SemiBin

SemiBin is a metagenomic binning tool (MAG builder). It is based on deep contrastive learning to incorporate background information (from reference genomes).

It achieves better results than other tools across a range of microbial habitats (both host-associated and environmental habitats).

SemiBin2 introduced self-supervised contrastive learning (removing the need for the more expensive semi-supervised step) and added support for binning long-read assemblies, making it both faster and more accurate than the original version.

Latest release

The current version is SemiBin 2.5.0 (released September 2026). This is a small release with a single user-visible change: utils.concatenate_fasta() gains a keep_full_header option, which preserves the full FASTA header (any text after the contig ID) on the renamed, sample-prefixed header instead of dropping it, useful if your headers carry information you want to keep around after concatenating samples for multi-sample binning (e.g., circularity flags from your assembler). See the ChangeLog for the full history.

SemiBin is available on bioconda and PyPI. We recommend installing it with pixi, which can even run it without installing:

pixi exec -c bioconda -c conda-forge -s semibin SemiBin2

Alternatively, with conda:

conda install -c conda-forge -c bioconda semibin

SemiBin links

• SemiBin documentation
• SemiBin Github repository
• SemiBin users mailing-list
• SemiBin1 manuscript: Shaojun Pan et al. A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments in Nature Communications 2022
• SemiBin2 manuscript: Shaojun Pan et al. SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing in Bioinformatics 2023