SemiBin2 vs VAMB
SemiBin2: Self-supervised deep learning metagenomic binning for short and long reads. VAMB: Variational autoencoder for metagenomic binning.
Facts below come from each project's own website, repository and paper. We don't publish benchmark claims here; see the papers for evaluations. Shared tags: binning, mags.
| Field | SemiBin2 | VAMB |
|---|---|---|
| Input | Contigs (short and long reads) | Contigs (k-mer + abundance) |
| Task | Metagenomic binning (self-supervised) | Metagenomic binning (VAE) |
| Reference database | — | — |
| License | MIT | MIT |
| Pricing | Open source | Open source |
| Released | 2023-01-09 | 2018-12-10 |
| Paper | Link | Link |
| GitHub | GitHub | GitHub |
| GitHub stars | 175 | 334 |
| Interface | Package | — |
License and commercial use
| Field | SemiBin2 | VAMB |
|---|---|---|
| Code license | MIT | MIT |
| Weights license | — | — |
| Commercial use | Permissive (commercial OK) | Permissive (commercial OK) |
| Answer | Yes. The recorded license is permissive, so closed-source commercial use is allowed if you keep the notices. Details | Yes. The recorded license is permissive, so closed-source commercial use is allowed if you keep the notices. Details |
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