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analyze_cov_per_contig.py
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analyze_cov_per_contig.py
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### Boas Pucker ###
### bpucker@cebitec.uni-bielefeld.de ###
### v0.1 ###
__usage__ = """
python analyze_cov_per_contig.py
--cov <COVERAGE_FILE>
--out <OUTPUT_FOLDER>
"""
import sys, os
import numpy as np
import matplotlib.pyplot as plt
# --- end of imports --- #
def analyze_coverage( cov_file, cov_summary_file):
"""! @brief analyze coverage per contig and summarize in one file """
with open( cov_summary_file, "w" ) as out:
with open( cov_file, "r" ) as f:
line = f.readline()
header = line.split('\t')[0]
covs = []
while line:
parts = line.strip().split('\t')
if parts[0] != header:
out.write( header + '\t' + str( np.mean( covs ) ) + '\t' + str( np.median( covs ) ) + '\t' + str( len( covs ) ) + '\n' )
covs = []
header = parts[0]
covs.append( float( parts[-1] ) )
line = f.readline()
out.write( header + '\t' + str( np.mean( covs ) ) + '\t' + str( np.median( covs ) ) + '\t' + str( len( covs ) ) + '\n' )
def load_coverage_values( cov_summary_file ):
"""! @brief load average coverage value per chromosome """
mean_cov_per_contig = {}
median_cov_per_contig = {}
contig_lengths = {}
with open( cov_summary_file, "r" ) as f:
line = f.readline()
while line:
parts = line.strip().split('\t')
mean_cov_per_contig.update( { parts[0]: float( parts[1] ) } )
median_cov_per_contig.update( { parts[0]: float( parts[2] ) } )
contig_lengths.update( { parts[0]: int( parts[3] ) } )
line = f.readline()
return mean_cov_per_contig, median_cov_per_contig, contig_lengths
def generate_figure( mean_cov_values, median_cov_values, figfile ):
"""! @brief generate histogram of coverage distribution """
fig, ax = plt.subplots()
ax.hist( mean_cov_values, bins=5000, color="lime", alpha=0.5, label="mean" )
ax.hist( median_cov_values, bins=5000, color="magenta", alpha=0.5, label="median" )
ax.set_xlim( 0,200 )
ax.legend()
ax.plot( [ 15, 15 ], [ 0,20 ], color="black" )
ax.plot( [ 50, 50 ], [ 0, 20 ], color="black" )
ax.set_xlabel( "average coverage per contig" )
ax.set_ylabel( "number of contigs" )
plt.subplots_adjust( left=0.1, bottom=0.1, top=0.99, right=0.99 )
fig.savefig( figfile, dpi=300 )
def calculate_genome_size( mean_cov_per_contig, contig_lengths ):
"""! @brief calculate genome size based on coverage """
genome_size = 0
for contig in contig_lengths.keys():
if mean_cov_per_contig[ contig ] < 30:
genome_size += 2*contig_lengths[ contig ]
elif mean_cov_per_contig[ contig ] < 150:
genome_size += contig_lengths[ contig ]
else:
genome_size += contig_lengths[ contig ] * ( mean_cov_per_contig[ contig ] / 55 )
print "estimated haploid genome size: " + str( genome_size / 2000000.0 ) + " Mbp"
def main( arguments ):
"""! @brief run everything """
cov_file = arguments[ arguments.index('--cov')+1 ]
output_directory = arguments[ arguments.index('--out')+1 ]
if output_directory[-1] != "/":
output_directory += "/"
if not os.path.exists( output_directory ):
os.makedirs( output_directory )
cov_summary_file = output_directory + "cov_summary_file.txt"
figfile = output_directory + "cov_summary.pdf"
analyze_coverage( cov_file, cov_summary_file)
mean_cov_per_contig, median_cov_per_contig, contig_lengths = load_coverage_values( cov_summary_file )
generate_figure( mean_cov_per_contig.values(), median_cov_per_contig.values(), figfile )
calculate_genome_size( mean_cov_per_contig, contig_lengths )
if '--cov' in sys.argv and '--out' in sys.argv:
main( sys.argv )
else:
sys.exit( __usage__ )