nf-core pipeline · pangenome
nf-core/pangenome
Constructs locally directed, acyclic pangenome graphs from collections of sequences using Nextflow.
What nf-core/pangenome is
nf-core/pangenome is an analysis workflow designed to render a collection of input sequences into a pangenome graph. Originally adapted from the PanGenome Graph Builder (PGGB) by Simon Heumos and Michael Heuer, the pipeline aims to construct a graph representation that remains locally directed and acyclic while retaining large-scale genomic variation.
Preserving local linearity within the resulting graph structure is critical for downstream applications, including sequence mapping, comparative genomics, visual inspection, biological interpretation, and the long-term reuse of pangenome graphs across genomics research.
The workflow is implemented in Nextflow DSL2 and packages individual processes in Docker and Singularity containers to guarantee reproducible execution across compute infrastructures, including automated validation on AWS cloud environments.
What the pipeline does
The pipeline executes a six-stage sequential process. It begins with all-versus-all sequence alignment using WFMASH. Initial approximate alignments can be divided into equal-sized tasks, allowing base-level alignments to be distributed across distinct nodes or processes via the wfmash_chunks setting. The aligned sequences undergo graph induction through SEQWISH, followed by graph normalisation with SMOOTHXG.
Subsequent steps clean and evaluate the resulting graph structure: GFAFFIX removes structural redundancy, and ODGI extracts graph statistics and produces qualitative visualisations. Diagnostic summaries and quality metrics from across the pipeline run are aggregated into a final report using MultiQC.
Key facts
- Pipeline engine
- Nextflow DSL2
- Containerisation
- Docker, Singularity
- Alignment tool
- WFMASH
- Graph induction tool
- SEQWISH
- Graph normalisation tool
- SMOOTHXG
- Graph redundancy reduction
- GFAFFIX
- Graph analysis and visualisation
- ODGI
- Reporting tool
- MultiQC
- Original codebase
- PGGB
- Latest release
- 1.1.3 (2025-02-09)
What you can use it for
- Pangenome graph generation
- Assemble multiple genomic sequences or haplotypes into a unified, non-redundant graph representation.
- Comparative genomics
- Retain and inspect large-scale structural variation alongside shared sequence tracts across divergent haplotypes.
- Sequence read mapping preparation
- Construct locally directed and acyclic graphs suitable for read alignment and downstream variant analysis.
- Graph evaluation and inspection
- Calculate topological graph statistics and generate visualisations using ODGI, compiled alongside pipeline diagnostics in MultiQC.
Run it here
The research agent runs nf-core/pangenome 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
- Distributes the computationally intensive all-versus-all alignment across multiple cluster nodes by partitioning approximate alignments into equal chunks.
- Builds locally directed and acyclic graphs, preserving local linearity without losing large-scale structural variants.
- Encapsulates processes in modular Docker and Singularity containers for high reproducibility and portability across platforms.
- Undergoes automated continuous integration tests on AWS using full-sized datasets upon release.
Watch out for
- Pipeline execution parameters cannot be defined inside custom configuration files supplied via Nextflow's -c option; they must be passed via the CLI or -params-file.
- Requires explicit definition of haplotype counts and chunk partitioning parameters to manage cluster resource consumption and job run times efficiently.
Questions
Can I run nf-core/pangenome without my own cluster?
The research agent runs nf-core/pangenome 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/pangenome under?
MIT, like every nf-core pipeline.
How does this pipeline differ from PGGB?
nf-core/pangenome was originally adapted from PGGB, but it leverages Nextflow to split initial approximate alignments into equal-sized tasks and distribute base-level alignments across multiple nodes in a compute cluster.
What tools are included in the graph construction workflow?
The core pipeline consists of WFMASH for all-versus-all sequence alignment, SEQWISH for graph induction, SMOOTHXG for normalisation, GFAFFIX for eliminating redundancy, ODGI for topological metrics and visualisation, and MultiQC for reporting.
How can alignment jobs be tuned for shared compute clusters?
Users can set the --wfmash_chunks parameter to match the available node resources or queue constraints, splitting the heavy alignment stage across multiple tasks to avoid long waiting times.
Why is local linearity maintained in the generated graph?
Ensuring the graph is locally directed and acyclic is necessary for reliable visual inspection, comparative genomics, read mapping, and downstream integration or reuse.
How to cite it
Written from nf-core/pangenome's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.