{Reference Type}: Journal Article
{Title}: VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes.
{Author}: Dong Y;Chen WH;Zhao XM;
{Journal}: Genome Biol
{Volume}: 25
{Issue}: 1
{Year}: 2024 Jul 4
暂无{DOI}: 10.1186/s13059-024-03320-9
{Abstract}: Identifying viruses from metagenomes is a common step to explore the virus composition in the human gut. Here, we introduce VirRep, a hybrid language representation learning framework, for identifying viruses from human gut metagenomes. VirRep combines a context-aware encoder and an evolution-aware encoder to improve sequence representation by incorporating k-mer patterns and sequence homologies. Benchmarking on both simulated and real datasets with varying viral proportions demonstrates that VirRep outperforms state-of-the-art methods. When applied to fecal metagenomes from a colorectal cancer cohort, VirRep identifies 39 high-quality viral species associated with the disease, many of which cannot be detected by existing methods.