ViralQC: a tool for assessing completeness and contamination of predicted viral contigs

MOTIVATION: Viruses represent the most abundant biological entities on Earth, playing 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. However, distinguishing viral sequences from the vast background of microbial organisms in metagenomic data remains a significant challenge. Existing tools experience varying degrees of false positive rates due to noise in sequencing and assembly, and the integration of proviruses into microbial 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 viral contigs or bins. ViralQC identifies microbial contamination within putative viral sequences using an ensemble framework powered by DNA and protein foundation models and estimates completeness by analyzing protein organization. We evaluated ViralQC on multiple datasets and compared its performance against the state-of-the-art tool, CheckV. Leveraging both DNA and protein foundation models, ViralQC achieves higher sensitivity on contamination detection for contigs longer than 10 kbp while maintaining comparable accuracy. Additionally, ViralQC delivers more accurate estimation on contigs with completeness > 50%. AVAILABILITY: The source code of ViralQC is available via: https://github.com/ChengPENG-wolf/ViralQC.