nf-core pipeline · differential-expression, quantseq, slamseq
nf-core/slamseq
A Nextflow pipeline for processing SLAMSeq sequencing data, quantifying conversions, and identifying target genes.
What nf-core/slamseq is
nf-core/slamseq is a bioinformatics pipeline developed for the analysis of SLAMSeq (thiol-linked alkylation for the metabolic sequencing of RNA) data. Originally created by Tobias Neumann for use at the Research Institute of Molecular Pathology (IMP) in Vienna, the pipeline is maintained as part of the nf-core community project.
SLAMSeq allows researchers to study RNA dynamics and newly synthesised transcripts by monitoring chemically induced nucleotide conversions. The nf-core/slamseq workflow addresses the need for standardised processing of these datasets, turning raw sequencing outputs into quantifiable conversions and direct target identifications.
What the pipeline does
The pipeline processes raw SLAMSeq datasets using Slamdunk to detect and quantify experimentally induced nucleotide conversions. Downstream differential expression and inference of direct transcriptional targets are subsequently carried out using DESeq2.
Implemented in Nextflow, the workflow coordinates pipeline steps across multiple execution environments, using container engines such as Docker and Singularity or Conda environments to manage dependencies.
Key facts
- Pipeline engine
- Nextflow
- Core tools
- Slamdunk, DESeq2
- Original author
- Tobias Neumann (IMP Vienna)
- Latest release
- 1.0.0 (2020-05-14)
- Input requirements
- Design file (design.tsv) and reference genome (e.g. GRCh38)
- Environment support
- Docker, Singularity, Conda
What you can use it for
- SLAMSeq nucleotide conversion quantification
- Processing raw reads from metabolically labelled RNA experiments through Slamdunk to quantify conversion rates.
- Transcriptional target identification
- Applying DESeq2 downstream of conversion quantification to identify direct transcriptional targets across experimental conditions.
- QuantSeq metabolic labelling analysis
- Analysing QuantSeq-based transcriptomic datasets designed to measure metabolic RNA labelling.
Run it here
The research agent runs nf-core/slamseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Integrates conversion quantification by Slamdunk and target inference by DESeq2 into a single automated workflow.
- Provides containerisation profiles using Docker and Singularity to ensure reproducible executions across compute platforms.
- Includes a preconfigured test profile for immediate validation on a minimal test dataset.
Watch out for
- Conda dependency resolution is not recommended as the primary execution method and should only be used as a last resort.
- Requires structured design files and indexed reference genome files formatted for the pipeline.
Questions
Can I run nf-core/slamseq without my own cluster?
The research agent runs nf-core/slamseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/slamseq under?
MIT, like every nf-core pipeline.
What type of data does nf-core/slamseq process?
It is designed specifically for high-throughput sequencing data generated using the SLAMSeq protocol.
Which software performs the conversion quantification?
The pipeline runs Slamdunk to quantify experimentally induced nucleotide conversions from sequencing reads.
How are transcriptional targets identified?
Direct transcriptional targets are determined using DESeq2 for differential analysis.
Can the pipeline be verified before running full experimental datasets?
Yes, running the pipeline with the test profile executes a minimal dataset to verify correct installation and setup.
How to cite it
doi: 10.5281/zenodo.3826585
Written from nf-core/slamseq's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.