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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 from collections import defaultdict
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5 from collections import OrderedDict
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6 import argparse
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7
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8 class MismatchFrequencies:
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9 '''Iterate over a SAM/BAM alignment file, collecting reads with mismatches. One
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10 class instance per alignment file. The result_dict attribute will contain a
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11 nested dictionary with name, readlength and mismatch count.'''
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12 def __init__(self, result_dict={}, alignment_file=None, name="name", minimal_readlength=21, maximal_readlength=21,
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13 number_of_allowed_mismatches=1, ignore_5p_nucleotides=0, ignore_3p_nucleotides=0):
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14
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15 self.result_dict = result_dict
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16 self.name = name
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17 self.minimal_readlength = minimal_readlength
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18 self.maximal_readlength = maximal_readlength
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19 self.number_of_allowed_mismatches = number_of_allowed_mismatches
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20 self.ignore_5p_nucleotides = ignore_5p_nucleotides
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21 self.ignore_3p_nucleotides = ignore_3p_nucleotides
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22
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23 if alignment_file:
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24 self.pysam_alignment = pysam.Samfile(alignment_file)
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25 result_dict[name]=self.get_mismatches(self.pysam_alignment, minimal_readlength, maximal_readlength)
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26
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27 def get_mismatches(self, pysam_alignment, minimal_readlength, maximal_readlength):
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28 mismatch_dict = defaultdict(int)
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29 len_dict={}
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30 for i in range(minimal_readlength, maximal_readlength+1):
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31 len_dict[i]=mismatch_dict.copy()
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32 for alignedread in pysam_alignment:
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33 if self.read_is_valid(alignedread, minimal_readlength, maximal_readlength):
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34 len_dict[int(alignedread.rlen)]['total_mapped'] += 1
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35 MD=alignedread.opt('MD')
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36 if self.read_has_mismatch(alignedread, self.number_of_allowed_mismatches):
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37 (ref_base, mismatch_base)=self.read_to_reference_mismatch(MD, alignedread.seq, alignedread.is_reverse)
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38 if ref_base == None:
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39 continue
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40 else:
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41 for i, base in enumerate(ref_base):
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42 len_dict[int(alignedread.rlen)][ref_base[i]+' to '+mismatch_base[i]] += 1
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43 return len_dict
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44
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45 def read_is_valid(self, read, min_readlength, max_readlength):
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46 '''Filter out reads that are unmatched, too short or
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47 too long or that contian insertions'''
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48 if read.is_unmapped:
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49 return False
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50 if read.rlen < min_readlength:
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51 return False
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52 if read.rlen > max_readlength:
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53 return False
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54 else:
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55 return True
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56
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57 def read_has_mismatch(self, read, number_of_allowed_mismatches=1):
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58 '''keep only reads with one mismatch. Could be simplified'''
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59 NM=read.opt('NM')
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60 if NM <1: #filter out reads with no mismatch
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61 return False
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62 if NM >number_of_allowed_mismatches: #filter out reads with more than 1 mismtach
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63 return False
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64 else:
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65 return True
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66
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67 def mismatch_in_allowed_region(self, readseq, mismatch_position):
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68 '''
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69 >>> M = MismatchFrequencies()
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70 >>> readseq = 'AAAAAA'
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71 >>> mismatch_position = 2
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72 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
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73 True
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74 >>> M = MismatchFrequencies(ignore_3p_nucleotides=2, ignore_5p_nucleotides=2)
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75 >>> readseq = 'AAAAAA'
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76 >>> mismatch_position = 1
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77 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
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78 False
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79 >>> readseq = 'AAAAAA'
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80 >>> mismatch_position = 4
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81 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
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82 False
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83 '''
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84 mismatch_position+=1 # To compensate for starting the count at 0
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85 five_p = self.ignore_5p_nucleotides
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86 three_p = self.ignore_3p_nucleotides
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87 if any([five_p > 0, three_p > 0]):
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88 if any([mismatch_position <= five_p,
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89 mismatch_position >= (len(readseq)+1-three_p)]): #Again compensate for starting the count at 0
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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 else:
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94 return True
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95
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96 def read_to_reference_mismatch(self, MD, readseq, is_reverse):
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97 '''
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98 This is where the magic happens. The MD tag contains SNP and indel information,
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99 without looking to the genome sequence. This is a typical MD tag: 3C0G2A6.
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100 3 bases of the read align to the reference, followed by a mismatch, where the
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101 reference base is C, followed by 10 bases aligned to the reference.
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102 suppose a reference 'CTTCGATAATCCTT'
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103 ||| || ||||||
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104 and a read 'CTTATATTATCCTT'.
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105 This situation is represented by the above MD tag.
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106 Given MD tag and read sequence this function returns the reference base C, G and A,
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107 and the mismatched base A, T, T.
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108 >>> M = MismatchFrequencies()
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109 >>> MD='3C0G2A7'
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110 >>> seq='CTTATATTATCCTT'
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111 >>> result=M.read_to_reference_mismatch(MD, seq, is_reverse=False)
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112 >>> result[0]=="CGA"
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113 True
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114 >>> result[1]=="ATT"
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115 True
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116 >>>
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117 '''
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118 search=re.finditer('[ATGC]',MD)
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119 if '^' in MD:
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120 print 'WARNING insertion detected, mismatch calling skipped for this read!!!'
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121 return (None, None)
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122 start_index=0 # refers to the leading integer of the MD string before an edited base
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123 current_position=0 # position of the mismatched nucleotide in the MD tag string
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124 mismatch_position=0 # position of edited base in current read
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125 reference_base=""
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126 mismatched_base=""
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127 for result in search:
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128 current_position=result.start()
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129 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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130 start_index=result.end()
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131 reference_base+=MD[result.end()-1]
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132 mismatched_base+=readseq[mismatch_position-1]
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133 if is_reverse:
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134 reference_base=reverseComplement(reference_base)
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135 mismatched_base=reverseComplement(mismatched_base)
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136 if mismatched_base=='N':
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137 return (None, None)
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138 if self.mismatch_in_allowed_region(readseq, mismatch_position):
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139 return (reference_base, mismatched_base)
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140 else:
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141 return (None, None)
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142
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143
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144 def reverseComplement(sequence):
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145 '''do a reverse complement of DNA base.
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146 >>> reverseComplement('ATGC')=='GCAT'
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147 True
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148 >>>
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149 '''
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150 sequence=sequence.upper()
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151 complement = string.maketrans('ATCGN', 'TAGCN')
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152 return sequence.upper().translate(complement)[::-1]
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153
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154 def barplot(df, library, axes):
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155 df.plot(kind='bar', ax=axes, subplots=False,\
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156 stacked=False, legend='test',\
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157 title='Mismatches in TE small RNAs from {0}'.format(library))
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158
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159 def result_dict_to_df(result_dict):
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160 mismatches = []
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161 libraries = []
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162 for mismatch, library in result_dict.iteritems():
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163 mismatches.append(mismatch)
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164 libraries.append(pd.DataFrame.from_dict(library, orient='index'))
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165 df=pd.concat(libraries, keys=mismatches)
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166 df.index.names = ['library', 'readsize']
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167 return df
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168
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169 def df_to_tab(df, output):
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170 df.to_csv(output, sep='\t')
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171
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172 def plot_result(result_dict, args):
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173 names=args.name
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174 nrows=len(names)/2+1
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175 fig = plt.figure(figsize=(16,32))
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176 for i,library in enumerate (names):
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177 axes=fig.add_subplot(nrows,2,i+1)
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178 library_dict=result_dict[library]
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179 for length in library_dict.keys():
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180 for mismatch in library_dict[length]:
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181 if mismatch == 'total_mapped':
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182 continue
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183 library_dict[length][mismatch]=library_dict[length][mismatch]/float(library_dict[length]['total_mapped'])*100
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184 del library_dict[length]['total_mapped']
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185 df=pd.DataFrame(library_dict)
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186 barplot(df, library, axes),
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187 axes.set_ylabel('Percent of mapped reads with mismatches')
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188 fig.savefig(args.output_pdf, format='pdf')
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189
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190 def setup_MismatchFrequencies(args):
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191 resultDict=OrderedDict()
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192 kw_list=[{'result_dict' : resultDict,
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193 'alignment_file' :alignment_file,
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194 'name' : name,
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195 'minimal_readlength' : args.min,
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196 'maximal_readlength' : args.max,
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197 'number_of_allowed_mismatches' : args.n_mm,
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198 'ignore_5p_nucleotides' : args.five_p,
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199 'ignore_3p_nucleotides' : args.three_p}
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200 for alignment_file, name in zip(args.input, args.name)]
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201 return (kw_list, resultDict)
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202
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203 def run_MismatchFrequencies(args):
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204 kw_list, resultDict=setup_MismatchFrequencies(args)
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205 [MismatchFrequencies(**kw_dict) for kw_dict in kw_list]
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206 return resultDict
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207
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208 def main():
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209 result_dict=run_MismatchFrequencies(args)
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210 df=result_dict_to_df(result_dict)
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211 plot_result(result_dict, args)
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212 df_to_tab(df, args.output_tab)
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213
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214 if __name__ == "__main__":
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215
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216 parser = argparse.ArgumentParser(description='Produce mismatch statistics for BAM/SAM alignment files.')
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217 parser.add_argument('--input', nargs='*', help='Input files in SAM/BAM format')
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218 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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219 parser.add_argument('--output_pdf', help='Output filename for graph')
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220 parser.add_argument('--output_tab', help='Output filename for table')
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221 parser.add_argument('--min', '--minimal_readlength', type=int, help='minimum readlength')
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222 parser.add_argument('--max', '--maximal_readlength', type=int, help='maximum readlength')
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223 parser.add_argument('--n_mm', '--number_allowed_mismatches', type=int, default=1, help='discard reads with more than n mismatches')
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224 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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225 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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226 #args = parser.parse_args(['--input', '3mismatches_ago2ip.bam', '2mismatch.bam', '--name', 'Siomi1', 'Siomi2' , '--five_p', '3','--three_p','3','--output_pdf', 'out.pdf', '--output_tab', 'out.tab', '--min', '21', '--max', '21'])
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227 args = parser.parse_args()
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228 main()
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229
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