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1. ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data

   https://doi.org/10.1038/s41467-023-35945-y  

   Du, Yuxuan; Fuhrman, Jed A.; Sun, Fengzhu (January 2023, Nature Communications)

   Abstract The introduction of high-throughput chromosome conformation capture (Hi-C) into metagenomics enables reconstructing high-quality metagenome-assembled genomes (MAGs) from microbial communities. Despite recent advances in recovering eukaryotic, bacterial, and archaeal genomes using Hi-C contact maps, few of Hi-C-based methods are designed to retrieve viral genomes. Here we introduce ViralCC, a publicly available tool to recover complete viral genomes and detect virus-host pairs using Hi-C data. Compared to other Hi-C-based methods, ViralCC leverages the virus-host proximity structure as a complementary information source for the Hi-C interactions. Using mock and real metagenomic Hi-C datasets from several different microbial ecosystems, including the human gut, cow fecal, and wastewater, we demonstrate that ViralCC outperforms existing Hi-C-based binning methods as well as state-of-the-art tools specifically dedicated to metagenomic viral binning. ViralCC can also reveal the taxonomic structure of viruses and virus-host pairs in microbial communities. When applied to a real wastewater metagenomic Hi-C dataset, ViralCC constructs a phage-host network, which is further validated using CRISPR spacer analyses. ViralCC is an open-source pipeline available athttps://github.com/dyxstat/ViralCC. 

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2. virMine: automated detection of viral sequences from complex metagenomic samples

   https://doi.org/10.7717/peerj.6695  

   Garretto, Andrea; Hatzopoulos, Thomas; Putonti, Catherine (January 2019, PeerJ)

   Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments. 

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3. ViralRecall—A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in ‘Omic Data

   https://doi.org/10.3390/v13020150  

   Aylward, Frank O.; Moniruzzaman, Mohammad (February 2021, Viruses)

   null (Ed.)

   Giant viruses are widespread in the biosphere and play important roles in biogeochemical cycling and host genome evolution. Also known as nucleo-cytoplasmic large DNA viruses (NCLDVs), these eukaryotic viruses harbor the largest and most complex viral genomes known. Studies have shown that NCLDVs are frequently abundant in metagenomic datasets, and that sequences derived from these viruses can also be found endogenized in diverse eukaryotic genomes. The accurate detection of sequences derived from NCLDVs is therefore of great importance, but this task is challenging owing to both the high level of sequence divergence between NCLDV families and the extraordinarily high diversity of genes encoded in their genomes, including some encoding for metabolic or translation-related functions that are typically found only in cellular lineages. Here, we present ViralRecall, a bioinformatic tool for the identification of NCLDV signatures in ‘omic data. This tool leverages a library of giant virus orthologous groups (GVOGs) to identify sequences that bear signatures of NCLDVs. We demonstrate that this tool can effectively identify NCLDV sequences with high sensitivity and specificity. Moreover, we show that it can be useful both for removing contaminating sequences in metagenome-assembled viral genomes as well as the identification of eukaryotic genomic loci that derived from NCLDV. ViralRecall is written in Python 3.5 and is freely available on GitHub: https://github.com/faylward/viralrecall. 

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4. Accurate viral genome reconstruction and host assignment with proximity-ligation sequencing

   https://doi.org/10.1101/2021.06.14.448389  

   Uritskiy, G. (June 2021, bioRxiv)

   Viruses play crucial roles in the ecology of microbial communities, yet they remain relatively understudied in their native environments. Despite many advancements in high-throughput whole-genome sequencing (WGS), sequence assembly, and annotation of viruses, the reconstruction of full-length viral genomes directly from metagenomic sequencing is possible only for the most abundant phages and requires long-read sequencing technologies. Additionally, the prediction of their cellular hosts remains difficult from conventional metagenomic sequencing alone. To address these gaps in the field and to accelerate the study of viruses directly in their native microbiomes, we developed an end-to-end bioinformatics platform for viral genome reconstruction and host attribution from metagenomic data using proximity-ligation sequencing (i.e., Hi-C). We demonstrate the capabilities of the platform by recovering and characterizing the metavirome of a variety of metagenomes, including a fecal microbiome that has also been sequenced with accurate long reads, allowing for the assessment and benchmarking of the new methods. The platform can accurately extract numerous near-complete viral genomes even from highly fragmented short-read assemblies and can reliably predict their cellular hosts with minimal false positives. To our knowledge, this is the first software for performing these tasks. Being significantly cheaper than long-read sequencing of comparable depth, the incorporation of proximity-ligation sequencing in microbiome research shows promise to greatly accelerate future advancements in the field. 

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5. efam: an e xpanded, metaproteome-supported HMM profile database of viral protein fam ilies

   https://doi.org/10.1093/bioinformatics/btab451  

   Zayed, Ahmed A; Lücking, Dominik; Mohssen, Mohamed; Cronin, Dylan; Bolduc, Ben; Gregory, Ann C; Hargreaves, Katherine R; Piehowski, Paul D; White, Richard A; Huang, Eric L; et al (June 2021, Bioinformatics)

   Robinson, Peter (Ed.)

   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. 

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