| name | comebin |
| description | COMEBin — contrastive multi-view representation learning for metagenomic contig binning (composition + coverage). Use when: (1) Running COMEBin as a modern single binner, (2) Comparing to SemiBin2/VAMB/MetaBAT2, (3) Co- assembly, single-sample, or multi-sample binning modes, (4) Feeding bins into ensemble refinement. Upstream: https://github.com/ziyewang/COMEBin. Prefer ensemble (basalt ★ / metawrap / dastool) for catalogues.
|
| license | MIT |
| category | analysis-tools |
| tags | ["binning","COMEBin","contrastive-learning","MAG"] |
| upstream | https://github.com/ziyewang/COMEBin |
| stage | binning |
COMEBin
Upstream: ziyewang/COMEBin
Citation
Wang, Z. et al. Effective binning of metagenomic contigs using contrastive
multi-view representation learning. Nat. Commun. 15, 585 (2024).
https://doi.org/10.1038/s41467-023-44290-z
See also docs/references.md.
Analytical thinking
COMEBin learns contig embeddings with contrastive multi-view learning:
data augmentation creates multiple views per contig; a coverage module maps
variable sample counts to fixed-dimensional coverage embeddings. Benchmarks
(CAMI II and real data) show strong near-complete MAG recovery without
SemiBin1-style taxonomic semi-supervision.
Use as a diverse single-binner in ensembles (different inductive bias from
MetaBAT2 / MaxBin2 / VAMB). Prefer multi-coverage inputs when related samples
exist (Mattock & Watson 2023 — single-coverage hides contamination).
Prerequisites
| Requirement | Notes |
|---|
| Contigs | Assembly FASTA |
| Coverage | Multi-sample BAM/abundance when available |
| QC | checkm2 after binning |
How to run
COMEBin.sh \
-a contigs.fa \
-o temp/bins/comebin \
-p temp/map \
-t 32
Decision tree
Single binner choice?
├─ Classical TNF+coverage → metabat2 / concoct / maxbin2
├─ VAE latent → vamb
├─ Self-supervised contrastive → semibin2
├─ Multi-view contrastive → comebin
└─ Catalogue / multi-assembly → basalt ★ (ensemble)
Methods caveats
- Contrastive gains are benchmark-specific; always QC with CheckM2/GUNC.
- Report mode (co-assembly / single / multi-sample) and coverage recipe.
- Combine with ensemble tools rather than publishing one binner’s raw dump.
Related skills
semibin2 · vamb · metabat2 · basalt · dastool · checkm2 ·
tool-selection · metagenomics-workflow
