Nextflow Registry | nf-core/ribowaltz@0.0.0-6c4ed3a

nf-core/ribowaltz @ 0.0.0-6c4ed3a

Calculation of optimal P-site offsets, diagnostic analysis and visual inspection of ribosome profiling data

riboseq psite genomics

Latest version: 0.0.0-6c4ed3a

Total downloads: 10

Source: nf-core/modules

Authors: @iraiosub

Maintainers: @iraiosub

Calculation of optimal P-site offsets, diagnostic analysis and visual inspection of ribosome profiling data

Add the following snippet to your workflow script to include this module.

include { RIBOWALTZ } from 'nf-core/ribowaltz'

MIT License

Process

`Name`	`RIBOWALTZ`

Input 3 channels

#1 tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`bam` file	Transcriptome BAM file `*.{bam}`

#2 tuple

`meta2` map	Map containing reference information for the reference genome GTF file e.g. `[ id:'Ensembl human v.111' ]`
`gtf` file	GTF file of reference genome `*.{gtf.gz,gtf}`

#3 tuple

`meta3` map	Map containing reference information for the reference genome FASTA file e.g. `[ id:'Ensembl human v.111' ]`
`fasta` file	FASTA file of reference genome `*.{fasta,fa,fasta.gz,fa.gz}`

Output 11 channels

#1 offset tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`*.psite_offset.tsv{,.gz}` file	TSV file containing P-site offsets for each read length (optional, in case no offsets could be determined, usually because no reads pass filtering criteria) `*.psite_offset.tsv.gz`

#2 psites tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`*.psite.tsv{,.gz}` file	TSV file containing P-site transcriptomic coordinates and information for each alignment (optional) `*.psite.tsv.gz`

#3 versions

`versions.yml` file	File containing software versions `versions.yml`

#4 best_offset tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`*.best_offset.txt` file	Text file with the extremity used for the offset correction step and the best offset for each sample (optional, in case no offsets could be determined, usually because no reads pass filtering criteria) `*.best_offset.txt`

#5 offset_plot tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`offset_plot/*` file	P-site offset plots for each read length (optional) `offset_plot/*`

#6 cds_coverage tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`*.cds_coverage_psite.tsv{,.gz}` file	TSV file with CDS P-site in-frame counts for each transcript (optional) `*.cds_coverage_psite.tsv.gz`

#7 ribowaltz_qc tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`ribowaltz_qc/*.pdf` file	riboWaltz diagnostic plots (optional) `ribowaltz_qc/*`

#8 ribowaltz_qc_data tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`ribowaltz_qc/*.tsv` file	TSV files containing data underlying riboWaltz QC plots including read length distribution, read length bins for P-site offset identification, ends heatmap, codon usage, P-site region distribution, frame distribution, frame distribution stratified by read length, and metaprofile P-site frequency around start/stop codons (optional) `ribowaltz_qc/*.tsv`

#9 codon_coverage_rpf tuple

`meta` map	Groovy Map containing sample information e.g. `[ id:'sample1', single_end:false ]`
`*.codon_coverage_rpf.tsv{,.gz}` file	TSV file with codon-level RPF coverage for each transcript (optional) `*.codon_coverage_rpf.tsv.gz`

#10 cds_window_coverage tuple