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BWT.py
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BWT.py
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from app.settings import *
import csv, glob
from multiprocessing import Pool
import time
import statistics
class BWT(object):
"""
Class to align metagenomic reads to CARD and wildCARD reference using bwa or bowtie2 and
provide reports (gene, allele report and read level reports).
"""
def __init__(self, aligner, include_wildcard, include_baits, read_one, read_two, threads, output_file, debug, clean, local_database, mapq, mapped, coverage):
"""Creates BWT object."""
self.aligner = aligner
self.read_one = read_one
self.read_two = read_two
self.threads = threads
self.output_file = output_file
self.local_database = local_database
self.db = path
self.data = data_path
self.include_wildcard = include_wildcard
self.include_baits = include_baits
self.mapq = mapq
self.mapped = mapped
self.coverage = coverage
if self.local_database:
self.db = LOCAL_DATABASE
self.data = LOCAL_DATABASE
# index dbs
self.indecies_directory = os.path.join(self.db,"bwt")
logger.info("card")
self.reference_genome = os.path.join(self.data, "card_reference.fasta")
self.index_directory_bowtie2 = os.path.join(self.db, self.indecies_directory, "card_reference", "{}".format("bowtie2"))
self.index_directory_bwa = os.path.join(self.db, self.indecies_directory, "card_reference", "{}".format("bwa"))
if self.include_baits == True:
logger.info("baits")
self.reference_genome_baits = os.path.join(self.data, "baits_reference.fasta")
self.index_directory_bowtie2_baits = os.path.join(self.db, self.indecies_directory, "baits_reference", "{}".format("bowtie2_baits"))
self.index_directory_bwa_baits = os.path.join(self.db, self.indecies_directory, "baits_reference", "{}".format("bwa_baits"))
# card and variants
if self.include_wildcard == True:
logger.info("card variants")
self.reference_genome = os.path.join(self.data, "card_wildcard_reference.fasta")
self.index_directory_bowtie2 = os.path.join(self.db, self.indecies_directory, "card_wildcard_reference", "{}".format("bowtie2"))
self.index_directory_bwa = os.path.join(self.db, self.indecies_directory, "card_wildcard_reference", "{}".format("bwa"))
# outputs
self.working_directory = os.path.join(os.getcwd())
self.output_sam_file = os.path.join(self.working_directory, "{}.temp.sam".format(self.output_file))
self.output_sam_file_baits = os.path.join(self.working_directory, "{}.baits.temp.sam".format(self.output_file))
self.output_bam_file = os.path.join(self.working_directory, "{}.temp.bam".format(self.output_file))
self.output_bam_file_baits = os.path.join(self.working_directory, "{}.baits.temp.bam".format(self.output_file))
self.output_bam_sorted_file = os.path.join(self.working_directory, "{}.sorted.temp.bam".format(self.output_file))
self.sorted_bam_sorted_file_length_100 = os.path.join(self.working_directory, "{}.sorted.length_100.temp.bam".format(self.output_file))
self.unmapped = os.path.join(self.working_directory, "{}.unmapped.temp.bam".format(self.output_file))
self.mapped = os.path.join(self.working_directory, "{}.mapped.temp.bam".format(self.output_file))
self.mapping_overall_stats = os.path.join(self.working_directory, "{}.overall_mapping_stats.temp.txt".format(self.output_file))
self.mapping_artifacts_stats = os.path.join(self.working_directory, "{}.artifacts_mapping_stats.temp.txt".format(self.output_file))
self.mapping_reference_stats = os.path.join(self.working_directory, "{}.reference_mapping_stats.temp.txt".format(self.output_file))
self.mapping_baits_stats = os.path.join(self.working_directory, "{}.baits_mapping_stats.temp.txt".format(self.output_file))
self.baits_reads_count = os.path.join(self.working_directory, "{}.baits_reads_count.temp.txt".format(self.output_file))
self.reads_baits_count = os.path.join(self.working_directory, "{}.reads_baits_count.temp.txt".format(self.output_file))
self.aro_term_reads = os.path.join(self.working_directory, "{}.aro_term_reads.temp.txt".format(self.output_file))
self.output_tab = os.path.join(self.working_directory, "{}.temp.txt".format(self.output_file))
self.output_tab_sequences = os.path.join(self.working_directory, "{}.seqs.temp.txt".format(self.output_file))
self.output_tab_coverage = os.path.join(self.working_directory, "{}.coverage.temp.txt".format(self.output_file))
self.output_tab_coverage_all_positions = os.path.join(self.working_directory, "{}.coverage_all_positions.temp.txt".format(self.output_file))
self.output_tab_coverage_all_positions_summary = os.path.join(self.working_directory, "{}.coverage_all_positions.summary.temp.txt".format(self.output_file))
self.model_species_data_type = os.path.join(self.working_directory, "{}.model_species_data_type.temp.txt".format(self.output_file))
self.allele_mapping_data_json = os.path.join(self.working_directory, "{}.allele_mapping_data.json".format(self.output_file))
self.allele_mapping_data_tab = os.path.join(self.working_directory, "{}.allele_mapping_data.txt".format(self.output_file))
self.gene_mapping_data_tab = os.path.join(self.working_directory, "{}.gene_mapping_data.txt".format(self.output_file))
self.baits_mapping_data_tab = os.path.join(self.working_directory, "{}.baits_mapping_data.temp.txt".format(self.output_file))
self.baits_mapping_data_json = os.path.join(self.working_directory, "{}.baits_mapping_data.temp.json".format(self.output_file))
self.reads_mapping_data_json = os.path.join(self.working_directory, "{}.reads_mapping_data.temp.json".format(self.output_file))
# map baits to complete genes
self.baits_card_sam = os.path.join(self.working_directory, "{}.baits_card.temp.sam".format(self.output_file))
self.baits_card_bam = os.path.join(self.working_directory, "{}.baits_card.temp.bam".format(self.output_file))
self.baits_card_tab = os.path.join(self.working_directory, "{}.baits_card.temp.txt".format(self.output_file))
self.baits_card_json = os.path.join(self.working_directory, "{}.baits_card.temp.json".format(self.output_file))
self.baits_card_data_tab = os.path.join(self.working_directory, "{}.baits_card_data.temp.txt".format(self.output_file))
self.card_baits_reads_count_json = os.path.join(self.working_directory, "{}.card_baits_reads_count.temp.json".format(self.output_file))
self.debug = debug
self.clean = clean
if self.debug:
logger.setLevel(10)
def __repr__(self):
"""Returns BWT class full object."""
return "BWT({}".format(self.__dict__)
def clean_files(self):
"""Cleans temporary files."""
if self.clean == True:
basename_output_file = os.path.splitext(os.path.basename(self.output_file))[0]
logger.info("Cleaning up temporary files...{}".format(basename_output_file))
# clean working_directory
self.clean_directory(self.working_directory, basename_output_file)
d_name, f_name = os.path.split(self.output_file)
# clean destination_directory
self.clean_directory(d_name, basename_output_file)
else:
logger.info("Clean up skipped.")
def clean_directory(self, directory, basename_output_file):
"""Cleans files in directory."""
logger.info(directory)
files = glob.glob(os.path.join(directory, "*"))
for f in files:
if os.path.basename(self.output_file) in f and ".temp" in f and os.path.isfile(f):
self.remove_file(f)
def remove_file(self, f):
"""Removes file."""
if os.path.exists(f):
try:
logger.info("Removed file: {}".format(f))
os.remove(f)
except Exception as e:
raise e
else:
logger.warning("Missing file: {}".format(f))
def create_index(self, index_directory, reference_genome):
"""
Create bwa or bowtie2 index for reference genome
"""
if self.aligner == "bowtie2":
if not os.path.exists(index_directory):
os.makedirs(index_directory)
logger.info("created index at {}".format(index_directory))
os.system("bowtie2-build --quiet {reference_genome} {index_directory} --threads {threads}".format(
index_directory=index_directory,
reference_genome=reference_genome,
threads=self.threads
)
)
else:
if not os.path.exists(index_directory):
os.makedirs(index_directory)
logger.info("created index at {}".format(index_directory))
os.system("bwa index -p {index_directory} {reference_genome}".format(
index_directory=index_directory,
reference_genome=reference_genome
)
)
def align_bowtie2_unpaired(self, reference_genome, index_directory, output_sam_file):
"""
Align unpaired reads to card or wildcard
"""
self.check_index(index_directory=index_directory, reference_genome=reference_genome)
cmd = "bowtie2 --very-sensitive-local --threads {threads} -x {index_directory} -U {unpaired_reads} -S {output_sam_file}".format(
threads=self.threads,
index_directory=index_directory,
unpaired_reads=self.read_one,
output_sam_file=output_sam_file
)
os.system(cmd)
def align_bowtie2(self, reference_genome, index_directory, output_sam_file):
"""
Align paired reads to card or wildcard
"""
self.check_index(index_directory=index_directory, reference_genome=reference_genome)
logger.info("align reads -1 {} -2 {} to {}".format(self.read_one, self.read_two, reference_genome))
cmd = "bowtie2 --quiet --very-sensitive-local --threads {threads} -x {index_directory} -1 {read_one} -2 {read_two} -S {output_sam_file}".format(
threads=self.threads,
index_directory=index_directory,
read_one=self.read_one,
read_two=self.read_two,
output_sam_file=output_sam_file
)
os.system(cmd)
def align_bowtie2_baits_to_genes(self, reference_genome, index_directory, output_sam_file):
"""
Align baits to genes
"""
self.check_index(index_directory=index_directory, reference_genome=reference_genome)
logger.info("align baits -f {} to complete genes in {}".format(self.reference_genome_baits, reference_genome))
cmd = "bowtie2 --quiet --very-sensitive-local --threads {threads} -x {index_directory} -f {unpaired_reads} -S {output_sam_file}".format(
threads=self.threads,
index_directory=index_directory,
unpaired_reads=self.reference_genome_baits,
output_sam_file=output_sam_file
)
os.system(cmd)
def align_bwa_single_end_mapping(self):
"""
Align unpaired reads to reference genome using bwa
"""
os.system("bwa mem -M -t {threads} {index_directory} {read_one} > {output_sam_file}".format(
threads=self.threads,
index_directory=self.index_directory_bwa,
read_one=self.read_one,
output_sam_file=self.output_sam_file
)
)
def align_bwa_paired_end_mapping(self, reference_genome, index_directory, output_sam_file):
"""
Align paired reads to reference genome using bwa
"""
self.check_index(index_directory=index_directory, reference_genome=reference_genome)
os.system("bwa mem -t {threads} {index_directory} {read_one} {read_two} > {output_sam_file}".format(
threads=self.threads,
index_directory=index_directory,
read_one=self.read_one,
read_two=self.read_two,
output_sam_file=output_sam_file
)
)
def convert_sam_to_bam(self, input_sam_file, output_bam_file):
"""
Convert sam file to bam file
"""
os.system("samtools view --threads {threads} -b {input_sam_file} > {output_bam_file}".format(
threads=self.threads,
output_bam_file=output_bam_file,
input_sam_file=input_sam_file
)
)
def sort_bam(self):
"""
Sort bam file
"""
os.system("samtools sort --threads {threads} -T {output_file}.sorted -o {sorted_bam_file} {unsorted_bam_file}".format(
threads=self.threads,
output_file=self.output_file,
unsorted_bam_file=self.output_bam_file,
sorted_bam_file=self.output_bam_sorted_file
)
)
def index_bam(self, bam_file):
"""
Index input bam file using 'samtools index' function
"""
os.system("samtools index {input_bam}".format(input_bam=bam_file))
def extract_alignments_with_length(self, length=10, map_quality=2):
"""
Get alignments from bam file using length as a filter (default length=10, map_quality=2)
TODO:: add filters (mapped, length, coverage and map_quality)
"""
cmd="bamtools filter -in {input_bam} -out {output_bam}".format(
input_bam=self.output_bam_sorted_file,
output_bam=self.sorted_bam_sorted_file_length_100,
length=length,
map_quality=map_quality
)
# logger.debug(cmd)
os.system(cmd)
def get_aligned(self):
"""
Get stats for aligned reads using 'samtools idxstats' function
"""
cmd = "samtools idxstats {input_bam} > {output_tab}".format(
input_bam=self.sorted_bam_sorted_file_length_100,
output_tab=self.output_tab
)
os.system(cmd)
def get_qname_rname_sequence(self):
"""
MAPQ (mapping quality - describes the uniqueness of the alignment, 0=non-unique, >10 probably unique) | awk '$5 > 0'
"""
cmd="samtools view --threads {threads} {input_bam} | cut -f 1,2,3,4,5,7 | sort -s -n -k 1,1 > {output_tab}".format(
threads=self.threads,
input_bam=self.sorted_bam_sorted_file_length_100,
output_tab=self.output_tab_sequences
)
os.system(cmd)
def get_coverage(self):
"""
Get coverage using 'samtools depth' function and write outputs to a tab-delimited file
"""
cmd="samtools depth {sorted_bam_file} > {output_tab}".format(
sorted_bam_file=self.sorted_bam_sorted_file_length_100,
output_tab=self.output_tab_coverage
)
os.system(cmd)
def get_coverage_all_positions(self):
"""
Get converage for all positions using 'genomeCoverageBed -ibam' function
BAM file _must_ be sorted by position
"""
cmd = "bedtools genomecov -ibam {sorted_bam_file} > {output_tab}".format(
sorted_bam_file=self.sorted_bam_sorted_file_length_100,
output_tab=self.output_tab_coverage_all_positions
)
os.system(cmd)
os.system("cat {output_tab} | awk '$2 > 0' | cut -f1,3,4,5 > {output_file}".format(
output_tab=self.output_tab_coverage_all_positions,
output_file=self.output_tab_coverage_all_positions_summary
)
)
def get_baits_count(self):
"""
TODO:: Get baits count
"""
pass
def get_reads_count(self):
"""
Parse tab-delimited file for read counts to a dictionary
"""
sequences = {}
with open(self.output_tab, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter='\t', quotechar='|')
for row in reader:
if int(row[2]) > 0:
sequences[row[0]] = {
"mapped": row[2],
"unmapped": row[3],
"all": format(sum(map(int, [row[2], row[3]])))
}
# write file reference_stats
with open(self.mapping_reference_stats, "w") as out:
out.write("**********************************************\n")
out.write("Stats for Reference: \n")
out.write("**********************************************\n")
out.write("\n")
out.write("how many reference terms (or ARO terms): {}\n\n".format(len(sequences.keys())))
return sequences
def get_model_details(self, by_accession=False):
"""
Parse card.json to get each model details
"""
models = {}
try:
with open(os.path.join(self.data, "card.json"), 'r') as jfile:
data = json.load(jfile)
except Exception as e:
logger.error("{}".format(e))
exit()
for i in data:
if i.isdigit():
categories = {}
taxon = []
if "model_sequences" in data[i]:
for item in data[i]["model_sequences"]["sequence"]:
taxa = " ".join(data[i]["model_sequences"]["sequence"][item]["NCBI_taxonomy"]["NCBI_taxonomy_name"].split()[:2])
if taxa not in taxon:
taxon.append(taxa)
for c in data[i]["ARO_category"]:
if data[i]["ARO_category"][c]["category_aro_class_name"] not in categories.keys():
categories[data[i]["ARO_category"][c]["category_aro_class_name"]] = []
if data[i]["ARO_category"][c]["category_aro_name"] not in categories[data[i]["ARO_category"][c]["category_aro_class_name"]]:
categories[data[i]["ARO_category"][c]["category_aro_class_name"]].append(data[i]["ARO_category"][c]["category_aro_name"])
if by_accession == False:
models[data[i]["model_id"]] = {
"model_id": data[i]["model_id"],
"ARO_accession": data[i]["ARO_accession"],
"model_name": data[i]["model_name"],
"model_type": data[i]["model_type"],
"categories": categories,
"taxon": taxon
}
else:
models[data[i]["ARO_accession"]] = {
"model_id": data[i]["model_id"],
"ARO_accession": data[i]["ARO_accession"],
"model_name": data[i]["model_name"],
"model_type": data[i]["model_type"],
"categories": categories,
"taxon": taxon
}
return models
def get_variant_details(self):
"""
Parse tab-delimited to a dictionary for all variants
"""
os.system("cat {index_file} | cut -f1,2,6,7,8,9,10 | sort > {output_file}".format(
index_file=os.path.join(self.data, "index-for-model-sequences.txt"),
output_file=self.model_species_data_type
)
)
variants = {}
# prevalence_sequence_id model_id accession species_name data_type rgi_criteria percent_identity
# 10687 2882 FBHN01 Campylobacter coli ncbi_contig Strict 67.71
# 0 1 2 3 4 5 6
'''
1031: { # model_id
'1280': { # prevalence_sequence_id
0: { # ncbi accession
data_type: "ncbi_contig",
percent_identity: "99.64",
rgi_criteria: "Strict",
species_name: 'Escherichia coli'
}
},
'438': { # prevalence_sequence_id
0: { # ncbi accession
data_type: "ncbi_contig",
percent_identity: "99.64",
rgi_criteria: "Strict",
species_name: 'Klebsiella pneumoniae'
}
},
}
'''
# prevalence_sequence_id model_id species_name accession data_type rgi_criteria percent_identity
# 10687 2882 Campylobacter coli FBHN01 ncbi_contig Strict 67.71
# 0 1 2 3 4 5 6
with open(self.model_species_data_type, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter='\t', quotechar='|')
for row in reader:
# add new model
if row[1] not in variants.keys():
variants.update({
row[1]: { # model_id
row[0]: { # prevalence_sequence_id
row[3]: #accession
{
"data_type":row[4],
"rgi_criteria":row[5],
"percent_identity":row[6],
"species_name": row[2]
}
}
}
})
# update existing model
else:
# check if prev_id is present
if row[0] not in variants[row[1]].keys():
# new prevalence_sequence_id
variants[row[1]].update({
row[0]: { # prevalence_sequence_id
row[3]: #accession
{
"data_type":row[4],
"rgi_criteria":row[5],
"percent_identity":row[6],
"species_name": row[2]
}
}
})
else:
# new accession
variants[row[1]][row[0]].update({
row[3]: #accession
{
"data_type":row[4],
"rgi_criteria":row[5],
"percent_identity":row[6],
"species_name": row[2]
}
})
return variants
def get_baits_details(self):
"""
Parse index file to a dictionary for all baits
"""
baits = {}
# 0 1 2 3 4 5 6 7
# ProbeID, GeneID, TaxaID, ARO, ProbeSeq, Upstream, Downstream,RevComp
with open(os.path.join(self.data, "baits-probes-with-sequence-info.txt"), 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
if row[0] != "ProbeID":
baits.update({
"{}|{}".format(row[0],row[3]): {
"ProbeID": row[0],
"GeneID":row[1],
"TaxaID":row[2],
"ARO": row[3],
"ProbeSeq":row[4],
"Upstream": row[5],
"Downstream": row[6],
"RevComp": row[7]
}
})
return baits
def get_alignments(self, hit_id, ref_len=0):
"""
Parse tab-delimited file into dictionary for mapped reads
"""
sequences = []
with open(self.output_tab_sequences, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter='\t', quotechar='|')
for row in reader:
if hit_id == row[2]:
sequences.append({
"qname": str(row[0]),
"flag": str(row[1]),
"rname": str(row[2]),
"pos": str(row[3]),
"mapq": str(row[4]),
"mrnm": str(row[5])
})
return sequences
def get_coverage_details(self, hit_id):
"""
Parse tab-delimited file
"""
sequences = {}
sequences.update({
hit_id: {
"covered": 0,
"uncovered": 0,
"length": 0
}
})
with open(self.output_tab_coverage_all_positions_summary, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter='\t', quotechar='|')
for row in reader:
if hit_id == row[0]:
sequences[hit_id]["covered"] = sequences[hit_id]["covered"] + int(row[1])
sequences[hit_id]["length"] = int(row[2])
sequences[hit_id]["uncovered"] = sequences[hit_id]["length"] - sequences[hit_id]["covered"]
return sequences
def filter_count_reads(self, is_mapped="false", length=""):
"""
Filter reads using mapQuality, length and isMapped
TODO:: Review to remove / include
"""
read_one=os.path.join(self.working_directory, "{}.R1.fastq".format(self.output_file))
read_two=os.path.join(self.working_directory, "{}.R2.fastq".format(self.output_file))
options = ""
if length:
options = options + " -length >={}".format(length)
if self.mapq:
options = options + " -mapQuality >={}".format(self.mapq)
filter_cmd = "bamtools filter -in {in_bam} -out {out_bam} -isMapped {is_mapped} {options}".format(
in_bam=self.sorted_bam_sorted_file_length_100,
out_bam=self.mapped,
is_mapped=is_mapped,
options=options
)
os.system(filter_cmd)
# bedtools bamtobed -i trimmedreadstocardvslamq35l40.bam > trimmedreadstocardvslamq35l40.bed
os.system("bedtools bamtobed -i {} > out.bed".format(self.mapped))
extract_cmd = "samtools fastq -1 {read_one} -2 {read_two} {in_bam}".format(
read_one=read_one,
read_two=read_two,
in_bam=self.mapped
)
os.system(extract_cmd)
read_one_count_cmd = "awk '{c}' {read_one}".format(
c="{s++}END{print s/4}",
read_one=read_one
)
read_two_count_cmd = "awk '{c}' {read_two}".format(
c="{s++}END{print s/4}",
read_two=read_two
)
return os.popen(read_one_count_cmd).readlines()[0].strip("\n") , os.popen(read_two_count_cmd).readlines()[0].strip("\n")
def get_stats(self):
"""
Get stats using 'bamtools stats' and 'samtools flagstat' function
"""
'''
stats = {
"mapped": {
"read_one": 0,
"read_two": 0
},
"unmapped": {
"read_one": 0,
"read_two": 0
}
}
# unmapped
unmapped = self.filter_count_reads()
stats["unmapped"]["read_one"] = unmapped[0]
stats["unmapped"]["read_two"] = unmapped[1]
# mapped
# max mapQuality for bowtie2 is 42
# max mapQuality for bwa is 37
# see http://www.acgt.me/blog/2014/12/16/understanding-mapq-scores-in-sam-files-does-37-42
mapped = self.filter_count_reads(is_mapped="true")
stats["mapped"]["read_one"] = mapped[0]
stats["mapped"]["read_two"] = mapped[1]
logger.info("count reads {}".format(json.dumps(stats,indent=2)))
'''
# overall stats for mapping
cmd_overall = "bamtools stats -in {} > {}".format(self.sorted_bam_sorted_file_length_100 , self.mapping_overall_stats)
# logger.info("overall mapping stats using {}".format(cmd_overall))
os.system(cmd_overall)
# stats showing duplicates
# write file reference_stats
with open(self.mapping_artifacts_stats, "w") as out:
out.write("**********************************************\n")
out.write("Stats for Artifacts: \n")
out.write("**********************************************\n")
out.write("\n")
cmd_artifacts = "samtools flagstat {} >> {}".format(self.sorted_bam_sorted_file_length_100 , self.mapping_artifacts_stats)
# logger.info("mapping artifacts stats i.e duplicates using {}".format(cmd_artifacts))
os.system(cmd_artifacts)
def find_between(s, start, end):
return (s.split(start))[1].split(end)[0]
def probes_stats(self, baits_card):
stats = {}
baits = {}
with open(self.baits_mapping_data_tab, "r") as f2:
reader=csv.reader(f2,delimiter='\t')
for row in reader:
if "ARO" in row[2]:
bait = row[2]
read = "{}|{}".format(row[0], row[1])
if bait not in baits.keys():
baits[bait] = [read]
else:
if read not in baits[bait]:
baits[bait].append(read)
aro_to_reads = {}
for m in baits:
aro = (m.split("|ARO:"))[1].split( "|")[0]
if "|ARO:{}|".format(aro) in m:
for r in baits[m]:
if aro in aro_to_reads.keys():
if r not in aro_to_reads[aro]:
aro_to_reads[aro].append(r)
else:
aro_to_reads[aro] = [r]
with open(self.aro_term_reads, "w") as tab_out3:
writer = csv.writer(tab_out3, delimiter='\t', dialect='excel')
writer.writerow([
"ARO",
"Number of Mapped Reads to baits"
])
for aro in aro_to_reads:
writer.writerow([aro, len(aro_to_reads[aro])])
with open(self.baits_mapping_data_json, "w") as outfile:
json.dump(baits, outfile)
reads_to_baits = {}
for i in baits.keys():
for j in baits[i]:
t = i
if j not in reads_to_baits.keys():
reads_to_baits[j] = [t]
else:
if t not in reads_to_baits[j]:
reads_to_baits[j].append(t)
with open(self.reads_mapping_data_json, "w") as outfile2:
json.dump(reads_to_baits, outfile2)
with open(self.baits_reads_count, "w") as tab_out2:
writer = csv.writer(tab_out2, delimiter='\t', dialect='excel')
writer.writerow([
"Bait",
"Number of Mapped Reads"
])
for item in baits:
writer.writerow([item, len(baits[item])])
probe_reads_count = {}
with open(self.baits_reads_count, 'r') as csv_file:
for row in csv.reader(csv_file, delimiter='\t'):
probe_reads_count[row[0]] = row[1]
data_out = {}
for i in baits_card:
data_out[i] = {}
for k in baits_card[i]:
probe_dict = k.split("|")
probe = "|".join(probe_dict[:-1])
if probe in probe_reads_count.keys():
data_out[i].update({k : int(probe_reads_count[probe])})
else:
data_out[i].update({k : 0})
with open(self.card_baits_reads_count_json, "w") as af:
af.write(json.dumps(data_out,sort_keys=True))
with open(self.reads_baits_count, "w") as tab_out2:
writer = csv.writer(tab_out2, delimiter='\t', dialect='excel')
writer.writerow([
"Read",
"Baits"
])
for item in reads_to_baits:
writer.writerow([item,
"; ".join(reads_to_baits[item])
])
with open(self.baits_mapping_data_tab, "r") as f2:
reader=csv.reader(f2,delimiter='\t')
for row in reader:
if "ARO" in row[2]:
term = row[2].split("|")[4]
name = row[2].split("|")[5]
probe = row[2]
probe_ok = False
for k in baits_card.keys():
if term in k:
matching = [s for s in baits_card[k] if probe in s]
if matching:
probe_ok = True
if probe_ok == True:
if term not in stats.keys():
# read reference fasta to count baits used
cmd = "cat {} | grep -c \"|{}|\"".format(self.reference_genome_baits,term)
cmd2 = "cat {} | grep -c \"|{}|\"".format(self.reads_baits_count,term)
stats[term] = {
"aro_name": name,
"total_baits": int(self.count_probes(cmd)),
"read_count": int(self.count_probes(cmd2)),
"mapped_baits": {
probe: len(baits[probe])
}
}
else:
if probe not in stats[term]["mapped_baits"].keys():
stats[term]["mapped_baits"].update({probe: len(baits[probe]) })
# write tab for probes and mapped probes
with open(self.mapping_baits_stats, "w") as tab_out:
writer = csv.writer(tab_out, delimiter='\t', dialect='excel')
writer.writerow([
"ARO Term",
"ARO Accession",
"Number of Baits",
"Number of Mapped Baits with Reads",
"Number of Reads Mapped to Baits",
"Average Number of reads per Bait",
"Number of reads per Bait Coefficient of Variation (%)"
])
'''
Coefficient of variation = (Standard Deviation / Mean ) * 100
- ratio of the standard devitation to the mean
| - | Number of Probes | Mapped Probes |
| -------- | -------- | ------------ |
| Mean | 57 | 22 |
| std_dev | 38 | 31 |
| CV | 66.67% | 140.91% |
# note - probes with >0 mapping were considered
'''
for i in stats:
accession, baits_count, baits_with_reads_count, reads_count, sample = self.get_counts(i, data_out)
standard_devitation = 0
coefficient_of_variation = 0
try:
standard_devitation = statistics.stdev(sample)
except Exception as e:
print(stats[i]["aro_name"], sample, e)
mean = statistics.mean(sample)
if mean > 0:
coefficient_of_variation = (standard_devitation / mean) * 100
writer.writerow([
stats[i]["aro_name"],
i,
baits_count,
baits_with_reads_count,
reads_count,
format(mean,'.2f'),
format(coefficient_of_variation,'.2f')
])
def get_counts(self, accession, data_out):
baits_count = 0
baits_with_reads_count = 0
reads_count = 0
sample = []
for i in data_out:
if accession in i:
baits_count = len(data_out[i])
for c in data_out[i]:
reads_count = reads_count + int(data_out[i][c])
sample.append(int(data_out[i][c]))
if int(data_out[i][c]) > 0:
baits_with_reads_count = baits_with_reads_count + 1
return accession, baits_count, baits_with_reads_count, reads_count, sample
return accession, baits_count, baits_with_reads_count, reads_count, sample
def count_probes(self, cmd):
return os.popen(cmd).readlines()[0].strip("\n")
def baits_reads_counts(self, accession):
"""
Returns
number_of_mapped_baits,
number_of_mapped_baits_with_reads,
average_bait_coverage,
bait_coverage_coefficient_of_variation
"""
if self.include_baits == True:
with open(self.mapping_baits_stats, "r") as f2:
reader=csv.reader(f2,delimiter='\t')
for row in reader:
if "ARO Term" not in row[0]:
if accession in row[1]:
return row[2], row[3], row[4], row[6]
return 0, 0, 0, 0
else:
return 0, 0, 0, 0
def get_model_id(self, models_by_accession, alignment_hit):
model_id = ""
if alignment_hit[0:22] == "Prevalence_Sequence_ID" or alignment_hit[0:4] == "ARO:":
model_id = alignment_hit.split("|")[1].split(":")[1]
else:
accession = alignment_hit.split("|")[4].split(":")[1]
try:
model_id = models_by_accession[accession]["model_id"]
except Exception as e:
logger.warning("missing aro accession: {} for alignment {} -> {}".format(accession,alignment_hit,e))
return model_id
def summary(self, alignment_hit, models, variants, baits, reads, models_by_accession):
start = time.time()
# logger.debug(alignment_hit)
coverage = self.get_coverage_details(alignment_hit)
model_id = self.get_model_id(models_by_accession, alignment_hit)
try:
alignments = self.get_alignments(alignment_hit)
mapq_l = []
mate_pair = []
mapq_average = 0
for a in alignments:
mapq_l.append(int(a["mapq"]))
if a["mrnm"] != "=" and a["mrnm"] not in mate_pair:
if "ARO:{}".format(models[model_id]["ARO_accession"]) not in a["mrnm"]:
mate_pair.append(a["mrnm"])
if len(mapq_l) > 0:
mapq_average = sum(mapq_l)/len(mapq_l)
observed_in_genomes = "no data"
observed_in_plasmids = "no data"
prevalence_sequence_id = ""
observed_data_types = []
# range_of_reference_allele_source = []
percent_identity = 0.0
# Genus and species level only (only get first two words)
observed_in_pathogens = []
database = "CARD"
reference_allele_source = "CARD curation"
# if variants and "Resistomes & Variants" in database and "ARO:" not in alignment_hit:
if "Prevalence_Sequence_ID" in alignment_hit:
database = "Resistomes & Variants"
# logger.debug("model_id: {}, alignment_hit: {}".format(model_id, alignment_hit))
if model_id in variants.keys():
_accession = ""
for s in variants[model_id]:
prevalence_sequence_id = alignment_hit.split("|")[0].split(":")[-1]
observed_in_genomes = "NO"
observed_in_plasmids = "NO"
for accession in variants[model_id][prevalence_sequence_id]:
_accession = accession
if variants[model_id][prevalence_sequence_id][accession]["data_type"] not in observed_data_types:
observed_data_types.append(variants[model_id][prevalence_sequence_id][accession]["data_type"])
if variants[model_id][prevalence_sequence_id][accession]["species_name"] not in observed_in_pathogens:
observed_in_pathogens.append(variants[model_id][prevalence_sequence_id][accession]["species_name"].replace('"', ""))
if "Resistomes & Variants" in database:
if "ncbi_chromosome" in observed_data_types:
observed_in_genomes = "YES"
if "ncbi_plasmid" in observed_data_types:
observed_in_plasmids = "YES"
# get prevalence_sequence_id Prevalence_Sequence_ID:10687|ID:2882|Name:tet(W/N/W)|ARO:3004442
if "Prevalence_Sequence_ID" in alignment_hit:
prevalence_sequence_id = alignment_hit.split("|")[0].split(":")[-1]
try:
reference_allele_source = "In silico {rgi_criteria} {percent_identity}% identity".format(
rgi_criteria=variants[model_id][prevalence_sequence_id][_accession]["rgi_criteria"],
percent_identity=variants[model_id][prevalence_sequence_id][_accession]["percent_identity"],
)
percent_identity = float(variants[model_id][prevalence_sequence_id][_accession]["percent_identity"])
except Exception as e:
reference_allele_source = ""
# logger.debug(alignment_hit)
# logger.debug(json.dumps(alignments, indent=2))
# logger.debug(json.dumps(variants[model_id], indent=2))
logger.warning("missing key with Prev_id: {}, Exception: {}, Database: {} for model_id: {}".format(prevalence_sequence_id, e, database, model_id))
else:
# provide info from model
observed_in_pathogens = models[model_id]["taxon"]
else:
# logger.debug("model_id: {}, alignment_hit: {}".format(model_id, alignment_hit))
observed_in_pathogens = models[model_id]["taxon"]
# logger.debug(coverage)
# assumption card canonical
percent_identity = 100.0
# check all clases categories
resistomes = models[model_id]["categories"]
if "AMR Gene Family" not in resistomes.keys():
resistomes["AMR Gene Family"] = []
if "Drug Class" not in resistomes.keys():
resistomes["Drug Class"] = []
if "Resistance Mechanism" not in resistomes.keys():
resistomes["Resistance Mechanism"] = []
stop = time.time()
elapsed = stop - start
logger.info("time lapsed: {} - {}".format(format(elapsed,'.3f'), alignment_hit))
# self.async_print(alignment_hit, start, stop, elapsed)
number_of_mapped_baits, number_of_mapped_baits_with_reads, average_bait_coverage, bait_coverage_coefficient_of_variation = self.baits_reads_counts(models[model_id]["ARO_accession"])
# logger.debug(">>> {}".format(alignment_hit))
return {
"id": alignment_hit,
"cvterm_name": models[model_id]["model_name"],
"aro_accession": models[model_id]["ARO_accession"],
"model_type": models[model_id]["model_type"],
"database": database,
"reference_allele_source": reference_allele_source,
"observed_in_genomes": observed_in_genomes,
"observed_in_plasmids": observed_in_plasmids,