def handle_compressed_input(inputfq, file_type="fastq"):
"""Return handles from compressed files according to extension.
Check for which fastq input is presented and open a handle accordingly
Can read from compressed files (gz, bz2, bgz) or uncompressed
Relies on file extensions to recognize compression
"""
ut.check_existance(inputfq)
if inputfq.endswith(('.gz', 'bgz')):
import gzip
logging.info("Nanoget: Decompressing gzipped {} {}".format(
file_type, inputfq))
return gzip.open(inputfq, 'rt')
elif inputfq.endswith('.bz2'):
import bz2
logging.info("Nanoget: Decompressing bz2 compressed {} {}".format(
file_type, inputfq))
return bz2.open(inputfq, 'rt')
elif inputfq.endswith(('.fastq', '.fq', 'fasta', '.fa', '.fas')):
return open(inputfq, 'r')
else:
logging.error(
"INPUT ERROR: Unrecognized file extension {}".format(inputfq))
sys.exit(
'INPUT ERROR:\nUnrecognized file extension in {}\n'
'Supported are gz, bz2, bgz, fastq, fq, fasta, fa and fas'.format(
inputfq))
def check_bam(bam, samtype="bam"):
"""Check if bam file is valid.
Bam file should:
- exists
- has an index (create if necessary)
- is sorted by coordinate
- has at least one mapped read
"""
ut.check_existance(bam)
samfile = pysam.AlignmentFile(bam, "rb")
if not samfile.has_index():
pysam.index(bam)
samfile = pysam.AlignmentFile(
bam, "rb") # Need to reload the samfile after creating index
logging.info(
"Nanoget: No index for bam file could be found, created index.")
if not samfile.header['HD']['SO'] == 'coordinate':
logging.error(
"Nanoget: Bam file {} not sorted by coordinate!.".format(bam))
sys.exit("Please use a bam file sorted by coordinate.")
if samtype == "bam":
logging.info(
"Nanoget: Bam file {} contains {} mapped and {} unmapped reads.".
format(bam, samfile.mapped, samfile.unmapped))
if samfile.mapped == 0:
logging.error(
"Nanoget: Bam file {} does not contain aligned reads.".format(
bam))
sys.exit(
"FATAL: not a single read was mapped in bam file {}".format(
bam))
return samfile
def process_summary(summaryfile, **kwargs):
"""Extracting information from an albacore summary file.
Only reads which have a >0 length are returned.
The fields below may or may not exist, depending on the type of sequencing performed.
Fields 1-14 are for 1D sequencing.
Fields 1-23 for 2D sequencing.
Fields 24-27, 2-5, 22-23 for 1D^2 (1D2) sequencing
Fields 28-38 for barcoded workflows
1 filename
2 read_id
3 run_id
4 channel
5 start_time
6 duration
7 num_events
8 template_start
9 num_events_template
10 template_duration
11 num_called_template
12 sequence_length_template
13 mean_qscore_template
14 strand_score_template
15 complement_start
16 num_events_complement
17 complement_duration
18 num_called_complement
19 sequence_length_complement
20 mean_qscore_complement
21 strand_score_complement
22 sequence_length_2d
23 mean_qscore_2d
24 filename1
25 filename2
26 read_id1
27 read_id2
28 barcode_arrangement
29 barcode_score
30 barcode_full_arrangement
31 front_score
32 rear_score
33 front_begin_index
34 front_foundseq_length
35 rear_end_index
36 rear_foundseq_length
37 kit
38 variant
"""
logging.info(
"Nanoget: Collecting metrics from summary file {} for {} sequencing".
format(summaryfile, kwargs["readtype"]))
ut.check_existance(summaryfile)
if kwargs["readtype"] == "1D":
cols = [
"channel", "start_time", "duration", "sequence_length_template",
"mean_qscore_template"
]
elif kwargs["readtype"] in ["2D", "1D2"]:
cols = [
"channel", "start_time", "duration", "sequence_length_2d",
"mean_qscore_2d"
]
if kwargs["barcoded"]:
cols.append("barcode_arrangement")
logging.info("Nanoget: Extracting metrics per barcode.")
try:
datadf = pd.read_csv(
filepath_or_buffer=summaryfile,
sep="\t",
usecols=cols,
)
except ValueError:
logging.error(
"Nanoget: did not find expected columns in summary file {}:\n {}".
format(summaryfile, ', '.join(cols)))
sys.exit("ERROR: expected columns in summary file {} not found:\n {}".
format(summaryfile, ', '.join(cols)))
if kwargs["barcoded"]:
datadf.columns = [
"channelIDs", "time", "duration", "lengths", "quals", "barcode"
]
else:
datadf.columns = ["channelIDs", "time", "duration", "lengths", "quals"]
logging.info(
"Nanoget: Finished collecting statistics from summary file {}".format(
summaryfile))
return ut.reduce_memory_usage(datadf.loc[datadf["lengths"] != 0].copy())