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simulatecb.py
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simulatecb.py
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#!/usr/bin/env python3
"""
Simulate a cell barcode
"""
from argparse import ArgumentParser
from dataclasses import dataclass
from pathlib import Path
from itertools import product
import sys
import re
import dnaio
from tinyalign import hamming_distance
@dataclass
class RecordHeader:
i1: str
ligation_index: str
rt_index: str
umi: str
def main():
parser = ArgumentParser()
parser.add_argument(
"--r1", "-o", help="R1 output (simulated cell barcode)", type=Path
)
parser.add_argument("--r2", "-p", help="R2 output (cDNA)", type=Path)
parser.add_argument(
"--list",
metavar="FILE",
help="Write all possible emulated barcodes to FILE",
type=Path,
)
parser.add_argument(
"--p7-stats",
metavar="FILE",
help="Write P7 index (I1) statistics to FILE",
)
parser.add_argument("rt_indices_fasta", type=Path)
parser.add_argument("ligation_indices_fasta", type=Path)
parser.add_argument("p7_indices_fasta", type=Path)
parser.add_argument("fastq", nargs="+", type=Path)
args = parser.parse_args()
# All ligation indices end with "T".
# We append an "A" to those that only have 9 nucleotides.
rt_indices = read_fasta(args.rt_indices_fasta)
ligation_indices = make_all_same_length(read_fasta(args.ligation_indices_fasta))
p7_indices = read_fasta(args.p7_indices_fasta)
if args.list:
write_list(args.list, rt_indices, ligation_indices, p7_indices)
print("Wrote", args.list, file=sys.stderr)
p7_index_mismatches = []
r1_path = args.r1
r2_path = args.r2
with dnaio.open(r1_path, r2_path, mode="w") as outf:
for fastq_path in args.fastq:
#sample = Path(fastq_path.stem).stem # S1, ..., S96
sample = re.search("_(S[0-9]+)_", fastq_path.stem)[1]
if lane_match := re.search("_(L[0-9]+)_", fastq_path.stem):
lane = lane_match[1]
else:
lane = None
p7_index = p7_indices[sample]
mismatches = [0] * len(p7_index)
with dnaio.open(fastq_path) as inf:
for record in inf:
header = parse(record.name)
mismatches[hamming_distance(p7_index, header.i1)] += 1
record.name = record.id
barcode_record = record[:]
components = (
rt_indices[header.rt_index],
ligation_indices[header.ligation_index],
p7_index,
header.umi,
)
barcode_record.sequence = "".join(components)
barcode_record.qualities = "F" * len(barcode_record.sequence)
outf.write(barcode_record, record)
p7_index_mismatches.append([sample, lane, p7_index] + mismatches)
if args.p7_stats:
write_mismatch_stats(args.p7_stats, p7_index_mismatches)
print("Wrote", args.p7_stats, file=sys.stderr)
def read_fasta(path):
"""
Read a FASTA file and return a dict that maps a record name to a sequence
"""
with dnaio.open(path) as f:
return {record.id: record.sequence.upper() for record in f}
def make_all_same_length(d):
target_length = max(len(seq) for seq in d.values())
return {name: seq.ljust(target_length, "A") for name, seq in d.items()}
def parse(header):
fields = header.split(" ")
i1 = fields[1].split(":")[3]
assert fields[2].startswith("ligation_index=")
ligation_index = fields[2].split("=")[1]
assert fields[3].startswith("umi=")
umi = fields[3].split("=")[1]
assert fields[4].startswith("rt_index=")
rt_index = fields[4].split("=")[1]
return RecordHeader(i1=i1, ligation_index=ligation_index, rt_index=rt_index, umi=umi)
def write_list(path, *indices_dicts):
with open(path, "w") as f:
for index_keys in product(*indices_dicts):
# name = "_".join(index_keys)
seq = "".join(
index_dict[name] for index_dict, name in zip(indices_dicts, index_keys)
)
print(seq, file=f)
def write_mismatch_stats(path, stats):
with open(path, "w") as f:
print("sample", "lane", "sequence", "total", "perfect", "one_mismatch", "two_mismatches", sep="\t", file=f)
for row in stats:
print(*row[0:3], sum(row[3:]), *row[3:6], sep="\t", file=f)
if __name__ == "__main__":
main()