First community challenge for automated virus taxonomy
Lood, C.; Doijad, S.; Adriaenssens, E.; Bao, Y.; Barylski, J.; Bolduc, B.; Bouras, G.; Brister, R. J.; Brown, T. C.; Camargo, A. P.; De Coninck, L.; Deorowicz, S.; Edgar, R.; Edwards, R.; Gong, S.; Gruber, A.; Gudys, A.; Hauptfeld, E.; ter Horst, A.; Huang, T.; Jiang, J.; Kaderali, L.; Kim, J.; Krupovic, M.; Kuhn, J. H.; Lefkowitz, E.; Leobold, M.; Li, S.-C.; Liu, Y.; von Meijenfeldt, B. F. A.; Neri, U.; Penzes, J.; Pierce-Ward, T.; Rahlff, J.; Reyes Munoz, A.; Rubino, L.; Sabanodzovic, S.; Shang, J.; Simmonds, P.; Steinegger, M.; Sullivan, M.; Sun, Y.; Tian, L.; Tong, Y.; Turnbull, R.; Turner
Show abstract
The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.
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