efam: an expanded, metaproteome-supported HMM profile database of viral protein families

Abstract Motivation Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is cha...

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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 37; H. 22; S. 4202 - 4208
Hauptverfasser: Zayed, Ahmed A, Lücking, Dominik, Mohssen, Mohamed, Cronin, Dylan, Bolduc, Ben, Gregory, Ann C, Hargreaves, Katherine R, Piehowski, Paul D, White III, Richard A, Huang, Eric L, Adkins, Joshua N, Roux, Simon, Moraru, Cristina, Sullivan, Matthew B
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 15.11.2021
Oxford Publishing Limited (England)
Schlagworte:
Animals
Annotations
Aquatic habitats
Availability
BASIC BIOLOGICAL SCIENCES
Bioinformatics
Datasets
Ecosystems
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Marine ecosystems
Marine resources
Markov chains
MATHEMATICS AND COMPUTING
Metagenomics
Metagenomics - methods
Microbiomes
Microbiota
Oceans
Original Papers
Plant diseases
Protein families
Protein folding
Proteins
Software
Viral Proteins
Virions
Viruses
ISSN:1367-4803, 1367-4811, 1367-4811
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Zusammenfassung:Abstract Motivation Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. Results Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from ‘conservative’ to ‘eXtremely Conservative’ resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ∼24% on average (up to ∼42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem. Availability and implementation The resources are available on the iVirus platform at (doi.org/10.25739/9vze-4143). Supplementary information Supplementary data are available at Bioinformatics online.
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Gordon and Betty Moore Foundation
USDOE
SC0020173; AC02-05CH11231; AC05-76RL01830; 3790; OCE-1536989; OCE-1829831; ABI-1758974; #DE-SC0020173; AC05-76RL0 1830
USDOE Office of Science (SC)
National Science Foundation (NSF)
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btab451