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bioRxiv · 10.1101/2022.02.12.479459

Improved Metagenomic Binning with Transformers

Shenker-Tauris, N.·Gehrig, J.
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Abstract

AO_SCPLOWBSTRACTC_SCPLOWTraditional metagenome binning methods cluster contiguous DNA sequences (contigs) based on uncontextualized features of the sequences which ignores both the semantic relationship between genes and the positional embedding of k-mers. This paper presents a novel binning method that addresses these concerns. Firstly, taken from natural language processing literature, a sequence representation model - Bidirectional Encoder Representations from Transformers (BERT) - is utilized to generate semantic and positional contig embeddings. Secondly, two workflows are presented; one which applies a hierarchical density-based clustering algorithm to find metagenomic bins and the other which incorporates contig embedding into a state-of-the-art binner. Experimental results on a publicly available metagenomic dataset show superior clustering for shorter contigs compared to traditionally used tetranucleotide frequency (TNF), reconstruction of up to 17% more high-precision genomes, and improved semantic understanding of contigs.

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Shenker-Tauris, N., Gehrig, J.. 2022-02-13. Improved Metagenomic Binning with Transformers. https://doi.org/10.1101/2022.02.12.479459

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