A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments.

ABSTRACT: 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 enable the recovery of genomes from previously unsampled clades. However, they do not leverage the knowledge in existing databases. Here, we introduce SemiBin, an open source tool that uses deep siamese neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of reconstructing high-quality bins that are outside the reference dataset. Using simulated and real microbiome datasets from several different habitats from GMGCv1 (Global Microbial G

SUBMITTER: Pan S 

PROVIDER: S-EPMC9051138 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature