comparison merge_pcr_duplicates.py @ 8:17ef0e0dae68 draft

Uploaded

7:bb59215dfd8f

Barcodes containing uncalled base 'N' are removed. By default output is written
to stdout.

Input:
* bed6 file containing alignments with fastq read-id in name field
* fasta library with fastq read-id as sequence ids

Output:
* bed6 file with random barcode in name field and number of PCR duplicates as
  score, sorted by fields chrom, start, stop, strand, name

    help="Path to fasta barcode library.")
# optional arguments
parser.add_argument(
    "-o", "--outfile",
    help="Write results to this file.")
# misc arguments
parser.add_argument(
    "-v", "--verbose",
    help="Be verbose.",
    action="store_true")

    logging.info("  outfile: '{}'".format(args.outfile))
logging.info("")

# load barcode library into dictionary
input_handle = open(args.bclib, "rU")
input_seq_iterator = SeqIO.parse(input_handle, "fasta")
bcs = pd.DataFrame.from_records(
    data=fasta_tuple_generator(input_seq_iterator),
    columns=["read_id", "bc"])

# load alignments

8:17ef0e0dae68

Barcodes containing uncalled base 'N' are removed. By default output is written
to stdout.

Input:
* bed6 file containing alignments with fastq read-id in name field
* fastq library of random barcodes

Output:
* bed6 file with random barcode in name field and number of PCR duplicates as
  score, sorted by fields chrom, start, stop, strand, name

    help="Path to fasta barcode library.")
# optional arguments
parser.add_argument(
    "-o", "--outfile",
    help="Write results to this file.")
parser.add_argument(
    "--fasta-library",
    dest="fasta_library",
    action="store_true",
    help="Read random barcode library as fasta format.")
# misc arguments
parser.add_argument(
    "-v", "--verbose",
    help="Be verbose.",
    action="store_true")

    logging.info("  outfile: '{}'".format(args.outfile))
logging.info("")

# load barcode library into dictionary
input_handle = open(args.bclib, "rU")
if args.fasta_library:
    input_seq_iterator = SeqIO.parse(input_handle, "fasta")
else:
    input_seq_iterator = SeqIO.parse(input_handle, "fastq")
bcs = pd.DataFrame.from_records(
    data=fasta_tuple_generator(input_seq_iterator),
    columns=["read_id", "bc"])

# load alignments