ViWrap: A modular pipeline to identify, bin, classify, and predict viral–host relationships for viruses from metagenomes

Abstract

AbstractViruses are increasingly being recognized as important components of human and environmental microbiomes. However, viruses in microbiomes remain difficult to study because of the difficulty in culturing them and the lack of sufficient model systems. As a result, computational methods for identifying and analyzing uncultivated viral genomes from metagenomes have attracted significant attention. Such bioinformatics approaches facilitate the screening of viruses from enormous sequencing data sets originating from various environments. Although many tools and databases have been developed for advancing the study of viruses from metagenomes, there is a lack of integrated tools enabling a comprehensive workflow and analysis platform encompassing all the diverse segments of virus studies. Here, we developed ViWrap, a modular pipeline written in Python. ViWrap combines the power of multiple tools into a single platform to enable various steps of virus analyses, including identification, annotation, genome binning, species‐ and genus‐level clustering, assignment of taxonomy, prediction of hosts, characterization of genome quality, comprehensive summaries, and intuitive visualization of results. Overall, ViWrap enables a standardized and reproducible pipeline for both extensive and stringent characterization of viruses from metagenomes, viromes, and microbial genomes. Our approach has flexibility in using various options for diverse applications and scenarios, and its modular structure can be easily amended with additional functions as necessary. ViWrap is designed to be easily and widely used to study viruses in human and environmental systems. ViWrap is publicly available via GitHub (https://github.com/AnantharamanLab/ViWrap). A detailed description of the software, its usage, and interpretation of results can be found on the website.