annotate vsnp_statistics.py @ 18:076069733de7 draft

Uploaded
author greg
date Wed, 04 Aug 2021 13:27:17 +0000
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children 377c1a96aae9
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1 #!/usr/bin/env python
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2
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3 import argparse
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4 import gzip
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5 import os
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6 from functools import partial
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8 import numpy
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9 import pandas
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10 from Bio import SeqIO
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13 class Statistics:
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15 def __init__(self, reference, fastq_file, file_size, total_reads, mean_read_length, mean_read_quality, reads_passing_q30):
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16 self.reference = reference
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17 self.fastq_file = fastq_file
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18 self.file_size = file_size
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19 self.total_reads = total_reads
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20 self.mean_read_length = mean_read_length
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21 self.mean_read_quality = mean_read_quality
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22 self.reads_passing_q30 = reads_passing_q30
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24
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25 def nice_size(size):
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26 # Returns a readably formatted string with the size
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27 words = ['bytes', 'KB', 'MB', 'GB', 'TB', 'PB', 'EB']
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28 prefix = ''
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29 try:
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30 size = float(size)
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31 if size < 0:
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32 size = abs(size)
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33 prefix = '-'
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34 except Exception:
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35 return '??? bytes'
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36 for ind, word in enumerate(words):
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37 step = 1024 ** (ind + 1)
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38 if step > size:
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39 size = size / float(1024 ** ind)
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40 if word == 'bytes': # No decimals for bytes
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41 return "%s%d bytes" % (prefix, size)
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42 return "%s%.1f %s" % (prefix, size, word)
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43 return '??? bytes'
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46 def get_statistics(dbkey, fastq_file, gzipped):
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47 sampling_size = 10000
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48 # Read fastq_file into a data fram to
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49 # get the phred quality scores.
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50 _open = partial(gzip.open, mode='rt') if gzipped else open
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51 with _open(fastq_file) as fh:
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52 identifiers = []
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53 seqs = []
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54 letter_annotations = []
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55 for seq_record in SeqIO.parse(fh, "fastq"):
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56 identifiers.append(seq_record.id)
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57 seqs.append(seq_record.seq)
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58 letter_annotations.append(seq_record.letter_annotations["phred_quality"])
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59 # Convert lists to Pandas series.
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60 s1 = pandas.Series(identifiers, name='id')
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61 s2 = pandas.Series(seqs, name='seq')
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62 # Gather Series into a data frame.
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63 fastq_df = pandas.DataFrame(dict(id=s1, seq=s2)).set_index(['id'])
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64 # Starting at row 3, keep every 4 row
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65 # random sample specified number of rows.
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66 file_size = nice_size(os.path.getsize(fastq_file))
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67 total_reads = int(len(fastq_df.index) / 4)
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68 # Mean Read Length
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69 if sampling_size > total_reads:
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70 sampling_size = total_reads
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71 fastq_df = fastq_df.iloc[3::4].sample(sampling_size)
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72 dict_mean = {}
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73 list_length = []
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74 i = 0
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75 for id, seq, in fastq_df.iterrows():
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76 dict_mean[id] = numpy.mean(letter_annotations[i])
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77 list_length.append(len(seq.array[0]))
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78 i += 1
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79 mean_read_length = '%.1f' % numpy.mean(list_length)
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80 # Mean Read Quality
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81 df_mean = pandas.DataFrame.from_dict(dict_mean, orient='index', columns=['ave'])
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82 mean_read_quality = '%.1f' % df_mean['ave'].mean()
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83 # Reads Passing Q30
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84 reads_gt_q30 = len(df_mean[df_mean['ave'] >= 30])
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85 reads_passing_q30 = '{:10.2f}'.format(reads_gt_q30 / sampling_size)
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86 stats = Statistics(dbkey, os.path.basename(fastq_file), file_size, total_reads, mean_read_length,
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87 mean_read_quality, reads_passing_q30)
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88 return stats
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90
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91 def accrue_statistics(dbkey, read1, read2, gzipped):
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92 read1_stats = get_statistics(dbkey, read1, gzipped)
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93 if read2 is None:
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94 read2_stats = None
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95 else:
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96 read2_stats = get_statistics(dbkey, read2, gzipped)
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97 return read1_stats, read2_stats
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98
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100 def output_statistics(read1_stats, read2_stats, idxstats_file, metrics_file, output_file):
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101 paired_reads = read2_stats is not None
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102 if paired_reads:
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103 columns = ['Reference', 'Read1 FASTQ', 'File Size', 'Reads', 'Mean Read Length', 'Mean Read Quality',
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104 'Reads Passing Q30', 'Read2 FASTQ', 'File Size', 'Reads', 'Mean Read Length', 'Mean Read Quality',
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105 'Reads Passing Q30', 'Total Reads', 'All Mapped Reads', 'Unmapped Reads',
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106 'Unmapped Reads Percentage of Total', 'Reference with Coverage', 'Average Depth of Coverage',
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107 'Good SNP Count']
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108 else:
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109 columns = ['Reference', 'FASTQ', 'File Size', 'Mean Read Length', 'Mean Read Quality', 'Reads Passing Q30',
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110 'Total Reads', 'All Mapped Reads', 'Unmapped Reads', 'Unmapped Reads Percentage of Total',
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111 'Reference with Coverage', 'Average Depth of Coverage', 'Good SNP Count']
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112 with open(output_file, "w") as outfh:
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113 outfh.write("%s\n" % "\t".join(columns))
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114 line_items = []
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115 # Get the current stats and associated files.
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116 # Get and output the statistics.
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117 line_items.append(read1_stats.reference)
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118 line_items.append(read1_stats.fastq_file)
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119 line_items.append(read1_stats.file_size)
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120 if paired_reads:
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121 line_items.append(read1_stats.total_reads)
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122 line_items.append(read1_stats.mean_read_length)
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123 line_items.append(read1_stats.mean_read_quality)
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124 line_items.append(read1_stats.reads_passing_q30)
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125 if paired_reads:
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126 line_items.append(read2_stats.fastq_file)
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127 line_items.append(read2_stats.file_size)
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128 line_items.append(read2_stats.total_reads)
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129 line_items.append(read2_stats.mean_read_length)
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130 line_items.append(read2_stats.mean_read_quality)
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131 line_items.append(read2_stats.reads_passing_q30)
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132 # Total Reads
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133 if paired_reads:
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134 total_reads = read1_stats.total_reads + read2_stats.total_reads
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135 else:
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136 total_reads = read1_stats.total_reads
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137 line_items.append(total_reads)
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138 # All Mapped Reads
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139 all_mapped_reads, unmapped_reads = process_idxstats_file(idxstats_file)
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140 line_items.append(all_mapped_reads)
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141 line_items.append(unmapped_reads)
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142 # Unmapped Reads Percentage of Total
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143 if unmapped_reads > 0:
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144 unmapped_reads_percentage = '{:10.2f}'.format(unmapped_reads / total_reads)
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145 else:
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146 unmapped_reads_percentage = 0
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147 line_items.append(unmapped_reads_percentage)
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148 # Reference with Coverage
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149 ref_with_coverage, avg_depth_of_coverage, good_snp_count = process_metrics_file(metrics_file)
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150 line_items.append(ref_with_coverage)
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151 line_items.append(avg_depth_of_coverage)
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152 line_items.append(good_snp_count)
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153 outfh.write('%s\n' % '\t'.join(str(x) for x in line_items))
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155
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156 def process_idxstats_file(idxstats_file):
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157 all_mapped_reads = 0
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158 unmapped_reads = 0
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159 with open(idxstats_file, "r") as fh:
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160 for i, line in enumerate(fh):
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161 line = line.rstrip('\r\n')
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162 items = line.split("\t")
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163 if i == 0:
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164 # NC_002945.4 4349904 213570 4047
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165 all_mapped_reads = int(items[2])
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166 elif i == 1:
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167 # * 0 0 82774
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168 unmapped_reads = int(items[3])
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169 return all_mapped_reads, unmapped_reads
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170
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171
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172 def process_metrics_file(metrics_file):
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173 ref_with_coverage = '0%'
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174 avg_depth_of_coverage = 0
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175 good_snp_count = 0
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176 with open(metrics_file, "r") as ifh:
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177 for i, line in enumerate(ifh):
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178 if i == 0:
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179 # Skip comments.
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180 continue
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181 line = line.rstrip('\r\n')
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182 items = line.split("\t")
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183 if i == 1:
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184 # MarkDuplicates 10.338671 98.74%
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185 ref_with_coverage = items[3]
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186 avg_depth_of_coverage = items[2]
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187 elif i == 2:
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188 # VCFfilter 611
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189 good_snp_count = items[1]
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190 return ref_with_coverage, avg_depth_of_coverage, good_snp_count
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193 parser = argparse.ArgumentParser()
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194
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195 parser.add_argument('--dbkey', action='store', dest='dbkey', help='Reference dbkey')
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196 parser.add_argument('--gzipped', action='store_true', dest='gzipped', required=False, default=False, help='Input files are gzipped')
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197 parser.add_argument('--output', action='store', dest='output', help='Output Excel statistics file')
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198 parser.add_argument('--read1', action='store', dest='read1', help='Required: single read')
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199 parser.add_argument('--read2', action='store', dest='read2', required=False, default=None, help='Optional: paired read')
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200 parser.add_argument('--samtools_idxstats', action='store', dest='samtools_idxstats', help='Output of samtools_idxstats')
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201 parser.add_argument('--vsnp_azc_metrics', action='store', dest='vsnp_azc_metrics', help='Output of vsnp_add_zero_coverage')
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202
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203 args = parser.parse_args()
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204
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205 stats_list = []
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206 idxstats_files = []
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207 metrics_files = []
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208 # Accumulate inputs.
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209 read1_stats, read2_stats = accrue_statistics(args.dbkey, args.read1, args.read2, args.gzipped)
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210 output_statistics(read1_stats, read2_stats, args.samtools_idxstats, args.vsnp_azc_metrics, args.output)