Informative Regions In Viral Genomes

Abstract

Viruses, far from being just parasites affecting hosts’ fitness, are major players in any microbial ecosystem. In spite of their broad abundance, viruses, in particular bacteriophages, remain largely unknown since only about 20% of sequences obtained from viral community DNA surveys could be annotated by comparison with public databases. In order to shed some light into this genetic dark matter we expanded the search of orthologous groups as potential markers to viral taxonomy from bacteriophages and included eukaryotic viruses, establishing a set of 31,150 ViPhOGs (Eukaryotic Viruses and Phages Orthologous Groups). To do this, we examine the non-redundant viral diversity stored in public databases, predict proteins in genomes lacking such information, and used all annotated and predicted proteins to identify potential protein domains. The clustering of domains and unannotated regions into orthologous groups was done using cogSoft. Finally, we employed a random forest implementation to classify genomes into their taxonomy and found that the presence or absence of ViPhOGs is significantly associated with their taxonomy. Furthermore, we established a set of 1457 ViPhOGs that given their importance for the classification could be considered as markers or signatures for the different taxonomic groups defined by the ICTV at the order, family, and genus levels.

Biological foundations of successful bacteriophage therapy

“…Modification of phages to enhance their therapeutic potential (Pires et al , 2016 ; Brown et al , 2017 ; Chen et al, 2019 ; Kilcher & Loessner, 2019 ; Monteiro et al , 2019 ) is complicated by the presence of large proportions of uncharacterized genetic material (“dark matter”) in phage genomes, which must be experimentally addressed (Hatfull & Hendrix, 2011 ; Wittebole et al , 2014 ; Hatfull, 2015 ; Philipson et al , 2018 ; Moreno‐Gallego & Reyes, 2021 ).…”

VIRify: an integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models

“…The ViPhOG database consists of a collection of 31,150 protein profile HMMs that were built using sequences retrieved from all viral genomes available in April 2015 in NCBI’s RefSeq (Moreno-Gallego and Reyes 2021). We manually curated the taxon-specific profile HMMs by searching the complete ViPhOG database against all entries in UniProtKB (February 2019 version).…”

“…The analysis was performed on contigs that were at least 1.5 kb long to filter out shorter contigs with potentially few ORFs, which are less likely to be correctly classified by our ViPhOG-based taxonomic annotation pipeline. Previous studies have revealed that viral genomes shorter than 1.5 kb tend to code for no more than 5 proteins (Moreno-Gallego and Reyes 2021). Based on a previous study in which global oceanic viral populations were surveyed, we manually updated the parameter configuration file of WtP to include all VirSorter predictions from categories 1-5 and to filter VirFinder results by p-values < 0.05 and scores ≥ 0.7 (Gregory et al 2019).…”

“…Phage classification with ClassiPhage demonstrated agreement with the reference ICTV taxonomy, although the generation and testing of the taxon-specific profile HMMs was solely focused on phages targeting members of the bacterial family Vibrionaceae (Chibani et al 2019). More recently, a set of 31,150 profile HMMs representing proteins predicted in viral genomes from NCBI were used as the basis for a set of Random Forest classifiers aimed at assigning viral taxonomy at the order, family and genus ranks (Moreno-Gallego and Reyes 2021). These classifiers demonstrated very high classification accuracy (≥ 89%) at all the aforementioned ranks, suggesting that these profile HMMs (or ViPhOGs as they were named) could be the foundation for a new method that taxonomically classifies viral genomic sequences present in metagenomic datasets.…”

Perspective on taxonomic classification of uncultivated viruses

“…For example, for uncultivated single-stranded DNA (ssDNA) viruses from the Genomoviridae and Smacoviridae families, 76-77% genome-wide pairwise identity of member viruses was chosen as a species demarcation threshold [56,57]. The reasons for this variability are rooted in the legitimate differences in the evolutionary rates, genome architectures, and replication strategies of viruses across different taxa, as well as variability in the taxonomically informative regions in viral genomes [58].…”