PT  - JOURNAL ARTICLE
AU  - Shaojun Pan
AU  - Chengkai Zhu
AU  - Xing-Ming Zhao
AU  - Luis Pedro Coelho
TI  - SemiBin: Incorporating information from reference genomes with semi-supervised deep learning leads to better metagenomic assembled genomes (MAGs)
AID  - 10.1101/2021.08.16.456517
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.08.16.456517
4099  - http://biorxiv.org/content/early/2021/08/16/2021.08.16.456517.short
4100  - http://biorxiv.org/content/early/2021/08/16/2021.08.16.456517.full
AB  - Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used methods for binning are reference-independent, operating de novo and allow the recovery of genomes from previously unsampled clades. However, they do not leverage the knowledge in existing databases. Here, we propose SemiBin, an open source tool that uses neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of binning genomes that are outside the reference dataset. SemiBin outperforms existing state-of-the-art binning methods in simulated and real microbiome datasets across three different environments (human gut, dog gut, and marine microbiomes). SemiBin returns more high-quality bins with larger taxonomic diversity, including more distinct genera and species. SemiBin is available as open source software at https://github.com/BigDataBiology/SemiBin/.Competing Interest StatementThe authors have declared no competing interest.