annotate vsnp_statistics.py @ 6:429892a80419 draft

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author greg
date Wed, 16 Jun 2021 17:57:32 +0000
parents d0fbdeaaa488
children 1becb6606626
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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 csv
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5 import gzip
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6 import os
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7 from functools import partial
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8
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9 import numpy
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10 import pandas
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11 from Bio import SeqIO
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12
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13
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14 def nice_size(size):
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15 # Returns a readably formatted string with the size
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16 words = ['bytes', 'KB', 'MB', 'GB', 'TB', 'PB', 'EB']
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17 prefix = ''
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18 try:
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19 size = float(size)
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20 if size < 0:
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21 size = abs(size)
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22 prefix = '-'
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23 except Exception:
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24 return '??? bytes'
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25 for ind, word in enumerate(words):
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26 step = 1024 ** (ind + 1)
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27 if step > size:
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28 size = size / float(1024 ** ind)
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29 if word == 'bytes': # No decimals for bytes
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30 return "%s%d bytes" % (prefix, size)
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31 return "%s%.1f %s" % (prefix, size, word)
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32 return '??? bytes'
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33
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34
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35 def output_statistics(fastq_files, idxstats_files, metrics_files, output_file, gzipped, dbkey):
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36 # Produce an Excel spreadsheet that
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37 # contains a row for each sample.
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38 columns = ['Reference', 'File Size', 'Mean Read Length', 'Mean Read Quality', 'Reads Passing Q30',
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39 'Total Reads', 'All Mapped Reads', 'Unmapped Reads', 'Unmapped Reads Percentage of Total',
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40 'Reference with Coverage', 'Average Depth of Coverage', 'Good SNP Count']
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41 data_frames = []
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42 for i, fastq_file in enumerate(fastq_files):
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43 idxstats_file = idxstats_files[i]
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44 metrics_file = metrics_files[i]
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45 file_name_base = os.path.basename(fastq_file)
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46 # Read fastq_file into a data frame.
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47 _open = partial(gzip.open, mode='rt') if gzipped else open
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48 with _open(fastq_file) as fh:
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49 identifiers = []
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50 seqs = []
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51 letter_annotations = []
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52 for seq_record in SeqIO.parse(fh, "fastq"):
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53 identifiers.append(seq_record.id)
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54 seqs.append(seq_record.seq)
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55 letter_annotations.append(seq_record.letter_annotations["phred_quality"])
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56 # Convert lists to Pandas series.
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57 s1 = pandas.Series(identifiers, name='id')
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58 s2 = pandas.Series(seqs, name='seq')
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59 # Gather Series into a data frame.
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60 fastq_df = pandas.DataFrame(dict(id=s1, seq=s2)).set_index(['id'])
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61 total_reads = int(len(fastq_df.index) / 4)
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62 current_sample_df = pandas.DataFrame(index=[file_name_base], columns=columns)
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63 # Reference
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64 current_sample_df.at[file_name_base, 'Reference'] = dbkey
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65 # File Size
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66 current_sample_df.at[file_name_base, 'File Size'] = nice_size(os.path.getsize(fastq_file))
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67 # Mean Read Length
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68 sampling_size = 10000
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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 current_sample_df.at[file_name_base, '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 current_sample_df.at[file_name_base, '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 current_sample_df.at[file_name_base, 'Reads Passing Q30'] = reads_passing_q30
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87 # Total Reads
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88 current_sample_df.at[file_name_base, 'Total Reads'] = total_reads
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89 # All Mapped Reads
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90 all_mapped_reads, unmapped_reads = process_idxstats_file(idxstats_file)
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91 current_sample_df.at[file_name_base, 'All Mapped Reads'] = all_mapped_reads
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92 # Unmapped Reads
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93 current_sample_df.at[file_name_base, 'Unmapped Reads'] = unmapped_reads
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94 # Unmapped Reads Percentage of Total
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95 if unmapped_reads > 0:
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96 unmapped_reads_percentage = '{:10.2f}'.format(unmapped_reads / total_reads)
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97 else:
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98 unmapped_reads_percentage = 0
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99 current_sample_df.at[file_name_base, 'Unmapped Reads Percentage of Total'] = unmapped_reads_percentage
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100 # Reference with Coverage
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101 ref_with_coverage, avg_depth_of_coverage, good_snp_count = process_metrics_file(metrics_file)
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102 current_sample_df.at[file_name_base, 'Reference with Coverage'] = ref_with_coverage
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103 # Average Depth of Coverage
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104 current_sample_df.at[file_name_base, 'Average Depth of Coverage'] = avg_depth_of_coverage
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105 # Good SNP Count
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106 current_sample_df.at[file_name_base, 'Good SNP Count'] = good_snp_count
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107 data_frames.append(current_sample_df)
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108 output_df = pandas.concat(data_frames)
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109 output_df.to_csv(output_file, sep='\t', quoting=csv.QUOTE_NONE, escapechar='\\')
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110
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111
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112 def process_idxstats_file(idxstats_file):
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113 all_mapped_reads = 0
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114 unmapped_reads = 0
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115 with open(idxstats_file, "r") as fh:
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116 for i, line in enumerate(fh):
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117 line = line.rstrip('\r\n')
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118 items = line.split("\t")
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119 if i == 0:
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120 # NC_002945.4 4349904 213570 4047
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121 all_mapped_reads = int(items[2])
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122 elif i == 1:
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123 # * 0 0 82774
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124 unmapped_reads = int(items[3])
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125 return all_mapped_reads, unmapped_reads
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126
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127
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128 def process_metrics_file(metrics_file):
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129 ref_with_coverage = '0%'
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130 avg_depth_of_coverage = 0
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131 good_snp_count = 0
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132 with open(metrics_file, "r") as ifh:
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133 for i, line in enumerate(ifh):
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134 if i == 0:
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135 # Skip comments.
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136 continue
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137 line = line.rstrip('\r\n')
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138 items = line.split("\t")
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139 if i == 1:
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140 # MarkDuplicates 10.338671 98.74%
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141 ref_with_coverage = items[3]
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142 avg_depth_of_coverage = items[2]
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143 elif i == 2:
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144 # VCFfilter 611
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145 good_snp_count = items[1]
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146 return ref_with_coverage, avg_depth_of_coverage, good_snp_count
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147
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148
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149 parser = argparse.ArgumentParser()
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151 parser.add_argument('--dbkey', action='store', dest='dbkey', help='Reference dbkey')
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152 parser.add_argument('--gzipped', action='store_true', dest='gzipped', required=False, default=False, help='Input files are gzipped')
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153 parser.add_argument('--input_idxstats_dir', action='store', dest='input_idxstats_dir', required=False, default=None, help='Samtools idxstats input directory')
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154 parser.add_argument('--input_metrics_dir', action='store', dest='input_metrics_dir', required=False, default=None, help='vSNP add zero coverage metrics input directory')
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155 parser.add_argument('--input_reads_dir', action='store', dest='input_reads_dir', required=False, default=None, help='Samples input directory')
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156 parser.add_argument('--list_paired', action='store_true', dest='list_paired', required=False, default=False, help='Input samples is a list of paired reads')
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157 parser.add_argument('--output', action='store', dest='output', help='Output Excel statistics file')
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158 parser.add_argument('--read1', action='store', dest='read1', help='Required: single read')
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159 parser.add_argument('--read2', action='store', dest='read2', required=False, default=None, help='Optional: paired read')
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160 parser.add_argument('--samtools_idxstats', action='store', dest='samtools_idxstats', help='Output of samtools_idxstats')
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161 parser.add_argument('--vsnp_azc', action='store', dest='vsnp_azc', help='Output of vsnp_add_zero_coverage')
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162
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163 args = parser.parse_args()
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164
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165 fastq_files = []
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166 idxstats_files = []
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167 metrics_files = []
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168 # Accumulate inputs.
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169 if args.read1 is not None:
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170 # The inputs are not dataset collections, so
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171 # read1, read2 (possibly) and vsnp_azc will also
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172 # not be None.
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173 fastq_files.append(args.read1)
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174 idxstats_files.append(args.samtools_idxstats)
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175 metrics_files.append(args.vsnp_azc)
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176 if args.read2 is not None:
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177 fastq_files.append(args.read2)
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178 idxstats_files.append(args.samtools_idxstats)
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179 metrics_files.append(args.vsnp_azc)
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180 else:
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181 for file_name in sorted(os.listdir(args.input_reads_dir)):
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182 fastq_files.append(os.path.join(args.input_reads_dir, file_name))
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183 for file_name in sorted(os.listdir(args.input_idxstats_dir)):
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184 idxstats_files.append(os.path.join(args.input_idxstats_dir, file_name))
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185 if args.list_paired:
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186 # Add the idxstats file for reverse.
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187 idxstats_files.append(os.path.join(args.input_idxstats_dir, file_name))
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188 for file_name in sorted(os.listdir(args.input_metrics_dir)):
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189 metrics_files.append(os.path.join(args.input_metrics_dir, file_name))
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190 if args.list_paired:
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191 # Add the metrics file for reverse.
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192 metrics_files.append(os.path.join(args.input_metrics_dir, file_name))
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193 output_statistics(fastq_files, idxstats_files, metrics_files, args.output, args.gzipped, args.dbkey)