BioinformaticsBioinformaticsPeer-reviewed journal article
Cluster efficient pangenome graph construction with nf-core/pangenome
Simon HeumosiD, Michael HeueriD, Friederike HansseniD, Lukas HeumosiD, Andrea GuarracinoiD, Peter HeringeriD, Philipp EhmeleiD, Pjotr PrinsiD, Erik P. GarrisoniD, Sven NahnseniD
Abstract
MotivationPangenome graphs offer a comprehensive way of capturing genomic variability across multiple genomes. However, current construction methods often introduce biases, excluding complex sequences or relying on references. The PanGenome Graph Builder (PGGB) addresses these issues. To date, though, there is no state-of-the-art pipeline allowing for easy deployment, efficient and dynamic use of available resources, and scalable usage at the same time. ResultsTo overcome these limitations, we present nf-core/pangenome, a reference-unbiased approach implemented in Nextflow following nf-cores best practices. Leveraging biocontainers ensures portability and seamless deployment in HPC environments. Unlike PGGB, nf-core/pangenome distributes alignments across cluster nodes, enabling scalability. Demonstrating its efficiency, we constructed pangenome graphs for 1000 human chromosome 19 haplotypes and 2146 E. coli sequences, achieving a two to threefold speedup compared to PGGB without increasing greenhouse gas emissions. Availabilitynf-core/pangenome is released under the MIT open-source license, available on GitHub and Zenodo, with documentation accessible at https://nf-co.re/pangenome/1.1.2/docs/usage. Contactsimon.heumos@qbic.uni-tuebingen.de, sven.nahnsen@qbic.uni-tuebingen.de
- organism: human
- organism: E. coli
- assay: whole-genome sequencing
- research focus: database / resource
- approach: statistical model
- Citations
- 20
- FWCI
- 2.71
- Field percentile
- top 8%
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