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 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 The resources are available on the iVirus platform at (doi.org/10.25739/9vze-4143). Supplementary information Supplementary data are available at Bioinformatics online.

NeoRdRp: A comprehensive dataset for identifying RNA-dependent RNA polymerase of various RNA viruses from metatranscriptomic data

“…Since amino acid sequences of RdRp varied greatly among all RNA viruses, various bioinformatics attempts have been made to find RdRp genes from metatranscriptome data (Wolf et al, 2018;Bigot et al, 2019;Babaian and Edgar, 2021;Zayed et al, 2021). In this study, RdRp genes were comprehensively obtained (named NeoRdRp-seq), clustered based on amino acid similarity, and HMM profiles were constructed (named NeoRdRp-HMM) (Fig.…”

New Microviridae isolated from Sulfitobacter reveals two cosmopolitan subfamilies of ssDNA phages infecting marine and terrestrial Alphaproteobacteria

“…The prokaryotic Viruses Orthologous Groups (pVOGs) database (Grazziotin et al 2017), the Virus Orthologous Group (VOGDB) database (Kiening et al 2019), and the Prokaryotic virus Remote Homologous Groups (PHROGS) database were searched using hhsearch (Steinegger et al 2019). Finally, the efam database (Zayed et al 2021) was searched using hmmscan (Eddy 2011). At the end, the annotations of each protein were manually curated and compared with the annotations of the other proteins in the same protein cluster.…”

The Use of Bacteriophages in Biotechnology and Recent Insights into Proteomics

“…This is facilitated by the recent creation of free servers, such as PVPredn, that can identify phage virion proteins from nucleotide sequences [38]; further assistance is provided by databases, such as IMG/VR v3 (created in 2016) that, although not specific for bacteriophages, represents the largest collection of viral sequences so far compiled [39]. Additional, more specialized, bioinformatic tools include efam, established by Zayed et al in 2021, and described by the authors as "an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset" [40].…”