| name | taxvamb |
| description | TaxVAMB — semisupervised bimodal VAE binning that integrates TNF, coabundance, and contig taxonomy for improved MAG recovery (incl. incomplete genomes and long-read sets). Use when: (1) Taxonomy-informed binning, (2) Comparing to VAMB/AAMB/SemiBin2, (3) Single-sample setups needing more HQ bins. Upstream: https://github.com/RasmussenLab/vamb. Taxonomy labels are an input feature — garbage taxonomy in, garbage bins out.
|
| license | MIT |
| category | analysis-tools |
| tags | ["binning","TaxVAMB","VAMB","taxonomy","deep-learning"] |
| upstream | https://github.com/RasmussenLab/vamb |
| stage | binning |
TaxVAMB
Upstream: RasmussenLab/vamb
Citation
Kutuzova, S. et al. Improving metagenome binning by integrating intrinsic
features and taxonomy. Nat. Biotechnol. (2026).
https://doi.org/10.1038/s41587-026-03098-0
See also docs/references.md.
Analytical thinking
TaxVAMB adds contig-level taxonomy into a VAMB-style VAE. Strong reported
gains on CAMI2 human sets and long-read data, and on incomplete genomes — but
performance inherits taxonomy DB bias. Prefer high-quality taxonomic inputs;
do not circularly claim novelty from taxonomy-informed bins without independent
QC (checkm2, gunc, gtdbtk).
How to run
Related skills
vamb · aamb · metabuli · mmseqs2 · basalt · checkm2 · tool-selection
