| name | plasmaag |
| description | PlasMAAG — recover plasmids and cellular genomes from metagenomes using assembly–alignment graphs and contrastive learning across samples. Use when: (1) Plasmid MAG reconstruction beyond circular-path assemblers, (2) Improving plasmid contig classification vs geNomad alone, (3) Host–plasmid association across multi-sample datasets. Upstream: https://github.com/RasmussenLab/PlasMAAG. Pair with genomad for MGE screening and checkv for viral (not plasmid) QC.
|
| license | MIT |
| category | analysis-tools |
| tags | ["plasmid","MAG","PlasMAAG","MGE","binning"] |
| upstream | https://github.com/RasmussenLab/PlasMAAG |
| stage | mining |
PlasMAAG
Upstream: RasmussenLab/PlasMAAG
Citation
Piera Líndez, P. et al. Accurate plasmid reconstruction from metagenomics data
using assembly–alignment graphs and contrastive learning. Nat. Biotechnol.
(2026). https://doi.org/10.1038/s41587-026-03005-7
See also docs/references.md.
Analytical thinking
Plasmids are under-recovered by chromosome-oriented binning and by single-sample
circular-path assemblers (fragmentation, entanglement, low coverage). PlasMAAG
builds an assembly–alignment graph across samples and bins plasmids +
organisms together with contrastive learning.
multi-sample assemblies / graphs + alignments
→ plasmaag (plasmids + cellular bins)
→ genomad (orthogonal MGE labels)
→ resistome on plasmid bins (rgi) as needed
geNomad classifies contigs; PlasMAAG aims to reconstruct plasmid genomes.
Use both when plasmid biology is a primary claim.
How to run
Related skills
genomad · basalt · vamb · rgi · microbial-mining · tool-selection
