Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes

Abstract

Metagenome-assembled genomes (MAGs) offer valuable insights into the exploration of microbial dark matter using metagenomic sequencing data. However, there is a growing concern that contamination in MAGs may significantly impact the downstream analysis results. Existing MAG decontamination methods heavily rely on marker genes but do not fully leverage genomic sequences. To address the limitations, we have introduced a novel decontamination approach named Deepurify, which utilizes a multi-modal deep language model employing contrastive learning to learn taxonomic similarities of genomic sequences. Deepurify utilizes inferred taxonomic lineages to guide the allocation of contigs into a MAG-separated tree and employs a tree traversal strategy for maximizing the total number of medium- and high-quality MAGs. Extensive experiments were conducted on two simulated datasets, CAMI I, and human gut metagenomic sequencing data. These results demonstrate that Deepurify significantly outperforms other decontamination methods.