TransGINmer: Identifying viral sequences from metagenomes with self-attention and Graph Isomorphism Network

Abstract

Viruses, abundant across diverse environments, play pivotal roles in microbial ecosystems and impact human health. Traditional virus studies are limited by their reliance on culture cultivation, which has been mitigated by metagenomics. It obtains nucleotide sequences of all microorganisms from the environment samples through the next-generation sequencing technology. This advancement prompts the need for efficient viral identification methods. To identify viruses accurately and quickly, We propose TransGINmer, a novel deep learning model to identify viral sequences directly from metagenomes. It encodes sequences by a k-mer frequency embedding model, constructs graphs from significant codon token correlations, and classifies them using graph isomorphism neural networks. In comparative tests against some SOTA methods DeepVirFinder, VirSorter2 and PhaMer on the testing dataset, the Amazon River dataset, the Sharon dataset and the CAMI Strain dataset, TransGINmer demonstrates superior accuracy, sensitivity, specificity, and AUC values, showcasing its potential as a robust tool for viral identification from metagenomes. TransGINmer is freely available at Github (https://github.com/xizhilangcc/TransGINmer).