Mercurial > repos > sanbi-uwc > qc

annotate qc.py @ 0:2aa014ad54bc draft

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"planemo upload for repository https://github.com/SANBI-SA/tools-sanbi-uwc/tree/master/tools/qc commit 54450d56a66bd4b01a17c8ec8aa3e649e3e4749f-dirty"
author sanbi-uwc
date Tue, 30 Mar 2021 15:00:18 +0000
parents
children 878f18249f76
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2aa014ad54bc "planemo upload for repository https://github.com/SANBI-SA/tools-sanbi-uwc/tree/master/tools/qc commit 54450d56a66bd4b01a17c8ec8aa3e649e3e4749f-dirty"
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1 #!/usr/bin/env python3
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2aa014ad54bc "planemo upload for repository https://github.com/SANBI-SA/tools-sanbi-uwc/tree/master/tools/qc commit 54450d56a66bd4b01a17c8ec8aa3e649e3e4749f-dirty"
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3 from Bio import SeqIO
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4 import csv
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5 import subprocess
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6 import pandas as pd
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7 import matplotlib.pyplot as plt
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8 import shlex
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10 """
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11 This script can incorporate as many QC checks as required
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12 as long as it outputs a csv file containing a final column
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13 headed with 'qc_pass' and rows for each sample indcating
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14 'TRUE' if the overall QC check has passed or 'FALSE' if not.
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15 """
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17 def make_qc_plot(depth_pos, n_density, samplename, window=200):
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18 depth_df = pd.DataFrame( { 'position' : [pos[1] for pos in depth_pos], 'depth' : [dep[2] for dep in depth_pos] } )
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19 depth_df['depth_moving_average'] = depth_df.iloc[:,1].rolling(window=window).mean()
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21 n_df = pd.DataFrame( { 'position' : [pos[0] for pos in n_density], 'n_density' : [dens[1] for dens in n_density] } )
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23 fig, ax1 = plt.subplots()
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24 ax2 = ax1.twinx()
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26 ax1.set_xlabel('Position')
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28 ax1.set_ylabel('Depth', color = 'g')
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29 ax1.set_ylim(top=10**5, bottom=1)
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30 ax1.set_yscale('log')
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31 ax1.plot(depth_df['depth_moving_average'], color = 'g')
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33 ax2.set_ylabel('N density', color = 'r')
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34 ax2.plot(n_df['n_density'], color = 'r')
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35 ax2.set_ylim(top=1)
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37 plt.title(samplename)
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38 plt.savefig(samplename + '.depth.png')
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40 def read_depth_file(bamfile):
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41 p = subprocess.Popen(['samtools', 'depth', '-a', '-d', '0', bamfile],
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42 stdout=subprocess.PIPE)
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43 out, err = p.communicate()
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44 counter = 0
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46 pos_depth = []
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47 for ln in out.decode('utf-8').split("\n"):
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48 if ln:
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49 pos_depth.append(ln.split("\t"))
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51 return pos_depth
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54 def get_covered_pos(pos_depth, min_depth):
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55 counter = 0
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56 for contig, pos,depth in pos_depth:
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57 if int(depth) >= min_depth:
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58 counter = counter + 1
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60 return counter
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62 def get_N_positions(fasta):
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63 n_pos = [i for i, letter in enumerate(fasta.seq.lower()) if letter == 'n']
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65 return n_pos
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67 def get_pct_N_bases(fasta):
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69 count_N = len(get_N_positions(fasta))
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71 pct_N_bases = count_N / len(fasta.seq) * 100
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73 return pct_N_bases
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75 def get_largest_N_gap(fasta):
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76 n_pos = get_N_positions(fasta)
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78 n_pos = [0] + n_pos + [len(fasta.seq)]
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80 n_gaps = [j-i for i, j in zip(n_pos[:-1], n_pos[1:])]
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81
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82 return sorted(n_gaps)[-1]
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83
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84 def get_ref_length(ref):
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85 record = SeqIO.read(ref, "fasta")
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86 return len(record.seq)
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87
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88
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89 def sliding_window_N_density(sequence, window=10):
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90
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91 sliding_window_n_density = []
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92 for i in range(0, len(sequence.seq), 1):
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93 window_mid = i + ( window / 2)
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94 window_seq = sequence.seq[i:i+window]
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95 n_count = window_seq.lower().count('n')
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96 n_density = n_count / window
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97
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98 sliding_window_n_density.append( [ window_mid, n_density ] )
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99
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100 return sliding_window_n_density
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101
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102 def get_num_reads(bamfile):
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103
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104 st_filter = '0x900'
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105 command = 'samtools view -c -F{} {}'.format(st_filter, bamfile)
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106 what = shlex.split(command)
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107
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108 return subprocess.check_output(what).decode().strip()
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109
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110 def go(args):
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111 if args.illumina:
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112 depth = 10
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113 elif args.nanopore:
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114 depth = 20
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115
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116 ## Depth calcs
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117 ref_length = get_ref_length(args.ref)
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118 depth_pos = read_depth_file(args.bam)
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119
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120 depth_covered_bases = get_covered_pos(depth_pos, depth)
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121
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122 pct_covered_bases = depth_covered_bases / ref_length * 100
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123
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124 ## Number of aligned reads calculaton
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125 num_reads = get_num_reads(args.bam)
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126
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127 # Unknown base calcs
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128 fasta = SeqIO.read(args.fasta, "fasta")
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129
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130 pct_N_bases = 0
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131 largest_N_gap = 0
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132 qc_pass = "FALSE"
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133
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134 if len(fasta.seq) != 0:
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135
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136 pct_N_bases = get_pct_N_bases(fasta)
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137 largest_N_gap = get_largest_N_gap(fasta)
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138
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139 # QC PASS / FAIL
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140 if largest_N_gap >= 10000 or pct_N_bases < 50.0:
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141 qc_pass = "TRUE"
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142
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143
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144 qc_line = { 'sample_name' : args.sample,
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145 'pct_N_bases' : "{:.2f}".format(pct_N_bases),
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146 'pct_covered_bases' : "{:.2f}".format(pct_covered_bases),
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147 'longest_no_N_run' : largest_N_gap,
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148 'num_aligned_reads' : num_reads,
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149 'fasta': args.fasta,
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150 'bam' : args.bam,
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151 'qc_pass' : qc_pass}
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152
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153
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154 with open(args.outfile, 'w') as csvfile:
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155 header = qc_line.keys()
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156 writer = csv.DictWriter(csvfile, fieldnames=header)
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157 writer.writeheader()
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158 writer.writerow(qc_line)
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159
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160 N_density = sliding_window_N_density(fasta)
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161 make_qc_plot(depth_pos, N_density, args.sample)
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162
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163 def main():
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164 import argparse
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165
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166 parser = argparse.ArgumentParser()
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167 group = parser.add_mutually_exclusive_group(required=True)
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168 group.add_argument('--nanopore', action='store_true')
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169 group.add_argument('--illumina', action='store_true')
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170 parser.add_argument('--outfile', required=True)
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171 parser.add_argument('--sample', required=True)
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172 parser.add_argument('--ref', required=True)
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173 parser.add_argument('--bam', required=True)
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174 parser.add_argument('--fasta', required=True)
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175
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176 args = parser.parse_args()
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177 go(args)
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178
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179 if __name__ == "__main__":
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180 main()