/
postprocess_alignments.py
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/
postprocess_alignments.py
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#! /usr/bin/env python
"""
Create a "hit list" of how much will be removed at what ranks.
"""
import sys
import argparse
import csv
import os.path
import json
import glob
import itertools
from . import alignplot
from .alignplot import AlignmentContainer
import sourmash
from . import utils
# minimum alignment size, in kb
# in practice, this is also bounded by the aligner used - mashmap generally
# won't go below 5kb, for example.
MIN_ALIGN_SIZE=0.5
def main(args):
"Main entry point for scripting. Use cmdline for command line entry."
inp_dir = args.input_directory
hitlist = utils.CSV_DictHelper(args.hit_list, 'genome')
genomebase = os.path.basename(args.genome)
with open(args.matches_json, 'rt') as fp:
matches_info = json.load(fp)
##
genome_lin = utils.make_lineage(matches_info['query_info']['genome_lineage'])
match_rank = matches_info['query_info']['match_rank']
scaled = matches_info['query_info']['scaled']
clean_accs = []
clean_accs_d = {}
dirty_accs = []
dirty_accs_d = {}
for match_acc, acc_info in matches_info['matches'].items():
match_counts = acc_info['counts']
match_type = acc_info['match_type']
match_lineage = acc_info['lineage']
assert match_acc not in clean_accs_d
assert match_acc not in dirty_accs_d
if match_type == 'clean':
clean_accs.append((match_acc, match_lineage, match_counts))
clean_accs_d[match_acc] = (match_lineage, match_counts)
elif match_type == 'dirty':
dirty_accs.append((match_acc, match_lineage, match_counts))
dirty_accs_d[match_acc] = (match_lineage, match_counts)
clean_accs.sort(key=lambda x: -x[2])
dirty_accs.sort(key=lambda x: -x[2])
output = []
output.append(f'loaded {len(clean_accs)} clean accs and {len(dirty_accs)} dirty accs')
output.append('')
output.append(f'query genome lineage: `{utils.display_lineage(genome_lin)}`\n')
output.append(f'genomes that match the lineage at {match_rank}:')
for (match_acc, match_lineage, match_counts) in clean_accs:
output.append(f'* `{match_acc}` with est {match_counts*scaled} kb;\n`{match_lineage}`')
output.append('')
output.append('genomes that do NOT match the lineage:')
for (match_acc, match_lineage, match_counts) in dirty_accs:
output.append(f'* `{match_acc}` with est {match_counts*scaled} kb;\n`{match_lineage}`')
print("\n".join(output))
def load_target_pairs(match_list):
pairs = []
for acc, _, _ in match_list:
filename = glob.glob(f'genbank_genomes/{acc}*.fna.gz')
#assert len(filename) == 1, filename # @CTB
filename = filename[0]
pairs.append((acc, filename))
return pairs
contaminant_pairs = load_target_pairs(dirty_accs)
clean_pairs = load_target_pairs(clean_accs)
contigs_by_acc = {}
contigs_to_acc = {}
all_sizes = {}
all_sizes.update(alignplot.load_contig_sizes(args.genome))
for acc, _, _ in itertools.chain(clean_accs, dirty_accs):
filename = glob.glob(f'genbank_genomes/{acc}*.fna.gz')
filename = filename[0]
sizes = alignplot.load_contig_sizes(filename)
all_sizes.update(sizes)
contigs_by_acc[acc] = sizes
for contig_name in sizes:
assert contig_name not in contigs_to_acc
contigs_to_acc[contig_name] = acc
dirty_alignment = AlignmentContainer(genomebase, args.genome, contaminant_pairs, f'{inp_dir}/hitlist-accessions.info.csv')
results = {}
for t_acc, _ in contaminant_pairs:
mashmap_file = f'{inp_dir}/{genomebase}.x.{t_acc}.mashmap.align'
results[t_acc] = dirty_alignment._read_mashmap(mashmap_file)
dirty_alignment.results = results
print(f'filtering dirty alignments to query size >= 500 and identity >= {args.min_align_pident}%')
dirty_alignment.filter(query_size=MIN_ALIGN_SIZE, pident=args.min_align_pident)
print(f'filtering dirty alignments to min query coverage {args.min_query_coverage}')
dirty_alignment = dirty_alignment.filter_by_query_coverage(args.min_query_coverage)
sum_dirty_kb = sum(dirty_alignment.calc_shared().values())
print(f'query genome lineage: {utils.display_lineage(genome_lin)}')
print(f'**dirty bases: {sum_dirty_kb:.1f}kb of alignments to query genome, across all targets.**')
all_regions = []
for t_acc, vv in dirty_alignment.results.items():
all_regions.extend(vv)
regions_by_query = alignplot.group_regions_by(all_regions, 'query')
query_shared = dirty_alignment.calc_shared()
sum_to_remove = 0
for k, covered_bases in query_shared.items():
sum_to_remove += all_sizes[k]
print(f'\nremoving {all_sizes[k]:.0f}kb with {covered_bases:.0f}kb dirty, contig name {k}.')
for region in regions_by_query[k]:
source_acc = contigs_to_acc[region.target]
source_lin = utils.make_lineage(dirty_accs_d[source_acc][0])
query_aligned = alignplot.region_size(region, 'query')
print(f' {query_aligned:.0f}kb aligns to {source_acc}:{region.target} at {region.pident:.1f}%')
print(f' ({utils.display_lineage(source_lin)})')
disagree_rank = utils.find_disagree_rank(genome_lin, source_lin)
query_at_rank = utils.pop_to_rank(genome_lin, disagree_rank)[-1].name
source_at_rank = utils.pop_to_rank(source_lin, disagree_rank)[-1].name
print(f" ** disagreement at rank '{disagree_rank}'; genome {query_at_rank}, source {source_at_rank}")
print(f'\nremoving {sum_to_remove:.0f}kb total in contigs >= {args.min_query_coverage*100:.0f}% dirty, based on alignments at {args.min_align_pident:.0f}% identity over {MIN_ALIGN_SIZE:.2f}kb or more')
###
with open(args.json_out, 'wt') as fp:
pass
with open(args.summary_csv, 'wt') as fp:
w = csv.writer(fp)
w.writerow(["genome", "dirty_kb", "remove_kb"])
w.writerow([genomebase, round(sum_dirty_kb, 1), round(sum_to_remove, 1)])
return 0
def cmdline(sys_args):
"Command line entry point w/argparse action."
p = argparse.ArgumentParser(sys_args)
p.add_argument('--input-directory', required=True)
p.add_argument('--hit-list', required=True)
p.add_argument('--matches-json', required=True)
p.add_argument('--json-out', required=True)
p.add_argument('--summary-csv', required=True)
p.add_argument('--min-query-coverage', type=float, required=True)
p.add_argument('--min-align-pident', type=float, required=True)
p.add_argument('genome')
args = p.parse_args()
return main(args)
# execute this, when run with `python -m`.
if __name__ == '__main__':
returncode = cmdline(sys.argv[1:])
sys.exit(returncode)