305 refKey = refName
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306 if not refKey in MYBED[0] and refKey[3:] in MYBED[0]: # account for 1 vs chr1, again...
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307 refKey = refKey[3:]
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308 if refKey not in MYBED[0]:
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309 inBed = False
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310 else:
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311 inBed = isInBed(MYBED[0][refKey],chrStart)
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312 if inBed != MYBED[1]:
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313 continue
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314
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diff changeset
315 # we want only snps
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316 # so, no '-' characters allowed, and chrStart must be same as chrEnd
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317 if '-' not in allele_normal and '-' not in allele_tumor and chrStart == chrEnd:
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318 trinuc_ref = refSequence[chrStart-1:chrStart+2]
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319 if not trinuc_ref in VALID_TRINUC:
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320 continue # skip ref trinuc with invalid characters
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321 # only consider positions where ref allele in tsv matches the nucleotide in our reference
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322 if allele_ref == trinuc_ref[1]:
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323 trinuc_normal = refSequence[chrStart-1] + allele_normal + refSequence[chrStart+1]
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parents:
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324 trinuc_tumor = refSequence[chrStart-1] + allele_tumor + refSequence[chrStart+1]
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325 if not trinuc_normal in VALID_TRINUC or not trinuc_tumor in VALID_TRINUC:
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326 continue # skip if mutation contains invalid char
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parents:
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327 key = (trinuc_normal,trinuc_tumor)
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parents:
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328 if key not in TRINUC_TRANSITION_COUNT:
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thondeboer
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329 TRINUC_TRANSITION_COUNT[key] = 0
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330 TRINUC_TRANSITION_COUNT[key] += 1
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331 SNP_COUNT += 1
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parents:
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332 key2 = (allele_normal,allele_tumor)
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parents:
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333 if key2 not in SNP_TRANSITION_COUNT:
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parents:
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334 SNP_TRANSITION_COUNT[key2] = 0
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parents:
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335 SNP_TRANSITION_COUNT[key2] += 1
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parents:
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336
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337 if IS_VCF:
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parents:
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338 myPopFreq = VCF_DEFAULT_POP_FREQ
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thondeboer
parents:
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339 if ';CAF=' in vcf_info:
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parents:
diff changeset
340 cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
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341 if ',' in cafStr:
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parents:
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342 myPopFreq = float(cafStr[5:].split(',')[1])
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thondeboer
parents:
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343 VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
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thondeboer
parents:
diff changeset
344 else:
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thondeboer
parents:
diff changeset
345 VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
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thondeboer
parents:
diff changeset
346 TOTAL_DONORS[donor_id] = True
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thondeboer
parents:
diff changeset
347 else:
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thondeboer
parents:
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348 print '\nError: ref allele in variant call does not match reference.\n'
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thondeboer
parents:
diff changeset
349 exit(1)
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thondeboer
parents:
diff changeset
350
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thondeboer
parents:
diff changeset
351 # now let's look for indels...
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thondeboer
parents:
diff changeset
352 if '-' in allele_normal: len_normal = 0
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thondeboer
parents:
diff changeset
353 else: len_normal = len(allele_normal)
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thondeboer
parents:
diff changeset
354 if '-' in allele_tumor: len_tumor = 0
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thondeboer
parents:
diff changeset
355 else: len_tumor = len(allele_tumor)
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thondeboer
parents:
diff changeset
356 if len_normal != len_tumor:
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thondeboer
parents:
diff changeset
357 indel_len = len_tumor - len_normal
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thondeboer
parents:
diff changeset
358 if indel_len not in INDEL_COUNT:
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thondeboer
parents:
diff changeset
359 INDEL_COUNT[indel_len] = 0
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thondeboer
parents:
diff changeset
360 INDEL_COUNT[indel_len] += 1
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thondeboer
parents:
diff changeset
361
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thondeboer
parents:
diff changeset
362 if IS_VCF:
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thondeboer
parents:
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363 myPopFreq = VCF_DEFAULT_POP_FREQ
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thondeboer
parents:
diff changeset
364 if ';CAF=' in vcf_info:
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thondeboer
parents:
diff changeset
365 cafStr = re.findall(r";CAF=.*?(?=;)",vcf_info)[0]
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thondeboer
parents:
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366 if ',' in cafStr:
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thondeboer
parents:
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367 myPopFreq = float(cafStr[5:].split(',')[1])
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thondeboer
parents:
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368 VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor,myPopFreq))
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thondeboer
parents:
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369 else:
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thondeboer
parents:
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370 VDAT_COMMON.append((chrStart,allele_ref,allele_normal,allele_tumor))
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thondeboer
parents:
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371 TOTAL_DONORS[donor_id] = True
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thondeboer
parents:
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372 f.close()
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thondeboer
parents:
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373
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thondeboer
parents:
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374 # if we didn't find anything, skip ahead along to the next reference sequence
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thondeboer
parents:
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375 if not len(VDAT_COMMON):
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thondeboer
parents:
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376 print 'Found no variants for this reference, moving along...'
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thondeboer
parents:
diff changeset
377 continue
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thondeboer
parents:
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378
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thondeboer
parents:
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379 #
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thondeboer
parents:
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380 # identify common mutations
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thondeboer
parents:
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381 #
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thondeboer
parents:
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382 percentile_var = 95
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thondeboer
parents:
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383 if IS_VCF:
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thondeboer
parents:
diff changeset
384 minVal = np.percentile([n[4] for n in VDAT_COMMON],percentile_var)
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thondeboer
parents:
diff changeset
385 for k in sorted(VDAT_COMMON):
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thondeboer
parents:
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386 if k[4] >= minVal:
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thondeboer
parents:
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387 COMMON_VARIANTS.append((refName,k[0],k[1],k[3],k[4]))
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thondeboer
parents:
diff changeset
388 VDAT_COMMON = {(n[0],n[1],n[2],n[3]):n[4] for n in VDAT_COMMON}
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thondeboer
parents:
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389 else:
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thondeboer
parents:
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390 N_DONORS = len(TOTAL_DONORS)
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thondeboer
parents:
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391 VDAT_COMMON = list_2_countDict(VDAT_COMMON)
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thondeboer
parents:
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392 minVal = int(np.percentile(VDAT_COMMON.values(),percentile_var))
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thondeboer
parents:
diff changeset
393 for k in sorted(VDAT_COMMON.keys()):
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thondeboer
parents:
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394 if VDAT_COMMON[k] >= minVal:
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thondeboer
parents:
diff changeset
395 COMMON_VARIANTS.append((refName,k[0],k[1],k[3],VDAT_COMMON[k]/float(N_DONORS)))
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thondeboer
parents:
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396
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thondeboer
parents:
diff changeset
397 #
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thondeboer
parents:
diff changeset
398 # identify areas that have contained significantly higher random mutation rates
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thondeboer
parents:
diff changeset
399 #
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thondeboer
parents:
diff changeset
400 dist_thresh = 2000
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thondeboer
parents:
diff changeset
401 percentile_clust = 97
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thondeboer
parents:
diff changeset
402 qptn = 1000
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thondeboer
parents:
diff changeset
403 # identify regions with disproportionately more variants in them
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thondeboer
parents:
diff changeset
404 VARIANT_POS = sorted([n[0] for n in VDAT_COMMON.keys()])
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thondeboer
parents:
diff changeset
405 clustered_pos = clusterList(VARIANT_POS,dist_thresh)
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thondeboer
parents:
diff changeset
406 byLen = [(len(clustered_pos[i]),min(clustered_pos[i]),max(clustered_pos[i]),i) for i in xrange(len(clustered_pos))]
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thondeboer
parents:
diff changeset
407 #byLen = sorted(byLen,reverse=True)
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thondeboer
parents:
diff changeset
408 #minLen = int(np.percentile([n[0] for n in byLen],percentile_clust))
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thondeboer
parents:
diff changeset
409 #byLen = [n for n in byLen if n[0] >= minLen]
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thondeboer
parents:
diff changeset
410 candidate_regions = []
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thondeboer
parents:
diff changeset
411 for n in byLen:
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thondeboer
parents:
diff changeset
412 bi = int((n[1]-dist_thresh)/float(qptn))*qptn
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thondeboer
parents:
diff changeset
413 bf = int((n[2]+dist_thresh)/float(qptn))*qptn
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thondeboer
parents:
diff changeset
414 candidate_regions.append((n[0]/float(bf-bi),max([0,bi]),min([len(refSequence),bf])))
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thondeboer
parents:
diff changeset
415 minVal = np.percentile([n[0] for n in candidate_regions],percentile_clust)
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thondeboer
parents:
diff changeset
416 for n in candidate_regions:
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thondeboer
parents:
diff changeset
417 if n[0] >= minVal:
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thondeboer
parents:
diff changeset
418 HIGH_MUT_REGIONS.append((refName,n[1],n[2],n[0]))
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thondeboer
parents:
diff changeset
419 # collapse overlapping regions
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thondeboer
parents:
diff changeset
420 for i in xrange(len(HIGH_MUT_REGIONS)-1,0,-1):
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thondeboer
parents:
diff changeset
421 if HIGH_MUT_REGIONS[i-1][2] >= HIGH_MUT_REGIONS[i][1] and HIGH_MUT_REGIONS[i-1][0] == HIGH_MUT_REGIONS[i][0]:
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thondeboer
parents:
diff changeset
422 avgMutRate = 0.5*HIGH_MUT_REGIONS[i-1][3]+0.5*HIGH_MUT_REGIONS[i][3] # not accurate, but I'm lazy
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thondeboer
parents:
diff changeset
423 HIGH_MUT_REGIONS[i-1] = (HIGH_MUT_REGIONS[i-1][0], HIGH_MUT_REGIONS[i-1][1], HIGH_MUT_REGIONS[i][2], avgMutRate)
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thondeboer
parents:
diff changeset
424 del HIGH_MUT_REGIONS[i]
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thondeboer
parents:
diff changeset
425
6e75a84e9338 planemo upload commit e96b43f96afce6a7b7dfd4499933aad7d05c955e-dirty
thondeboer
parents:
diff changeset
426 #
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thondeboer
parents:
diff changeset
427 # if we didn't count ref trinucs because we found file, read in ref counts from file now
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thondeboer
parents:
diff changeset
428 #
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thondeboer
parents:
diff changeset
429 if os.path.isfile(REF+'.trinucCounts'):
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thondeboer
parents:
diff changeset
430 print 'reading pre-computed trinuc counts...'
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thondeboer
parents:
diff changeset
431 f = open(REF+'.trinucCounts','r')
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thondeboer
parents:
diff changeset
432 for line in f:
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thondeboer
parents:
diff changeset
433 splt = line.strip().split('\t')
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thondeboer
parents:
diff changeset
434 TRINUC_REF_COUNT[splt[0]] = int(splt[1])
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thondeboer
parents:
diff changeset
435 f.close()
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thondeboer
parents:
diff changeset
436 # otherwise, save trinuc counts to file, if desired
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thondeboer
parents:
diff changeset
437 elif SAVE_TRINUC:
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thondeboer
parents:
diff changeset
438 if MYBED != None:
6e75a84e9338 planemo upload commit e96b43f96afce6a7b7dfd4499933aad7d05c955e-dirty
thondeboer
parents:
diff changeset
439 print 'unable to save trinuc counts to file because using input bed region...'
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thondeboer
parents:
diff changeset
440 else:
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thondeboer
parents:
diff changeset
441 print 'saving trinuc counts to file...'
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thondeboer
parents:
diff changeset
442 f = open(REF+'.trinucCounts','w')
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thondeboer
parents:
diff changeset
443 for trinuc in sorted(TRINUC_REF_COUNT.keys()):
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thondeboer
parents:
diff changeset
444 f.write(trinuc+'\t'+str(TRINUC_REF_COUNT[trinuc])+'\n')
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thondeboer
parents:
diff changeset
445 f.close()
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thondeboer
parents:
diff changeset
446
6e75a84e9338 planemo upload commit e96b43f96afce6a7b7dfd4499933aad7d05c955e-dirty
thondeboer
parents:
diff changeset
447 #
6e75a84e9338 planemo upload commit e96b43f96afce6a7b7dfd4499933aad7d05c955e-dirty
thondeboer
parents:
diff changeset
448 # if using an input bed region, make necessary adjustments to trinuc ref counts based on the bed region trinuc counts
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thondeboer
parents:
diff changeset
449 #
6e75a84e9338 planemo upload commit e96b43f96afce6a7b7dfd4499933aad7d05c955e-dirty
thondeboer
parents:
diff changeset
450 if MYBED != None:
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thondeboer
parents:
diff changeset
451 if MYBED[1] == True: # we are restricting our attention to bed regions, so ONLY use bed region trinuc counts
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thondeboer
parents:
diff changeset
452 TRINUC_REF_COUNT = TRINUC_BED_COUNT
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thondeboer
parents:
diff changeset
453 else: # we are only looking outside bed regions, so subtract bed region trinucs from entire reference trinucs
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thondeboer
parents:
diff changeset
454 for k in TRINUC_REF_COUNT.keys():
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thondeboer
parents:
diff changeset
455 if k in TRINUC_BED_COUNT:
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thondeboer
parents:
diff changeset
456 TRINUC_REF_COUNT[k] -= TRINUC_BED_COUNT[k]
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thondeboer
parents:
diff changeset
457
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thondeboer
parents:
diff changeset
458 # if for some reason we didn't find any valid input variants, exit gracefully...
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thondeboer
parents:
diff changeset
459 totalVar = SNP_COUNT + sum(INDEL_COUNT.values())
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thondeboer
parents:
diff changeset
460 if totalVar == 0:
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thondeboer
parents:
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461 print '\nError: No valid variants were found, model could not be created. (Are you using the correct reference?)\n'
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thondeboer
parents:
diff changeset
462 exit(1)
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thondeboer
parents:
diff changeset
463
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thondeboer
parents:
diff changeset
464 """ ##########################################################################
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thondeboer
parents:
diff changeset
465 ### COMPUTE PROBABILITIES ###
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thondeboer
parents:
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466 ########################################################################## """
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thondeboer
parents:
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467
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thondeboer
parents:
diff changeset
468
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thondeboer
parents:
diff changeset
469 #for k in sorted(TRINUC_REF_COUNT.keys()):
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thondeboer
parents:
diff changeset
470 # print k, TRINUC_REF_COUNT[k]
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thondeboer
parents:
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471 #
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thondeboer
parents:
diff changeset
472 #for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
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thondeboer
parents:
diff changeset
473 # print k, TRINUC_TRANSITION_COUNT[k]
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thondeboer
parents:
diff changeset
474
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thondeboer
parents:
diff changeset
475 # frequency that each trinuc mutated into anything else
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thondeboer
parents:
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476 TRINUC_MUT_PROB = {}
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thondeboer
parents:
diff changeset
477 # frequency that a trinuc mutates into another trinuc, given that it mutated
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thondeboer
parents:
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478 TRINUC_TRANS_PROBS = {}
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thondeboer
parents:
diff changeset
479 # frequency of snp transitions, given a snp occurs.
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thondeboer
parents:
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480 SNP_TRANS_FREQ = {}
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thondeboer
parents:
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481
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thondeboer
parents:
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482 for trinuc in sorted(TRINUC_REF_COUNT.keys()):
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thondeboer
parents:
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483 myCount = 0
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thondeboer
parents:
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484 for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
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thondeboer
parents:
diff changeset
485 if k[0] == trinuc:
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thondeboer
parents:
diff changeset
486 myCount += TRINUC_TRANSITION_COUNT[k]
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thondeboer
parents:
diff changeset
487 TRINUC_MUT_PROB[trinuc] = myCount / float(TRINUC_REF_COUNT[trinuc])
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thondeboer
parents:
diff changeset
488 for k in sorted(TRINUC_TRANSITION_COUNT.keys()):
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thondeboer
parents:
diff changeset
489 if k[0] == trinuc:
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thondeboer
parents:
diff changeset
490 TRINUC_TRANS_PROBS[k] = TRINUC_TRANSITION_COUNT[k] / float(myCount)
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thondeboer
parents:
diff changeset
491
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thondeboer
parents:
diff changeset
492 for n1 in VALID_NUCL:
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thondeboer
parents:
diff changeset
493 rollingTot = sum([SNP_TRANSITION_COUNT[(n1,n2)] for n2 in VALID_NUCL if (n1,n2) in SNP_TRANSITION_COUNT])
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thondeboer
parents:
diff changeset
494 for n2 in VALID_NUCL:
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thondeboer
parents:
diff changeset
495 key2 = (n1,n2)
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thondeboer
parents:
diff changeset
496 if key2 in SNP_TRANSITION_COUNT:
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thondeboer
parents:
diff changeset
497 SNP_TRANS_FREQ[key2] = SNP_TRANSITION_COUNT[key2] / float(rollingTot)
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thondeboer
parents:
diff changeset
498
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thondeboer
parents:
diff changeset
499 # compute average snp and indel frequencies
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thondeboer
parents:
diff changeset
500 SNP_FREQ = SNP_COUNT/float(totalVar)
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thondeboer
parents:
diff changeset
501 AVG_INDEL_FREQ = 1.-SNP_FREQ
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thondeboer
parents:
diff changeset
502 INDEL_FREQ = {k:(INDEL_COUNT[k]/float(totalVar))/AVG_INDEL_FREQ for k in INDEL_COUNT.keys()}
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thondeboer
parents:
diff changeset
503 if MYBED != None:
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thondeboer
parents:
diff changeset
504 if MYBED[1] == True:
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thondeboer
parents:
diff changeset
505 AVG_MUT_RATE = totalVar/float(getTrackLen(MYBED[0]))
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thondeboer
parents:
diff changeset
506 else:
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thondeboer
parents:
diff changeset
507 AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN - getTrackLen(MYBED[0]))
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thondeboer
parents:
diff changeset
508 else:
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thondeboer
parents:
diff changeset
509 AVG_MUT_RATE = totalVar/float(TOTAL_REFLEN)
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thondeboer
parents:
diff changeset
510
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thondeboer
parents:
diff changeset
511 #
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thondeboer
parents:
diff changeset
512 # if values weren't found in data, appropriately append null entries
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thondeboer
parents:
diff changeset
513 #
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thondeboer
parents:
diff changeset
514 printTrinucWarning = False
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thondeboer
parents:
diff changeset
515 for trinuc in VALID_TRINUC:
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thondeboer
parents:
diff changeset
516 trinuc_mut = [trinuc[0]+n+trinuc[2] for n in VALID_NUCL if n != trinuc[1]]
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thondeboer
parents:
diff changeset
517 if trinuc not in TRINUC_MUT_PROB:
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thondeboer
parents:
diff changeset
518 TRINUC_MUT_PROB[trinuc] = 0.
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thondeboer
parents:
diff changeset
519 printTrinucWarning = True
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thondeboer
parents:
diff changeset
520 for trinuc2 in trinuc_mut:
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thondeboer
parents:
diff changeset
521 if (trinuc,trinuc2) not in TRINUC_TRANS_PROBS:
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thondeboer
parents:
diff changeset
522 TRINUC_TRANS_PROBS[(trinuc,trinuc2)] = 0.
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thondeboer
parents:
diff changeset
523 printTrinucWarning = True
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thondeboer
parents:
diff changeset
524 if printTrinucWarning:
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thondeboer
parents:
diff changeset
525 print 'Warning: Some trinucleotides transitions were not encountered in the input dataset, probabilities of 0.0 have been assigned to these events.'
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thondeboer
parents:
diff changeset
526
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thondeboer
parents:
diff changeset
527 #
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thondeboer
parents:
diff changeset
528 # print some stuff
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thondeboer
parents:
diff changeset
529 #
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thondeboer
parents:
diff changeset
530 for k in sorted(TRINUC_MUT_PROB.keys()):
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thondeboer
parents:
diff changeset
531 print 'p('+k+' mutates) =',TRINUC_MUT_PROB[k]
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thondeboer
parents:
diff changeset
532
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thondeboer
parents:
diff changeset
533 for k in sorted(TRINUC_TRANS_PROBS.keys()):
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thondeboer
parents:
diff changeset
534 print 'p('+k[0]+' --> '+k[1]+' | '+k[0]+' mutates) =',TRINUC_TRANS_PROBS[k]
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thondeboer
parents:
diff changeset
535
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thondeboer
parents:
diff changeset
536 for k in sorted(INDEL_FREQ.keys()):
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thondeboer
parents:
diff changeset
537 if k > 0:
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thondeboer
parents:
diff changeset
538 print 'p(ins length = '+str(abs(k))+' | indel occurs) =',INDEL_FREQ[k]
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thondeboer
parents:
diff changeset
539 else:
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thondeboer
parents:
diff changeset
540 print 'p(del length = '+str(abs(k))+' | indel occurs) =',INDEL_FREQ[k]
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thondeboer
parents:
diff changeset
541
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thondeboer
parents:
diff changeset
542 for k in sorted(SNP_TRANS_FREQ.keys()):
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thondeboer
parents:
diff changeset
543 print 'p('+k[0]+' --> '+k[1]+' | SNP occurs) =',SNP_TRANS_FREQ[k]
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thondeboer
parents:
diff changeset
544
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thondeboer
parents:
diff changeset
545 #for n in COMMON_VARIANTS:
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thondeboer
parents:
diff changeset
546 # print n
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thondeboer
parents:
diff changeset
547
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thondeboer
parents:
diff changeset
548 #for n in HIGH_MUT_REGIONS:
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thondeboer
parents:
diff changeset
549 # print n
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thondeboer
parents:
diff changeset
550
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thondeboer
parents:
diff changeset
551 print 'p(snp) =',SNP_FREQ
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thondeboer
parents:
diff changeset
552 print 'p(indel) =',AVG_INDEL_FREQ
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thondeboer
parents:
diff changeset
553 print 'overall average mut rate:',AVG_MUT_RATE
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thondeboer
parents:
diff changeset
554 print 'total variants processed:',totalVar
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thondeboer
parents:
diff changeset
555
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thondeboer
parents:
diff changeset
556 #
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thondeboer
parents:
diff changeset
557 # save variables to file
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thondeboer
parents:
diff changeset
558 #
6e75a84e9338 planemo upload commit e96b43f96afce6a7b7dfd4499933aad7d05c955e-dirty
thondeboer
parents:
diff changeset
559 if SKIP_COMMON:
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thondeboer
parents:
diff changeset
560 OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
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thondeboer
parents:
diff changeset
561 'SNP_FREQ':SNP_FREQ,
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thondeboer
parents:
diff changeset
562 'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
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thondeboer
parents:
diff changeset
563 'INDEL_FREQ':INDEL_FREQ,
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thondeboer
parents:
diff changeset
564 'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
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thondeboer
parents:
diff changeset
565 'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS}
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566 else:
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567 OUT_DICT = {'AVG_MUT_RATE':AVG_MUT_RATE,
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568 'SNP_FREQ':SNP_FREQ,
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569 'SNP_TRANS_FREQ':SNP_TRANS_FREQ,
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570 'INDEL_FREQ':INDEL_FREQ,
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571 'TRINUC_MUT_PROB':TRINUC_MUT_PROB,
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572 'TRINUC_TRANS_PROBS':TRINUC_TRANS_PROBS,
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573 'COMMON_VARIANTS':COMMON_VARIANTS,
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574 'HIGH_MUT_REGIONS':HIGH_MUT_REGIONS}
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575 pickle.dump( OUT_DICT, open( OUT_PICKLE, "wb" ) )
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576
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577
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578 if __name__ == "__main__":
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579 main()
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580
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581
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582
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583