TY - JOUR
T1 - RaFAH
T2 - Host prediction for viruses of Bacteria and Archaea based on protein content
AU - Coutinho, Felipe Hernandes
AU - Zaragoza-Solas, Asier
AU - López-Pérez, Mario
AU - Barylski, Jakub
AU - Zielezinski, Andrzej
AU - Dutilh, Bas E.
AU - Edwards, Robert
AU - Rodriguez-Valera, Francisco
PY - 2021/7/9
Y1 - 2021/7/9
N2 - Culture-independent approaches have recently shed light on the genomic diversity of viruses of prokaryotes. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), that uses scores to 43,644 protein clusters to assign hosts to complete or fragmented genomes of viruses of Archaea and Bacteria. RaFAH displayed performance comparable with that of other methods for virus-host prediction in three different benchmarks encompassing viruses from RefSeq, single amplified genomes, and metagenomes. RaFAH was applied to assembled metagenomic datasets of uncultured viruses from eight different biomes of medical, biotechnological, and environmental relevance. Our analyses led to the identification of 537 sequences of archaeal viruses representing unknown lineages, whose genomes encode novel auxiliary metabolic genes, shedding light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.
AB - Culture-independent approaches have recently shed light on the genomic diversity of viruses of prokaryotes. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), that uses scores to 43,644 protein clusters to assign hosts to complete or fragmented genomes of viruses of Archaea and Bacteria. RaFAH displayed performance comparable with that of other methods for virus-host prediction in three different benchmarks encompassing viruses from RefSeq, single amplified genomes, and metagenomes. RaFAH was applied to assembled metagenomic datasets of uncultured viruses from eight different biomes of medical, biotechnological, and environmental relevance. Our analyses led to the identification of 537 sequences of archaeal viruses representing unknown lineages, whose genomes encode novel auxiliary metabolic genes, shedding light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.
KW - DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
KW - host prediction
KW - machine learning
KW - random forest
KW - viral diversity
KW - viral ecology
KW - virome
KW - virus
KW - virus-host associations
UR - http://www.scopus.com/inward/record.url?scp=85109448490&partnerID=8YFLogxK
U2 - 10.1016/j.patter.2021.100274
DO - 10.1016/j.patter.2021.100274
M3 - Article
AN - SCOPUS:85109448490
VL - 2
JO - Patterns
JF - Patterns
SN - 2666-3899
IS - 7
M1 - 100274
ER -