Mercurial > repos > mvdbeek > mismatch_frequencies

comparison mismatch_frequencies.py @ 6:a7bf987b8cc4

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author mvdbeek
date Tue, 27 Jan 2015 12:30:07 -0500
parents e8ebe5132737
children 270681625775
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5:e554c1973398 6:a7bf987b8cc4
1 import pysam, re, string
2 import matplotlib.pyplot as plt
3 import pandas as pd
4 from collections import defaultdict
5 from collections import OrderedDict
6 import argparse
7
8 class MismatchFrequencies:
9 '''Iterate over a SAM/BAM alignment file, collecting reads with mismatches. One
10 class instance per alignment file. The result_dict attribute will contain a
11 nested dictionary with name, readlength and mismatch count.'''
12 def __init__(self, result_dict={}, alignment_file=None, name="name", minimal_readlength=21, maximal_readlength=21,
13 number_of_allowed_mismatches=1, ignore_5p_nucleotides=0, ignore_3p_nucleotides=0):
14
15 self.result_dict = result_dict
16 self.name = name
17 self.minimal_readlength = minimal_readlength
18 self.maximal_readlength = maximal_readlength
19 self.number_of_allowed_mismatches = number_of_allowed_mismatches
20 self.ignore_5p_nucleotides = ignore_5p_nucleotides
21 self.ignore_3p_nucleotides = ignore_3p_nucleotides
22
23 if alignment_file:
24 self.pysam_alignment = pysam.Samfile(alignment_file)
25 result_dict[name]=self.get_mismatches(self.pysam_alignment, minimal_readlength, maximal_readlength)
26
27 def get_mismatches(self, pysam_alignment, minimal_readlength, maximal_readlength):
28 mismatch_dict = defaultdict(int)
29 len_dict={}
30 for i in range(minimal_readlength, maximal_readlength+1):
31 len_dict[i]=mismatch_dict.copy()
32 for alignedread in pysam_alignment:
33 if self.read_is_valid(alignedread, minimal_readlength, maximal_readlength):
34 len_dict[int(alignedread.rlen)]['total_mapped'] += 1
35 MD=alignedread.opt('MD')
36 if self.read_has_mismatch(alignedread, self.number_of_allowed_mismatches):
37 (ref_base, mismatch_base)=self.read_to_reference_mismatch(MD, alignedread.seq, alignedread.is_reverse)
38 if ref_base == None:
39 continue
40 else:
41 for i, base in enumerate(ref_base):
42 len_dict[int(alignedread.rlen)][ref_base[i]+' to '+mismatch_base[i]] += 1
43 return len_dict
44
45 def read_is_valid(self, read, min_readlength, max_readlength):
46 '''Filter out reads that are unmatched, too short or
47 too long or that contian insertions'''
48 if read.is_unmapped:
49 return False
50 if read.rlen < min_readlength:
51 return False
52 if read.rlen > max_readlength:
53 return False
54 else:
55 return True
56
57 def read_has_mismatch(self, read, number_of_allowed_mismatches=1):
58 '''keep only reads with one mismatch. Could be simplified'''
59 NM=read.opt('NM')
60 if NM <1: #filter out reads with no mismatch
61 return False
62 if NM >number_of_allowed_mismatches: #filter out reads with more than 1 mismtach
63 return False
64 else:
65 return True
66
67 def mismatch_in_allowed_region(self, readseq, mismatch_position):
68 '''
69 >>> M = MismatchFrequencies()
70 >>> readseq = 'AAAAAA'
71 >>> mismatch_position = 2
72 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
73 True
74 >>> M = MismatchFrequencies(ignore_3p_nucleotides=2, ignore_5p_nucleotides=2)
75 >>> readseq = 'AAAAAA'
76 >>> mismatch_position = 1
77 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
78 False
79 >>> readseq = 'AAAAAA'
80 >>> mismatch_position = 4
81 >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
82 False
83 '''
84 mismatch_position+=1 # To compensate for starting the count at 0
85 five_p = self.ignore_5p_nucleotides
86 three_p = self.ignore_3p_nucleotides
87 if any([five_p > 0, three_p > 0]):
88 if any([mismatch_position <= five_p,
89 mismatch_position >= (len(readseq)+1-three_p)]): #Again compensate for starting the count at 0
90 return False
91 else:
92 return True
93 else:
94 return True
95
96 def read_to_reference_mismatch(self, MD, readseq, is_reverse):
97 '''
98 This is where the magic happens. The MD tag contains SNP and indel information,
99 without looking to the genome sequence. This is a typical MD tag: 3C0G2A6.
100 3 bases of the read align to the reference, followed by a mismatch, where the
101 reference base is C, followed by 10 bases aligned to the reference.
102 suppose a reference 'CTTCGATAATCCTT'
103 ||| || ||||||
104 and a read 'CTTATATTATCCTT'.
105 This situation is represented by the above MD tag.
106 Given MD tag and read sequence this function returns the reference base C, G and A,
107 and the mismatched base A, T, T.
108 >>> M = MismatchFrequencies()
109 >>> MD='3C0G2A7'
110 >>> seq='CTTATATTATCCTT'
111 >>> result=M.read_to_reference_mismatch(MD, seq, is_reverse=False)
112 >>> result[0]=="CGA"
113 True
114 >>> result[1]=="ATT"
115 True
116 >>>
117 '''
118 search=re.finditer('[ATGC]',MD)
119 if '^' in MD:
120 print 'WARNING insertion detected, mismatch calling skipped for this read!!!'
121 return (None, None)
122 start_index=0 # refers to the leading integer of the MD string before an edited base
123 current_position=0 # position of the mismatched nucleotide in the MD tag string
124 mismatch_position=0 # position of edited base in current read
125 reference_base=""
126 mismatched_base=""
127 for result in search:
128 current_position=result.start()
129 mismatch_position=mismatch_position+1+int(MD[start_index:current_position]) #converts the leading characters before an edited base into integers
130 start_index=result.end()
131 reference_base+=MD[result.end()-1]
132 mismatched_base+=readseq[mismatch_position-1]
133 if is_reverse:
134 reference_base=reverseComplement(reference_base)
135 mismatched_base=reverseComplement(mismatched_base)
136 if mismatched_base=='N':
137 return (None, None)
138 if self.mismatch_in_allowed_region(readseq, mismatch_position):
139 return (reference_base, mismatched_base)
140 else:
141 return (None, None)
142
143
144 def reverseComplement(sequence):
145 '''do a reverse complement of DNA base.
146 >>> reverseComplement('ATGC')=='GCAT'
147 True
148 >>>
149 '''
150 sequence=sequence.upper()
151 complement = string.maketrans('ATCGN', 'TAGCN')
152 return sequence.upper().translate(complement)[::-1]
153
154 def barplot(df, library, axes):
155 df.plot(kind='bar', ax=axes, subplots=False,\
156 stacked=False, legend='test',\
157 title='Mismatches in TE small RNAs from {0}'.format(library))
158
159 def result_dict_to_df(result_dict):
160 mismatches = []
161 libraries = []
162 for mismatch, library in result_dict.iteritems():
163 mismatches.append(mismatch)
164 libraries.append(pd.DataFrame.from_dict(library, orient='index'))
165 df=pd.concat(libraries, keys=mismatches)
166 df.index.names = ['library', 'readsize']
167 return df
168
169 def df_to_tab(df, output):
170 df.to_csv(output, sep='\t')
171
172 def plot_result(result_dict, args):
173 names=args.name
174 nrows=len(names)/2+1
175 fig = plt.figure(figsize=(16,32))
176 for i,library in enumerate (names):
177 axes=fig.add_subplot(nrows,2,i+1)
178 library_dict=result_dict[library]
179 for length in library_dict.keys():
180 for mismatch in library_dict[length]:
181 if mismatch == 'total_mapped':
182 continue
183 library_dict[length][mismatch]=library_dict[length][mismatch]/float(library_dict[length]['total_mapped'])*100
184 del library_dict[length]['total_mapped']
185 df=pd.DataFrame(library_dict)
186 barplot(df, library, axes),
187 axes.set_ylabel('Percent of mapped reads with mismatches')
188 fig.savefig(args.output_pdf, format='pdf')
189
190 def setup_MismatchFrequencies(args):
191 resultDict=OrderedDict()
192 kw_list=[{'result_dict' : resultDict,
193 'alignment_file' :alignment_file,
194 'name' : name,
195 'minimal_readlength' : args.min,
196 'maximal_readlength' : args.max,
197 'number_of_allowed_mismatches' : args.n_mm,
198 'ignore_5p_nucleotides' : args.five_p,
199 'ignore_3p_nucleotides' : args.three_p}
200 for alignment_file, name in zip(args.input, args.name)]
201 return (kw_list, resultDict)
202
203 def run_MismatchFrequencies(args):
204 kw_list, resultDict=setup_MismatchFrequencies(args)
205 [MismatchFrequencies(**kw_dict) for kw_dict in kw_list]
206 return resultDict
207
208 def main():
209 result_dict=run_MismatchFrequencies(args)
210 df=result_dict_to_df(result_dict)
211 plot_result(result_dict, args)
212 df_to_tab(df, args.output_tab)
213
214 if __name__ == "__main__":
215
216 parser = argparse.ArgumentParser(description='Produce mismatch statistics for BAM/SAM alignment files.')
217 parser.add_argument('--input', nargs='*', help='Input files in SAM/BAM format')
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')
219 parser.add_argument('--output_pdf', help='Output filename for graph')
220 parser.add_argument('--output_tab', help='Output filename for table')
221 parser.add_argument('--min', '--minimal_readlength', type=int, help='minimum readlength')
222 parser.add_argument('--max', '--maximal_readlength', type=int, help='maximum readlength')
223 parser.add_argument('--n_mm', '--number_allowed_mismatches', type=int, default=1, help='discard reads with more than n mismatches')
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')
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')
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'])
227 args = parser.parse_args()
228 main()
229