2023
Wu, Ling-Yi; Pappas, Nikolaos; Wijesekara, Yasas; Piedade, Gonçalo J.; Brussaard, Corina P. D.; Dutilh, Bas E.
Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes Journal Article
In: bioRxiv, 2023.
Abstract | Links | BibTeX | Tags: Project 12, Project 13, WP 1.1 Virus identification, WP 1.2 Host prediction
@article{nokey,
title = {Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes},
author = {Ling-Yi Wu and Nikolaos Pappas and Yasas Wijesekara and Gonçalo J. Piedade and Corina P.D. Brussaard and Bas E. Dutilh},
doi = {10.1101/2023.04.26.538077},
year = {2023},
date = {2023-04-28},
journal = {bioRxiv},
abstract = {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/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten 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 had highly variable true positive rates (0 – 68%) and false positive rates (0 – 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.},
keywords = {Project 12, Project 13, WP 1.1 Virus identification, WP 1.2 Host prediction},
pubstate = {published},
tppubtype = {article}
}
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/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten 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 had highly variable true positive rates (0 – 68%) and false positive rates (0 – 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.
2021
Goettsch, Winfried; Beerenwinkel, Niko; Deng, Li; Dölken, Lars; Dutilh, Bas E.; Erhard, Florian; Kaderali, Lars; von Kleist, Max; Marquet, Roland; Matthijnssens, Jelle; McCallin, Shawna; McMahon, Dino; Rattei, Thomas; van Rij, Ronald P.; Robertson, David L.; Schwemmle, Martin; Stern-Ginossar, Noam; Marz, Manja
ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics Journal Article
In: Viruses, vol. 13, no. 5, pp. 766, 2021.
Abstract | Links | BibTeX | Tags: Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses
@article{nokey,
title = {ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics},
author = {Winfried Goettsch and Niko Beerenwinkel and Li Deng and Lars Dölken and Bas E. Dutilh and Florian Erhard and Lars Kaderali and Max von Kleist and Roland Marquet and Jelle Matthijnssens and Shawna McCallin and Dino McMahon and Thomas Rattei and Ronald P. {van Rij} and David L. Robertson and Martin Schwemmle and Noam Stern-Ginossar and Manja Marz},
doi = {10.3390/v13050766},
year = {2021},
date = {2021-04-27},
urldate = {2021-04-27},
journal = {Viruses},
volume = {13},
number = {5},
pages = {766},
abstract = {Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.},
keywords = {Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses},
pubstate = {published},
tppubtype = {article}
}
Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.