Mercurial > repos > artbio > small_rna_signatures

annotate signature.py @ 7:07771982ef9b draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 7276b6b73aef7af4058ad2c1e34c4557e9cccbe0
author artbio
date Sun, 10 Sep 2017 13:50:40 -0400
parents a35e6f9c1d34
children 8d3ca9652a5b
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a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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1 import argparse
a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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2 from collections import defaultdict
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4 import numpy
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6 import pysam
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9 def Parser():
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10 the_parser = argparse.ArgumentParser()
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11 the_parser.add_argument(
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12 '--input', action="store", type=str, help="bam alignment file")
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13 the_parser.add_argument(
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14 '--minquery', type=int,
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15 help="Minimum readsize of query reads (nt) - must be an integer")
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16 the_parser.add_argument(
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17 '--maxquery', type=int,
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18 help="Maximum readsize of query reads (nt) - must be an integer")
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19 the_parser.add_argument(
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20 '--mintarget', type=int,
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21 help="Minimum readsize of target reads (nt) - must be an integer")
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22 the_parser.add_argument(
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23 '--maxtarget', type=int,
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24 help="Maximum readsize of target reads (nt) - must be an integer")
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25 the_parser.add_argument(
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26 '--minscope', type=int,
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27 help="Minimum overlap analyzed (nt) - must be an integer")
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28 the_parser.add_argument(
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29 '--maxscope', type=int,
a35e6f9c1d34 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_signatures commit 6719543c5017d581ae012b864d7c9088f0767fc8
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30 help="Maximum overlap analyzed (nt) - must be an integer")
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31 the_parser.add_argument(
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32 '--output_h', action="store", type=str,
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33 help="h-signature dataframe")
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34 the_parser.add_argument(
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35 '--output_z', action="store", type=str,
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36 help="z-signature dataframe")
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37 args = the_parser.parse_args()
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38 return args
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41 class Map:
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42
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43 def __init__(self, bam_file, minquery=23, maxquery=29, mintarget=23,
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44 maxtarget=29, minscope=1, maxscope=19, output_h='',
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45 output_z=''):
0
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46 self.bam_object = pysam.AlignmentFile(bam_file, 'rb')
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47 self.query_range = range(minquery, maxquery + 1)
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48 self.target_range = range(mintarget, maxtarget + 1)
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49 self.scope = range(minscope, maxscope + 1)
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50 self.H = open(output_h, 'w')
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51 self.Z = open(output_z, 'w')
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52 self.chromosomes = dict(zip(self.bam_object.references,
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53 self.bam_object.lengths))
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54 self.map_dict = self.create_map(self.bam_object)
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55 self.query_positions = self.compute_query_positions()
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56 self.Z.write(self.compute_signature_pairs())
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57 self.H.write(self.compute_signature_h())
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58 self.H.close()
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59 self.Z.close()
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61 def create_map(self, bam_object):
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62 '''
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63 Returns a map_dictionary {(chromosome,read_position,polarity):
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64 [read_length, ...]}
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65 '''
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66 map_dictionary = defaultdict(list)
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67 # get empty value for start and end of each chromosome
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68 for chrom in self.chromosomes:
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69 map_dictionary[(chrom, 1, 'F')] = []
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70 map_dictionary[(chrom, self.chromosomes[chrom], 'F')] = []
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71 for chrom in self.chromosomes:
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72 for read in bam_object.fetch(chrom):
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73 if read.is_reverse:
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74 map_dictionary[(chrom, read.reference_end,
0
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75 'R')].append(read.query_alignment_length)
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76 else:
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77 map_dictionary[(chrom, read.reference_start+1,
0
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78 'F')].append(read.query_alignment_length)
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79 return map_dictionary
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80
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81 def compute_query_positions(self):
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82 ''' this method does not filter on read size, just forward reads
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83 that overlap reverse reads in the overlap range'''
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84 all_query_positions = defaultdict(list)
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85 for genomicKey in self.map_dict.keys():
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86 chrom, coord, pol = genomicKey
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87 for i in self.scope:
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88 if pol == 'F' and len(self.map_dict[chrom,
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89 coord+i-1,
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90 'R']) > 0:
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91 all_query_positions[chrom].append(coord)
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92 break
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93 for chrom in all_query_positions:
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94 all_query_positions[chrom] = sorted(
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95 list(set(all_query_positions[chrom])))
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96 return all_query_positions
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97
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98 def countpairs(self, uppers, lowers):
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99 query_range = self.query_range
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100 target_range = self.target_range
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101 uppers = [size for size in uppers if size in query_range or size in
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102 target_range]
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103 lowers = [size for size in lowers if size in query_range or size in
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104 target_range]
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105 paired = []
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106 for upread in uppers:
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107 for downread in lowers:
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108 if (upread in query_range and downread in target_range) or (
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109 upread in target_range and downread in query_range):
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110 paired.append(upread)
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111 lowers.remove(downread)
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112 break
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113 return len(paired)
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114
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115 def compute_signature_pairs(self):
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116 frequency_table = defaultdict(dict)
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117 scope = self.scope
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118 for chrom in self.chromosomes:
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119 for overlap in scope:
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120 frequency_table[chrom][overlap] = 0
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121 for chrom in self.query_positions:
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122 for coord in self.query_positions[chrom]:
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123 for overlap in scope:
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124 uppers = self.map_dict[chrom, coord, 'F']
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125 lowers = self.map_dict[chrom, coord+overlap-1, 'R']
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126 frequency_table[chrom][overlap] += self.countpairs(uppers,
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127 lowers)
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128 # compute overlaps for all chromosomes merged
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129 for overlap in scope:
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130 accumulator = []
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131 for chrom in frequency_table:
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132 if chrom != 'all_chromosomes':
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133 accumulator.append(frequency_table[chrom][overlap])
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134 frequency_table['all_chromosomes'][overlap] = sum(accumulator)
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135 return self.stringify_table(frequency_table)
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136
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137 def signature_tables(self):
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138 query_range = self.query_range
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139 target_range = self.target_range
0
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140 Query_table = defaultdict(dict)
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141 Target_table = defaultdict(dict)
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142 for key in self.map_dict:
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143 for size in self.map_dict[key]:
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144 if size in query_range or size in target_range:
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145 if key[2] == 'F':
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146 coordinate = key[1]
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147 else:
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148 coordinate = -key[1]
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149 if size in query_range:
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150 Query_table[key[0]][coordinate] = Query_table[key[0]].get(
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151 coordinate, 0) + 1
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152 if size in target_range:
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153 Target_table[key[0]][coordinate] = \
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154 Target_table[key[0]].get(coordinate, 0) + 1
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155 return Query_table, Target_table
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156
7
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157 def compute_signature_h(self):
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158 scope = self.scope
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159 Query_table, Target_table = self.signature_tables()
0
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160 frequency_table = defaultdict(dict)
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161 for chrom in self.chromosomes:
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162 for overlap in scope:
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163 frequency_table[chrom][overlap] = 0
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164 for chrom in Query_table:
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165 Total_Query_Numb = 0
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166 for coord in Query_table[chrom]:
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167 Total_Query_Numb += Query_table[chrom][coord]
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168 for coord in Query_table[chrom]:
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169 local_table = dict([(overlap, 0) for overlap in scope])
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170 number_of_targets = 0
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171 for overlap in scope:
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172 local_table[overlap] += Query_table[chrom][coord] * \
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173 Target_table[chrom].get(-coord - overlap + 1, 0)
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174 number_of_targets += Target_table[chrom].get(
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175 -coord - overlap + 1, 0)
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176 for overlap in scope:
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177 try:
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178 frequency_table[chrom][overlap] += \
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179 local_table[overlap] / number_of_targets \
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180 / float(Total_Query_Numb)
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181 except ZeroDivisionError:
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182 continue
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183 # compute overlap probabilities for all chromosomes merged
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184 general_frequency_table = dict([(overlap, 0) for overlap in scope])
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185 total_aligned_reads = 0
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186 for chrom in frequency_table:
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187 for overlap in frequency_table[chrom]:
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188 total_aligned_reads += self.bam_object.count(chrom)
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189 for chrom in frequency_table:
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190 for overlap in frequency_table[chrom]:
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191 try:
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192 general_frequency_table[overlap] += \
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193 frequency_table[chrom][overlap] / total_aligned_reads \
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194 * self.bam_object.count(chrom)
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195 except ZeroDivisionError:
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196 continue
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197 for overlap in general_frequency_table:
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198 frequency_table['all_chromosomes'][overlap] = \
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199 general_frequency_table[overlap]
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200 return self.stringify_table(frequency_table)
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201
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202 def stringify_table(self, frequency_table):
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203 '''
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204 method both to compute z-score and to return a writable string
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205 '''
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206 tablestring = []
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207 for chrom in sorted(frequency_table):
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208 accumulator = []
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209 for overlap in frequency_table[chrom]:
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210 accumulator.append(frequency_table[chrom][overlap])
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211 z_mean = numpy.mean(accumulator)
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212 z_std = numpy.std(accumulator)
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213 if z_std == 0:
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214 for overlap in sorted(frequency_table[chrom]):
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215 tablestring.append('%s\t%s\t%s\t%s\n' % (
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216 chrom, str(overlap),
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217 str(frequency_table[chrom][overlap]), str(0)))
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218 else:
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219 for overlap in sorted(frequency_table[chrom]):
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220 tablestring.append('%s\t%s\t%s\t%s\n' % (
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221 chrom, str(overlap),
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222 str(frequency_table[chrom][overlap]),
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223 str((frequency_table[chrom][overlap] - z_mean)/z_std)))
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224 return ''.join(tablestring)
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225
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226
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227 if __name__ == "__main__":
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228 args = Parser()
7
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diff changeset
229 mapobj = Map(args.input, args.minquery, args.maxquery, args.mintarget,
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diff changeset
230 args.maxtarget, args.minscope, args.maxscope, args.output_h,
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diff changeset
231 args.output_z)