Since on long read data, demultiplexing takes a long time to process, and if the process fails during SAM or BAM generation for one FASTQ file, it has to run on the entire processed.fastq.gz to demultiplex and perform minimap alignment.
This is highly inefficient since the cell barcode demultiplexing is a time-intensive task.
Also, in curator_io.py the curation_master() function fails to handle the SAM file generation if --inc_contig is not used, since the code does not account for the header lines.
if options.inc_contig:
SAM.write(out_msg.decode("utf-8"))
#---filter out non-autosome---
else: ---------------------------------------> # changed it with the code below
lines = out_msg.decode("utf-8").split("\n")
for line in lines:
if line == "":
break
if line.split("\t")[2].startswith("chr"):
SAM.write(line + "\n")
Since on long read data, demultiplexing takes a long time to process, and if the process fails during SAM or BAM generation for one FASTQ file, it has to run on the entire processed.fastq.gz to demultiplex and perform minimap alignment.
This is highly inefficient since the cell barcode demultiplexing is a time-intensive task.
Also, in curator_io.py the curation_master() function fails to handle the SAM file generation if --inc_contig is not used, since the code does not account for the header lines.
if options.inc_contig:
SAM.write(out_msg.decode("utf-8"))
#---filter out non-autosome---
else: ---------------------------------------> # changed it with the code below
lines = out_msg.decode("utf-8").split("\n")
for line in lines:
if line == "":
break
if line.split("\t")[2].startswith("chr"):
SAM.write(line + "\n")