Abstract
Background: As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. Results: We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0–97%) and false positive rates (0–30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Conclusions: Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.
Original language | English |
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Article number | 97 |
Number of pages | 23 |
Journal | Genome Biology |
Volume | 25 |
Issue number | 1 |
DOIs | |
Publication status | Published - 15 Apr 2024 |
Bibliographical note
Publisher Copyright:© The Author(s) 2024.
Funding
Open Access funding enabled and organized by Projekt DEAL. L.W. is funded by the Utrecht University One Health Initiative. Y.W. is funded by the European Union\u2019s Horizon 2020 research and innovation program, under the Marie Sk\u0142odowska-Curie Actions Innovative Training Networks grant agreement no. 955974 (VIROINF). N.P. is funded by the European Research Council (ERC) Consolidator grant 865694. G.P. and C.P.D.B. are funded by the Dutch Research Council NWO (grant ALWPP.2016.019). B.E.D. is funded by the European Research Council (ERC) Consolidator grant 865694: DiversiPHI, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u2019s Excellence Strategy\u2014EXC 2051\u2014Project-ID 390713860, and the Alexander von Humboldt Foundation in the context of an Alexander von Humboldt-Professorship founded by German Federal Ministry of Education and Research.
Funders | Funder number |
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Bundesministerium für Bildung und Forschung | |
Horizon 2020 Framework Programme | |
Alexander von Humboldt-Stiftung | |
Marie Skłodowska-Curie Actions Innovative Training Networks | 955974 |
European Research Council | 865694 |
Dutch Research Council NWO | ALWPP.2016.019 |
Deutsche Forschungsgemeinschaft | EXC 2051—Project-ID 390713860 |