ViralQC: A Tool for Assessing Completeness and Contamination of Predicted Viral Contigs

Motivation: Viruses represent the most abundant biological entities on the planet and play vital roles in diverse ecosystems. Cataloging viruses across various environments is essential for understanding their properties and functions. Metagenomic sequencing has emerged as the most comprehensive method for virus discovery, enabling the sequencing of all genetic materials, including viruses, from host or environmental samples. However, distinguishing viral sequences from the vast background of cellular organism-derived reads in metagenomic data remains a significant challenge. While several learning-based tools, such as VirSorter2 and geNomad, have shown promise in identifying viral contigs, they often experience varying degrees of false positive rates due to noise in sequencing and assembly, shared genes between viruses and their hosts, and the formation of proviruses within host genomes. This highlights the urgent need for an accurate and efficient method to evaluate the quality of viral contigs. Results: To address these challenges, we introduce ViralQC, a tool designed to assess the quality of reported viral contigs or bins. ViralQC identifies contamination regions within putative viral sequences using foundation models trained on viral and cellular genomes and estimates viral completeness through protein organization alignment. We evaluate ViralQC on multiple datasets and compare its performance against CheckV, the state-of-the-art in virus quality assessment. Notably, ViralQC correctly identifies 38% more contamination than CheckV, while maintaining a median absolute error of only 3%. In addition, ViralQC delivers more accurate results for medium- to high-quality (>50% completeness) contigs, demonstrating its superior performance in completeness estimation.