VIRALpre: Genomic Foundation Model Embedding Fused withK-mer Feature for Virus Identification

Virus, a submicroscopic infectious agent, influences all life forms. Identifying viral sequences is essential to understand their biological functions and then analyze their impacts on public health, and the development of microbial communities. For its significance, tools are developed based on various mathematical methods and algorithms. However, previous methods struggle to identify viral sequences, especially short contigs accurately since the limited information and small-scale close-set dataset. Here we propose VIRALpre, a hybrid framework combined with genomic foundation model (GFM) embedding and K-mer feature of sequences to precisely recognize viral genomic fragments. VIRALpre is empowered by the generalization competencies of GFMs, which have proven their strength in various downstream tasks, thanks to newly established large-scale training databases and Attention mechanism. On the other hand, K-mer features provide additional biological information to bridge the limitation of GFMs in classification tasks. Comprehensive experimental results demonstrate that VIRALpre significantly outperforms all the previous methods on virus identification performance by 4% in accuracy. To prove that this model is qualified when facing unique contigs to training data, BLASTn-based similarity cut-off test(setting e-value as 10-5) is done and it achieves about 10% F1-score improvement. More than well-built test datasets, new zero-shot cross-dataset tests on benchmark datasets sampling from natural environments are conducted, VIRALpre performs identify almost most viral sequences while keeping a very low False Positive Rate. Based on these solid experiments, VIRALpre has the ability to manage short-contig virus identification by truly learning the distinctions of viral sequences and hopefully act as an adviser to promote virus-related research.