Mercurial > repos > mvdbeek > mismatch_frequencies

annotate mismatch_frequencies.py @ 19:f7da7f3e2c98

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author mvdbeek
date Sun, 24 May 2015 11:12:24 -0400
parents 848d799e6fe8
children 942464ea4211
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1 import pysam, re, string
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2 import matplotlib.pyplot as plt
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3 import pandas as pd
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4 import json
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5 from collections import defaultdict
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6 from collections import OrderedDict
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7 import argparse
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8 import itertools
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9
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10 class MismatchFrequencies:
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11 '''Iterate over a SAM/BAM alignment file, collecting reads with mismatches. One
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12 class instance per alignment file. The result_dict attribute will contain a
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13 nested dictionary with name, readlength and mismatch count.'''
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14 def __init__(self, result_dict={}, alignment_file=None, name="name", minimal_readlength=21,
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15 maximal_readlength=21,
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16 number_of_allowed_mismatches=1,
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17 ignore_5p_nucleotides=0,
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18 ignore_3p_nucleotides=0,
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19 possible_mismatches = [
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20 'AC', 'AG', 'AT',
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21 'CA', 'CG', 'CT',
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22 'GA', 'GC', 'GT',
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23 'TA', 'TC', 'TG'
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24 ]):
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25
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26 self.result_dict = result_dict
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27 self.name = name
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28 self.minimal_readlength = minimal_readlength
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29 self.maximal_readlength = maximal_readlength
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30 self.number_of_allowed_mismatches = number_of_allowed_mismatches
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31 self.ignore_5p_nucleotides = ignore_5p_nucleotides
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32 self.ignore_3p_nucleotides = ignore_3p_nucleotides
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33 self.possible_mismatches = possible_mismatches
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34
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35 if alignment_file:
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36 self.pysam_alignment = pysam.Samfile(alignment_file)
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37 self.references = self.pysam_alignment.references #names of fasta reference sequences
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38 result_dict[name]=self.get_mismatches(
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39 self.pysam_alignment,
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40 minimal_readlength,
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41 maximal_readlength,
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42 possible_mismatches
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43 )
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44
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45 def get_mismatches(self, pysam_alignment, minimal_readlength,
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46 maximal_readlength, possible_mismatches):
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47 mismatch_dict = defaultdict(int)
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48 rec_dd = lambda: defaultdict(rec_dd)
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49 len_dict = rec_dd()
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50 for alignedread in pysam_alignment:
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51 if self.read_is_valid(alignedread, minimal_readlength, maximal_readlength):
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52 chromosome = pysam_alignment.getrname(alignedread.rname)
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53 try:
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54 len_dict[int(alignedread.rlen)][chromosome]['total valid reads'] += 1
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55 except TypeError:
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56 len_dict[int(alignedread.rlen)][chromosome]['total valid reads'] = 1
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57 MD = alignedread.opt('MD')
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58 if self.read_has_mismatch(alignedread, self.number_of_allowed_mismatches):
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59 (ref_base, mismatch_base)=self.read_to_reference_mismatch(MD, alignedread.seq, alignedread.is_reverse)
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60 if ref_base == None:
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61 continue
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62 else:
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63 for i, base in enumerate(ref_base):
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64 if not ref_base[i]+mismatch_base[i] in possible_mismatches:
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65 continue
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66 try:
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67 len_dict[int(alignedread.rlen)][chromosome][ref_base[i]+mismatch_base[i]] += 1
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68 except TypeError:
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69 len_dict[int(alignedread.rlen)][chromosome][ref_base[i]+mismatch_base[i]] = 1
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70 return len_dict
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71
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72 def read_is_valid(self, read, min_readlength, max_readlength):
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73 '''Filter out reads that are unmatched, too short or
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74 too long or that contian insertions'''
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75 if read.is_unmapped:
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76 return False
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77 if read.rlen < min_readlength:
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78 return False
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79 if read.rlen > max_readlength:
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80 return False
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81 else:
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82 return True
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83
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84 def read_has_mismatch(self, read, number_of_allowed_mismatches=1):
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85 '''keep only reads with one mismatch. Could be simplified'''
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86 NM=read.opt('NM')
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87 if NM <1: #filter out reads with no mismatch
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88 return False
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89 if NM >number_of_allowed_mismatches: #filter out reads with more than 1 mismtach
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90 return False
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91 else:
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92 return True
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93
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94 def mismatch_in_allowed_region(self, readseq, mismatch_position):
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95 '''
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96 >>> M = MismatchFrequencies()
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97 >>> readseq = 'AAAAAA'
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98 >>> mismatch_position = 2
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99 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
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100 True
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101 >>> M = MismatchFrequencies(ignore_3p_nucleotides=2, ignore_5p_nucleotides=2)
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102 >>> readseq = 'AAAAAA'
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103 >>> mismatch_position = 1
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104 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
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105 False
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106 >>> readseq = 'AAAAAA'
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107 >>> mismatch_position = 4
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108 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
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109 False
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110 '''
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111 mismatch_position+=1 # To compensate for starting the count at 0
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112 five_p = self.ignore_5p_nucleotides
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113 three_p = self.ignore_3p_nucleotides
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114 if any([five_p > 0, three_p > 0]):
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115 if any([mismatch_position <= five_p,
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116 mismatch_position >= (len(readseq)+1-three_p)]): #Again compensate for starting the count at 0
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117 return False
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118 else:
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119 return True
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120 else:
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121 return True
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122
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123 def read_to_reference_mismatch(self, MD, readseq, is_reverse):
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124 '''
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125 This is where the magic happens. The MD tag contains SNP and indel information,
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126 without looking to the genome sequence. This is a typical MD tag: 3C0G2A6.
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127 3 bases of the read align to the reference, followed by a mismatch, where the
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128 reference base is C, followed by 10 bases aligned to the reference.
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129 suppose a reference 'CTTCGATAATCCTT'
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130 ||| || ||||||
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131 and a read 'CTTATATTATCCTT'.
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132 This situation is represented by the above MD tag.
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133 Given MD tag and read sequence this function returns the reference base C, G and A,
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134 and the mismatched base A, T, T.
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135 >>> M = MismatchFrequencies()
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136 >>> MD='3C0G2A7'
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137 >>> seq='CTTATATTATCCTT'
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138 >>> result=M.read_to_reference_mismatch(MD, seq, is_reverse=False)
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139 >>> result[0]=="CGA"
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140 True
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141 >>> result[1]=="ATT"
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142 True
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143 >>>
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144 '''
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145 search=re.finditer('[ATGC]',MD)
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146 if '^' in MD:
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147 print 'WARNING insertion detected, mismatch calling skipped for this read!!!'
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148 return (None, None)
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149 start_index=0 # refers to the leading integer of the MD string before an edited base
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150 current_position=0 # position of the mismatched nucleotide in the MD tag string
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151 mismatch_position=0 # position of edited base in current read
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152 reference_base=""
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153 mismatched_base=""
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154 for result in search:
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155 current_position=result.start()
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156 mismatch_position=mismatch_position+1+int(MD[start_index:current_position]) #converts the leading characters before an edited base into integers
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157 start_index=result.end()
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158 reference_base+=MD[result.end()-1]
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159 mismatched_base+=readseq[mismatch_position-1]
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160 if is_reverse:
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161 reference_base=reverseComplement(reference_base)
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162 mismatched_base=reverseComplement(mismatched_base)
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163 mismatch_position=len(readseq)-mismatch_position-1
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164 if mismatched_base=='N':
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165 return (None, None)
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166 if self.mismatch_in_allowed_region(readseq, mismatch_position):
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167 return (reference_base, mismatched_base)
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168 else:
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169 return (None, None)
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170
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171 def reverseComplement(sequence):
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172 '''do a reverse complement of DNA base.
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173 >>> reverseComplement('ATGC')=='GCAT'
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174 True
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175 >>>
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176 '''
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177 sequence=sequence.upper()
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178 complement = string.maketrans('ATCGN', 'TAGCN')
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179 return sequence.upper().translate(complement)[::-1]
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180
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181 def barplot(df, library, axes):
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182 df.plot(kind='bar', ax=axes, subplots=False,\
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183 stacked=False, legend='test',\
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184 title='Mismatch frequencies for {0}'.format(library))
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185
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186 def df_to_tab(df, output):
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187 df.to_csv(output, sep='\t')
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188
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189 def reduce_result(df, possible_mismatches):
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190 '''takes a pandas dataframe with full mismatch details and
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191 summarises the results for plotting.'''
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192 alignments = df['Alignment_file'].unique()
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193 readlengths = df['Readlength'].unique()
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194 combinations = itertools.product(*[alignments, readlengths]) #generate all possible combinations of readlength and alignment files
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195 reduced_dict = {}
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196 frames = []
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197 last_column = 3+len(possible_mismatches)
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198 for combination in combinations:
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199 library_subset = df[df['Alignment_file'] == combination[0]]
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200 library_readlength_subset = library_subset[library_subset['Readlength'] == combination[1]]
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201 sum_of_library_and_readlength = library_readlength_subset.iloc[:,3:last_column+1].sum()
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202 if not reduced_dict.has_key(combination[0]):
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203 reduced_dict[combination[0]] = {}
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204 reduced_dict[combination[0]][combination[1]] = sum_of_library_and_readlength.to_dict()
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205 return reduced_dict
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206
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207 def plot_result(reduced_dict, args):
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208 names=reduced_dict.keys()
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209 nrows=len(names)/2+1
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210 fig = plt.figure(figsize=(16,32))
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211 for i,library in enumerate (names):
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212 axes=fig.add_subplot(nrows,2,i+1)
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213 library_dict=reduced_dict[library]
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214 df=pd.DataFrame(library_dict)
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215 df.drop(['total aligned reads'], inplace=True)
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216 barplot(df, library, axes),
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217 axes.set_ylabel('Mismatch count / all valid reads * readlength')
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218 fig.savefig(args.output_pdf, format='pdf')
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219
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220 def format_result_dict(result_dict, chromosomes, possible_mismatches):
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221 '''Turn nested dictionary into preformatted tab seperated lines'''
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222 header = "Reference sequence\tAlignment_file\tReadlength\t" + "\t".join(
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223 possible_mismatches) + "\ttotal aligned reads"
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224 libraries = result_dict.keys()
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225 readlengths = result_dict[libraries[0]].keys()
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226 result = []
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227 for chromosome in chromosomes:
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228 for library in libraries:
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229 for readlength in readlengths:
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230 line = []
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231 line.extend([chromosome, library, readlength])
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232 try:
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233 line.extend([result_dict[library][readlength][chromosome].get(mismatch, 0) for mismatch in possible_mismatches])
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234 line.extend([result_dict[library][readlength][chromosome].get(u'total valid reads', 0)])
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235 except KeyError:
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236 line.extend([0 for mismatch in possible_mismatches])
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237 line.extend([0])
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238 result.append(line)
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239 df = pd.DataFrame(result, columns=header.split('\t'))
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240 last_column=3+len(possible_mismatches)
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241 df['mismatches/per aligned nucleotides'] = df.iloc[:,3:last_column].sum(1)/(df.iloc[:,last_column]*df['Readlength'])
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242 return df
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243
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244 def setup_MismatchFrequencies(args):
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245 resultDict=OrderedDict()
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246 kw_list=[{'result_dict' : resultDict,
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247 'alignment_file' :alignment_file,
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248 'name' : name,
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249 'minimal_readlength' : args.min,
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250 'maximal_readlength' : args.max,
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251 'number_of_allowed_mismatches' : args.n_mm,
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252 'ignore_5p_nucleotides' : args.five_p,
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253 'ignore_3p_nucleotides' : args.three_p,
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254 'possible_mismatches' : args.possible_mismatches }
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255 for alignment_file, name in zip(args.input, args.name)]
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256 return (kw_list, resultDict)
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257
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258 def nested_dict_to_df(dictionary):
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259 dictionary = {(outerKey, innerKey): values for outerKey, innerDict in dictionary.iteritems() for innerKey, values in innerDict.iteritems()}
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260 df=pd.DataFrame.from_dict(dictionary).transpose()
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261 df.index.names = ['Library', 'Readlength']
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262 return df
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263
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264 def run_MismatchFrequencies(args):
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265 kw_list, resultDict=setup_MismatchFrequencies(args)
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266 references = [MismatchFrequencies(**kw_dict).references for kw_dict in kw_list]
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267 return (resultDict, references[0])
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268
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269 def main():
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270 result_dict, references = run_MismatchFrequencies(args)
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271 df = format_result_dict(result_dict, references, args.possible_mismatches)
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272 reduced_dict = reduce_result(df, args.possible_mismatches)
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273 plot_result(reduced_dict, args)
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274 reduced_df = nested_dict_to_df(reduced_dict)
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275 df_to_tab(reduced_df, args.output_tab)
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276 if not args.expanded_output_tab == None:
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277 df_to_tab(df, args.expanded_output_tab)
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278 return reduced_dict
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279
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280 if __name__ == "__main__":
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281
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282 parser = argparse.ArgumentParser(description='Produce mismatch statistics for BAM/SAM alignment files.')
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283 parser.add_argument('--input', nargs='*', help='Input files in SAM/BAM format')
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284 parser.add_argument('--name', nargs='*', help='Name for input file to display in output file. Should have same length as the number of inputs')
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285 parser.add_argument('--output_pdf', help='Output filename for graph')
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286 parser.add_argument('--output_tab', help='Output filename for table')
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287 parser.add_argument('--expanded_output_tab', default=None, help='Output filename for table')
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288 parser.add_argument('--possible_mismatches', default=[
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289 'AC', 'AG', 'AT','CA', 'CG', 'CT', 'GA', 'GC', 'GT', 'TA', 'TC', 'TG'
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290 ], nargs='+', help='specify mismatches that should be counted for the mismatch frequency. The format is Reference base -> observed base, eg AG for A to G mismatches.')
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291 parser.add_argument('--min', '--minimal_readlength', type=int, help='minimum readlength')
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292 parser.add_argument('--max', '--maximal_readlength', type=int, help='maximum readlength')
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293 parser.add_argument('--n_mm', '--number_allowed_mismatches', type=int, default=1, help='discard reads with more than n mismatches')
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294 parser.add_argument('--five_p', '--ignore_5p_nucleotides', type=int, default=0, help='when calculating nucleotide mismatch frequencies ignore the first N nucleotides of the read')
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295 parser.add_argument('--three_p', '--ignore_3p_nucleotides', type=int, default=1, help='when calculating nucleotide mismatch frequencies ignore the last N nucleotides of the read')
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296 #args = parser.parse_args(['--input', '3mismatches_ago2ip_s2.bam', '3mismatches_ago2ip_ovary.bam','--possible_mismatches','AC','AG', 'CG', 'TG', 'CT','--name', 'Siomi1', 'Siomi2' , '--five_p', '3','--three_p','3','--output_pdf', 'out.pdf', '--output_tab', 'out.tab', '--expanded_output_tab', 'expanded.tab', '--min', '20', '--max', '22'])
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297 args = parser.parse_args()
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298 reduced_dict = main()
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299
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300