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"""Functions for exhaustive adapter search
incorporating with read mapping process.
"""
import re
import os.path
import subprocess
import fileinput
from dnapilib.io_utils import get_file_obj
from dnapilib.io_utils import fastq_sequence
from dnapilib.io_utils import fastq_record
def rm_temp_dir(temp_dir):
"""Remove temporary directory.
"""
if temp_dir:
if os.path.exists(temp_dir):
subprocess.call("rm -r {}".format(temp_dir).split())
def clip_adapter(fp, aseed, tm5, tm3, min_len, max_len):
"""Return adapter-clipped clean reads.
"""
seed_len = len(aseed)
pp = re.compile("(.*)"+aseed, re.IGNORECASE)
for seq in fastq_sequence(fp):
if len(seq) < tm5 or len(seq) < tm3:
raise Exception("trimming length is too large")
match = pp.search(seq)
if not match:
continue
end = match.end() - seed_len
clipped_seq = seq[tm5 : end-tm3]
L = len(clipped_seq)
if min_len <= L and L <= max_len:
yield clipped_seq
def to_fasta(fastq, fasta, aseed, tm5, tm3, min_len, max_len):
"""Write FASTA containing clean reads, and return
the number of the reads.
"""
fq_obj = get_file_obj(fastq)
if "RAW_INPUT".startswith(aseed):
iterator = fastq_sequence(fq_obj)
else:
iterator = clip_adapter(fq_obj, aseed, tm5, tm3, min_len, max_len)
fas = {}
clean_read_count = 0
for seq in iterator:
fas[seq] = fas.get(seq, 0) + 1
fa_obj = open(fasta, "w")
for seq, cnt in fas.items():
clean_read_count += cnt
fa_obj.write(">{0}_{1}\n{0}\n".format(seq, cnt))
fa_obj.close()
fq_obj.close()
return clean_read_count
def fastq_input_prep(fastq, ratio, temp_dir):
"""Write FASTQ in the temporary directory, and retrun
(subsampled) FASTQ name, the total read count,
standard deviation of read lengths.
"""
num = int(1/ratio)
read_count = 0.0
stats = {}
fq_out = "{}/input.fq".format(temp_dir)
fq_obj = get_file_obj(fastq)
fout = open(fq_out, "w")
for i, rec in enumerate(fastq_record(fq_obj)):
if i % num == 0:
fout.write(rec)
read_count += 1
L = len(rec.split("\n")[1])
stats[L] = stats.get(L,0) + 1
fout.close()
fq_obj.close()
mean = sum([L*c for L,c in stats.items()]) / read_count
sum_square = sum([(L-mean)**2 * c for L,c in stats.items()])
sd = (sum_square / read_count)**0.5
return fq_out, read_count, sd
def count_mapped_read_sam(samout):
"""Return the number of mapped reads to the genome.
"""
if not os.path.exists(samout):
raise Exception("can't open SAM")
mapped = set()
for x in fileinput.input(samout):
if not x or x.startswith("@"):
continue
x = x.rstrip().split("\t")
if x[2] != '*':
mapped.add(x[0])
cnt = sum([int(n.split('_')[1]) for n in mapped])
return cnt
def map_clean_reads(fastq, adapter, tm5, tm3,
min_len, max_len, map_command, temp_dir):
"""Execute mapping command, and return the numbers
of clean and mapped reads.
"""
fasta = "{0}/insert_{1}.fa".format(temp_dir, adapter)
samout = "{}/output.sam".format(temp_dir)
clipped = to_fasta(fastq, fasta, adapter, tm5, tm3, min_len, max_len)
map_command = map_command.replace("@in",fasta).replace("@out",samout)
map_command += " 2> /dev/null"
if subprocess.call(map_command, shell=True) != 0:
raise Exception("mapping failed, check command line")
mapped = count_mapped_read_sam(samout)
return clipped, mapped
def make_stats_report(table, sampled_read, subsample_rate, prefix_match,
sd, fastq, output_dir, temp_dir, no_output_files):
"""Report read statistics with predicted adapters.
"""
out = ["# sampled_reads={} (total_reads * {:.2f})".format(
int(sampled_read), subsample_rate)]
out.append("\t".join([
"# 3'adapter",
"reads_extracted",
"(reads_extracted/sampled_reads)%",
"reads_mapped",
"(reads_mapped/sampled_reads)%",
"params_k:r"]))
max_mapped_read = -1
max_index = -1
for i, x in enumerate(table):
if x[3] > max_mapped_read:
max_mapped_read = x[3]
max_index = i
out.append("{}\t{}\t{:.2f}\t{}\t{:.2f}\t{}".format(*x))
optimal = [table[max_index][0]]
fq_prefix = os.path.basename(fastq).split(".")[0]
if table[max_index][4] < 20:
optimal.append("/POOR_QUALITY")
if optimal[0] == "RAW_INPUT":
if sd:
out.append("# input reads look already clean!")
else:
optimal.append("?")
else:
if no_output_files:
pass
else:
if not os.path.exists(output_dir):
subprocess.call("mkdir {}".format(output_dir).split())
aseq = optimal[0][:prefix_match]
fa_tmp = "{}/insert_{}.fa".format(temp_dir, aseq)
fa_out = "{}/{}_{}.fa".format(output_dir, fq_prefix, aseq)
subprocess.call(("mv {} {}".format(fa_tmp,fa_out)).split())
out.insert(0, "optimal_3'adapter={}\n".format(''.join(optimal)))
report = "\n".join(out)
print(report)
if not no_output_files:
f = open("{}/{}_report.txt".format(output_dir, fq_prefix), "w")
f.write(report + "\n")
f.close()
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