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Source: python-nanoget
Section: science
Priority: optional
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Andreas Tille <tille@debian.org>,
Étienne Mollier <emollier@debian.org>
Build-Depends: debhelper-compat (= 13),
dh-sequence-python3,
python3,
python3-setuptools,
python3-biopython,
python3-nanomath,
python3-pysam
Standards-Version: 4.6.2
Vcs-Browser: https://salsa.debian.org/med-team/python-nanoget
Vcs-Git: https://salsa.debian.org/med-team/python-nanoget.git
Homepage: https://github.com/wdecoster/nanoget
Rules-Requires-Root: no
Package: python3-nanoget
Architecture: all
Section: python
Depends: ${python3:Depends},
${misc:Depends},
python3-biopython,
python3-pysam,
python3-nanomath,
python3-pandas (>= 2.0.0)
Description: extract information from Oxford Nanopore sequencing data and alignments
The Python3 module nanoget provides functions to extract useful metrics
from Oxford Nanopore sequencing reads and alignments.
.
Data can be presented in the following formats, using the following functions:
.
* sorted bam file process_bam(bamfile, threads)
* standard fastq file process_fastq_plain(fastqfile, 'threads')
* fastq file with metadata from MinKNOW or Albacore
process_fastq_rich(fastqfile)
* sequencing_summary file generated by Albacore
process_summary(sequencing_summary.txt, 'readtype')
.
Fastq files can be compressed using gzip, bzip2 or bgzip. The data is
returned as a pandas DataFrame with standardized headernames for
convenient extraction. The functions perform logging while being called
and extracting data.
Package: python3-nanoget-examples
Architecture: all
Section: python
Depends: ${misc:Depends},
Enhances: python3-nanoget
Multi-Arch: foreign
Description: example data for python3-nanoget (dealing with Oxford Nanopore data)
The Python3 module nanoget provides functions to extract useful metrics
from Oxford Nanopore sequencing reads and alignments.
.
Data can be presented in the following formats, using the following functions:
.
* sorted bam file process_bam(bamfile, threads)
* standard fastq file process_fastq_plain(fastqfile, 'threads')
* fastq file with metadata from MinKNOW or Albacore
process_fastq_rich(fastqfile)
* sequencing_summary file generated by Albacore
process_summary(sequencing_summary.txt, 'readtype')
.
Fastq files can be compressed using gzip, bzip2 or bgzip. The data is
returned as a pandas DataFrame with standardized headernames for
convenient extraction. The functions perform logging while being called
and extracting data.
.
This package just contains an example script and the data to run the example.
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