annotate small_rna_maps.py @ 17:b28dcd4051e8 draft

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit 16f15e5ab2b79590a8ae410f76434aa6690c1fc4
author artbio
date Thu, 15 Nov 2018 12:29:57 -0500
parents 600e2498bd21
children 2c95c899d0a4
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6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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1 import argparse
6d48150495e3 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959
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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('--inputs', dest='inputs', required=True,
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12 nargs='+', help='list of input BAM files')
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13 the_parser.add_argument('--minsize', dest='minsize', type=int,
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14 default=0, help='minimal size of reads')
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15 the_parser.add_argument('--maxsize', dest='maxsize', type=int,
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16 default=10000, help='maximal size of reads')
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17 the_parser.add_argument('--cluster', dest='cluster', type=int,
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18 default=0, help='clustering distance')
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19 the_parser.add_argument('--sample_names', dest='sample_names',
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20 required=True, nargs='+',
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21 help='list of sample names')
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22 the_parser.add_argument('--outputs', nargs='+', action='store',
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23 help='list of two output paths (only two)')
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24 the_parser.add_argument('-M', '--plot_methods', nargs='+', action='store',
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25 help='list of 2 plot methods (only two) among:\
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26 Counts, Max, Mean, Median, Coverage and Size')
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27 the_parser.add_argument('--nostrand', action='store_true',
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28 help='Consider reads regardless their polarity')
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29
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30 args = the_parser.parse_args()
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31 return args
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34 class Map:
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35
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36 def __init__(self, bam_file, sample, minsize, maxsize, cluster, nostrand):
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37 self.sample_name = sample
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38 self.minsize = minsize
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39 self.maxsize = maxsize
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40 self.cluster = cluster
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41 if not nostrand:
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42 self.nostrand = False
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43 else:
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44 self.nostrand = True
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45 self.bam_object = pysam.AlignmentFile(bam_file, 'rb')
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46 self.chromosomes = dict(zip(self.bam_object.references,
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47 self.bam_object.lengths))
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48 self.map_dict = self.create_map(self.bam_object, self.minsize,
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49 self.maxsize, self.nostrand)
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50 if self.cluster:
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51 self.map_dict = self.tile_map(self.map_dict, self.cluster)
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52
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53 def create_map(self, bam_object, minsize, maxsize, nostrand=False):
0
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54 '''
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55 Returns a map_dictionary {(chromosome,read_position,polarity):
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56 [read_length, ...]}
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57 '''
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58 map_dictionary = defaultdict(list)
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59 for chrom in self.chromosomes:
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60 # get empty value for start and end of each chromosome
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61 map_dictionary[(chrom, 1, 'F')] = []
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62 map_dictionary[(chrom, self.chromosomes[chrom], 'F')] = []
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63 if not nostrand:
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64 for read in bam_object.fetch(chrom):
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65 positions = read.positions # a list of covered positions
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66 if read.is_reverse:
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67 map_dictionary[(chrom, positions[-1]+1, 'R')].append(
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68 read.query_alignment_length)
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69 else:
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70 map_dictionary[(chrom, positions[0]+1, 'F')].append(
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71 read.query_alignment_length)
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72 else:
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73 for read in bam_object.fetch(chrom):
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74 positions = read.positions # a list of covered positions
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75 if read.is_reverse:
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76 map_dictionary[(chrom, positions[-1]+1, 'F')].append(
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77 read.query_alignment_length)
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78 else:
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79 map_dictionary[(chrom, positions[0]+1, 'F')].append(
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80 read.query_alignment_length)
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81 return map_dictionary
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82
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83 def grouper(self, iterable, clust_distance):
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84 prev = None
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85 group = []
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86 for item in iterable:
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87 if not prev or item - prev <= clust_distance:
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88 group.append(item)
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89 else:
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diff changeset
90 yield group
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91 group = [item]
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92 prev = item
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93 if group:
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diff changeset
94 yield group
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95
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96 def tile_map(self, map_dic, clust_distance):
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97 '''
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98 takes a map_dictionary {(chromosome,read_position,polarity):
16
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99 [read_length, ...]}
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100 and returns a map_dictionary with structure:
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101 {(chromosome,read_position,polarity):
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102 ([read_length, ...], [start_clust, end_clust])}
9
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103 '''
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104 clustered_dic = defaultdict(list)
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105 for chrom in self.chromosomes:
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106 F_chrom_coord = []
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107 R_chrom_coord = []
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108 for key in map_dic:
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109 if key[0] == chrom and key[2] == 'F':
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110 F_chrom_coord.append(key[1])
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111 elif key[0] == chrom and key[2] == 'R':
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112 R_chrom_coord.append(key[1])
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113 F_chrom_coord = list(set(F_chrom_coord))
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114 R_chrom_coord = list(set(R_chrom_coord))
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115 F_chrom_coord.sort()
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116 R_chrom_coord.sort()
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117 F_clust_values = [i for i in self.grouper(F_chrom_coord,
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118 clust_distance)]
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119 F_clust_keys = [(i[-1]+i[0])/2 for i in F_clust_values]
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120 R_clust_values = [i for i in self.grouper(R_chrom_coord,
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121 clust_distance)]
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122 R_clust_keys = [(i[-1]+i[0])/2 for i in R_clust_values]
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123 # now 2 dictionnaries (F and R) with structure:
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124 # {centered_coordinate: [coord1, coord2, coord3, ..]}
9
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125 F_clust_dic = dict(zip(F_clust_keys, F_clust_values))
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126 R_clust_dic = dict(zip(R_clust_keys, R_clust_values))
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127 for centcoor in F_clust_dic:
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128 accumulator = []
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129 for coor in F_clust_dic[centcoor]:
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130 accumulator.extend(map_dic[(chrom, coor, 'F')])
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131 clustered_dic[(chrom, centcoor, 'F')] = [len(accumulator), [
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132 F_clust_dic[centcoor][0],
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133 F_clust_dic[centcoor][-1]]]
9
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134 for centcoor in R_clust_dic:
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135 accumulator = []
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136 for coor in R_clust_dic[centcoor]:
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137 accumulator.extend(map_dic[(chrom, coor, 'R')])
16
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138 clustered_dic[(chrom, centcoor, 'R')] = [len(accumulator), [
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139 R_clust_dic[centcoor][0],
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140 R_clust_dic[centcoor][-1]]]
9
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141 return clustered_dic
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142
3
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143 def compute_readcount(self, map_dictionary, out):
2
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144 '''
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145 takes a map_dictionary as input and writes
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146 a readmap_dictionary {(chromosome,read_position,polarity):
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147 number_of_reads}
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148 in an open file handler out
0
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149 '''
2
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150 readmap_dictionary = dict()
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151 for key in map_dictionary:
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152 readmap_dictionary[key] = len(map_dictionary[key])
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153 self.write_table(readmap_dictionary, out)
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154
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155 def compute_max(self, map_dictionary, out):
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156 '''
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157 takes a map_dictionary as input and writes
0
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158 a max_dictionary {(chromosome,read_position,polarity):
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159 max_of_number_of_read_at_any_position}
2
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160 Not clear this function is still required
0
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161 '''
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162 merge_keylist = [(i[0], 0) for i in map_dictionary.keys()]
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163 max_dictionary = dict(merge_keylist)
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164 for key in map_dictionary:
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165 if len(map_dictionary[key]) > max_dictionary[key[0]]:
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166 max_dictionary[key[0]] = len(map_dictionary[key])
2
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167 self.write_table(max_dictionary, out)
0
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168
2
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169 def compute_mean(self, map_dictionary, out):
0
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170 '''
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diff changeset
171 takes a map_dictionary as input and returns
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172 a mean_dictionary {(chromosome,read_position,polarity):
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diff changeset
173 mean_value_of_reads}
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174 '''
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175 mean_dictionary = dict()
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176 for key in map_dictionary:
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177 if len(map_dictionary[key]) == 0:
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artbio
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178 mean_dictionary[key] = 0
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179 else:
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artbio
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180 mean_dictionary[key] = round(numpy.mean(map_dictionary[key]),
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181 1)
2
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182 self.write_table(mean_dictionary, out)
0
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183
2
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184 def compute_median(self, map_dictionary, out):
0
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185 '''
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diff changeset
186 takes a map_dictionary as input and returns
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187 a mean_dictionary {(chromosome,read_position,polarity):
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188 mean_value_of_reads}
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189 '''
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artbio
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190 median_dictionary = dict()
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191 for key in map_dictionary:
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192 if len(map_dictionary[key]) == 0:
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artbio
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193 median_dictionary[key] = 0
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194 else:
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artbio
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195 median_dictionary[key] = numpy.median(map_dictionary[key])
2
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196 self.write_table(median_dictionary, out)
0
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197
16
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198 def compute_coverage(self, map_dictionary, out, quality=15):
0
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199 '''
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artbio
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200 takes a map_dictionary as input and returns
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201 a coverage_dictionary {(chromosome,read_position,polarity):
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artbio
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202 coverage}
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artbio
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203 '''
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artbio
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204 coverage_dictionary = dict()
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artbio
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205 for chrom in self.chromosomes:
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artbio
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206 coverage_dictionary[(chrom, 1, 'F')] = 0
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artbio
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207 coverage_dictionary[(chrom, self.chromosomes[chrom], 'F')] = 0
4
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208 for read in self.bam_object.fetch(chrom):
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artbio
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diff changeset
209 positions = read.positions # a list of covered positions
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artbio
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diff changeset
210 for pos in positions:
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211 if not map_dictionary[(chrom, pos+1, 'F')]:
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diff changeset
212 map_dictionary[(chrom, pos+1, 'F')] = []
0
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213 for key in map_dictionary:
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214 coverage = self.bam_object.count_coverage(
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artbio
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215 reference=key[0],
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artbio
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216 start=key[1]-1,
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artbio
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217 end=key[1],
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artbio
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diff changeset
218 quality_threshold=quality)
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219 """ Add the 4 coverage values """
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220 coverage = [sum(x) for x in zip(*coverage)]
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artbio
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diff changeset
221 coverage_dictionary[key] = coverage[0]
2
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diff changeset
222 self.write_table(coverage_dictionary, out)
0
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223
2
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diff changeset
224 def compute_size(self, map_dictionary, out):
0
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diff changeset
225 '''
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artbio
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diff changeset
226 Takes a map_dictionary and returns a dictionary of sizes:
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artbio
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diff changeset
227 {chrom: {polarity: {size: nbre of reads}}}
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228 '''
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artbio
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diff changeset
229 size_dictionary = defaultdict(lambda: defaultdict(
1
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diff changeset
230 lambda: defaultdict(int)))
0
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diff changeset
231 # to track empty chromosomes
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232 for chrom in self.chromosomes:
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233 if self.bam_object.count(chrom) == 0:
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diff changeset
234 size_dictionary[chrom]['F'][10] = 0
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diff changeset
235 for key in map_dictionary:
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artbio
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diff changeset
236 for size in map_dictionary[key]:
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artbio
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diff changeset
237 size_dictionary[key[0]][key[2]][size] += 1
2
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diff changeset
238 self.write_size_table(size_dictionary, out)
0
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239
2
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diff changeset
240 def write_table(self, mapdict, out):
0
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diff changeset
241 '''
16
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diff changeset
242 Writer of a tabular file
2
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diff changeset
243 Dataset, Chromosome, Chrom_length, Coordinate, Polarity,
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artbio
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diff changeset
244 <some mapped value>
0
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diff changeset
245 out is an *open* file handler
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246 '''
2
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artbio
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diff changeset
247 for key in sorted(mapdict):
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diff changeset
248 line = [self.sample_name, key[0], self.chromosomes[key[0]],
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diff changeset
249 key[1], key[2], mapdict[key]]
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diff changeset
250 line = [str(i) for i in line]
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diff changeset
251 out.write('\t'.join(line) + '\n')
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diff changeset
252
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diff changeset
253 def write_size_table(self, sizedic, out):
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254 '''
16
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artbio
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diff changeset
255 Writer of a tabular file
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artbio
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diff changeset
256 Dataset, Chromosome, Chrom_length, <category (size)>, <some value>
2
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diff changeset
257 out is an *open* file handler
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258 '''
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artbio
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diff changeset
259 for chrom in sorted(sizedic):
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artbio
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diff changeset
260 sizes = sizedic[chrom]['F'].keys()
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artbio
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diff changeset
261 sizes.extend(sizedic[chrom]['R'].keys())
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artbio
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diff changeset
262 for polarity in sorted(sizedic[chrom]):
0
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263 for size in range(min(sizes), max(sizes)+1):
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264 try:
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265 line = [self.sample_name, chrom, polarity, size,
2
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266 sizedic[chrom][polarity][size]]
0
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267 except KeyError:
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268 line = [self.sample_name, chrom, polarity, size, 0]
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269 line = [str(i) for i in line]
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270 out.write('\t'.join(line) + '\n')
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271
16
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272 def write_cluster_table(self, clustered_dic, out):
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273 '''
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274 Writer of a tabular file
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275 Dataset, Chromosome, Chrom_length, Coordinate, Polarity,
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276 <some mapped value>
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277 out is an *open* file handler
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278 '''
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279 for key in sorted(clustered_dic):
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280 start = clustered_dic[key][1][0]
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281 end = clustered_dic[key][1][1]
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282 size = end - start + 1
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283 density = float(clustered_dic[key][0]) / size
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284 line = [self.sample_name, key[0], self.chromosomes[key[0]],
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285 key[1], key[2], clustered_dic[key][0],
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286 str(start) + "-" + str(end), str(size), str(density)]
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287 line = [str(i) for i in line]
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288 out.write('\t'.join(line) + '\n')
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289
0
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290
17
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291 def main(inputs, samples, methods, outputs, minsize, maxsize, cluster,
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292 nostrand):
2
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293 for method, output in zip(methods, outputs):
16
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294 out = open(output, 'w')
2
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295 if method == 'Size':
5
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parents: 4
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296 header = ["Dataset", "Chromosome", "Polarity", method, "Counts"]
16
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297 elif cluster:
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298 header = ["Dataset", "Chromosome", "Chrom_length", "Coordinate",
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299 "Polarity", method, "Start-End", "Cluster Size",
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300 "density"]
2
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301 else:
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302 header = ["Dataset", "Chromosome", "Chrom_length", "Coordinate",
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artbio
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303 "Polarity", method]
16
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304 out.write('\t'.join(header) + '\n')
2
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305 for input, sample in zip(inputs, samples):
17
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306 mapobj = Map(input, sample, minsize, maxsize, cluster, nostrand)
3
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307 token = {"Counts": mapobj.compute_readcount,
2
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308 "Max": mapobj.compute_max,
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309 "Mean": mapobj.compute_mean,
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310 "Median": mapobj.compute_median,
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311 "Coverage": mapobj.compute_coverage,
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312 "Size": mapobj.compute_size,
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313 "cluster": mapobj.write_cluster_table}
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314 if cluster:
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315 token["cluster"](mapobj.map_dict, out)
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316 else:
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317 token[method](mapobj.map_dict, out)
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318 # mapobj.compute_coverage(mapobj.map_dict, out)
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319 out.close()
0
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diff changeset
320
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321
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322 if __name__ == "__main__":
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artbio
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diff changeset
323 args = Parser()
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diff changeset
324 # if identical sample names
2
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325 if len(set(args.sample_names)) != len(args.sample_names):
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326 args.sample_names = [name + '_' + str(i) for
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327 i, name in enumerate(args.sample_names)]
8
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diff changeset
328 main(args.inputs, args.sample_names, args.plot_methods, args.outputs,
17
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diff changeset
329 args.minsize, args.maxsize, args.cluster, args.nostrand)