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1. Virseqimprover: An Integrated Pipeline for Viral Contig Error Correction, Extension, and Annotation

   https://doi.org/10.21203/rs.3.rs-3318217/v1  

   Song, Haoqiu; Tithi, Saima; Aylward, Frank; Jensen, Roderick; Zhang, Liqing (September 2023, Research Square)

   Abstract Background:Despite the recent surge of viral metagenomic studies, it remains a significant challenge to recover complete virus genomes from metagenomic data. The majority of viral contigs generated fromde novoassembly programs are highly fragmented, presenting significant challenges to downstream analysis and inference.Methods:We have developed Virseqimprover, a computational pipeline that can extend assembled contigs to complete or nearly complete genomes while maintaining extension quality. Virseqimprover first examines whether there is any chimeric sequence based on read coverage, breaks the sequence into segments if there is, then extends the longest segment with uniform coverage, and repeats these procedures until the sequence cannot be extended. Finally, Virseqimprover annotates the gene content of the resulting sequence.Conclusion:Virseqimprover has good performance on correcting and extending viral contigs to their full lengths, hence can be a useful tool to improve the completeness and minimize the assembly errors of viral contigs. Both a web server and a conda package for Virseqimprover are provided to the research community free of charge. 

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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. Phage–bacterial contig association prediction with a convolutional neural network

   https://doi.org/10.1093/bioinformatics/btac239  

   Tang, Tianqi; Hou, Shengwei; Fuhrman, Jed A.; Sun, Fengzhu (June 2022, Bioinformatics)

   Abstract MotivationPhage–host associations play important roles in microbial communities. But in natural communities, as opposed to culture-based lab studies where phages are discovered and characterized metagenomically, their hosts are generally not known. Several programs have been developed for predicting which phage infects which host based on various sequence similarity measures or machine learning approaches. These are often based on whole viral and host genomes, but in metagenomics-based studies, we rarely have whole genomes but rather must rely on contigs that are sometimes as short as hundreds of bp long. Therefore, we need programs that predict hosts of phage contigs on the basis of these short contigs. Although most existing programs can be applied to metagenomic datasets for these predictions, their accuracies are generally low. Here, we develop ContigNet, a convolutional neural network-based model capable of predicting phage–host matches based on relatively short contigs, and compare it to previously published VirHostMatcher (VHM) and WIsH. ResultsOn the validation set, ContigNet achieves 72–85% area under the receiver operating characteristic curve (AUROC) scores, compared to the maximum of 68% by VHM or WIsH for contigs of lengths between 200 bps to 50 kbps. We also apply the model to the Metagenomic Gut Virus (MGV) catalogue, a dataset containing a wide range of draft genomes from metagenomic samples and achieve 60–70% AUROC scores compared to that of VHM and WIsH of 52%. Surprisingly, ContigNet can also be used to predict plasmid-host contig associations with high accuracy, indicating a similar genetic exchange between mobile genetic elements and their hosts. Availability and implementationThe source code of ContigNet and related datasets can be downloaded from https://github.com/tianqitang1/ContigNet. 

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4. OUP accepted manuscript

   https://doi.org/10.1093/bioinformatics/btaa490  

   Antipov, Rayko (May 2020, Bioinformatics)

   metaviralSPAdes: Assembly of Viruses From Metagenomic Data Abstract Motivation: Although the set of currently known viruses has been steadily expanding, only a tiny fraction of the Earth's virome has been sequenced so far. Shotgun metagenomic sequencing provides an opportunity to reveal novel viruses but faces the computational challenge of identifying viral genomes that are often difficult to detect in metagenomic assemblies. Results: We describe a metaviralSPAdes tool for identifying viral genomes in metagenomic assembly graphs that is based on analyzing variations in the coverage depth between viruses and bacterial chromosomes. We benchmarked metaviralSPAdes on diverse metagenomic datasets, verified our predictions using a set of virus-specific Hidden Markov Models, and demonstrated that it improves on the state-of-the-art viral identification pipelines. Availability: metaviralSPAdes includes viralAssembly, viralVerify, and viralComplete modules that are available as standalone packages: https://github.com/ablab/spades/tree/metaviral_publication, https://github.com/ablab/viralVerify/ and https://github.com/ablab/viralComplete/. Supplementary information: Supplementary data are available at Bioinformatics online. 

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5. Evaluating de Novo Assembly and Binning Strategies for Time Series Drinking Water Metagenomes

   https://doi.org/10.1128/Spectrum.01434-21  

   Vosloo, Solize; Huo, Linxuan; Anderson, Christopher L.; Dai, Zihan; Sevillano, Maria; Pinto, Ameet (December 2021, Microbiology Spectrum)

   Gralnick, Jeffrey A. (Ed.)

   ABSTRACT Reconstructing microbial genomes from metagenomic short-read data can be challenging due to the unknown and uneven complexity of microbial communities. This complexity encompasses highly diverse populations, which often includes strain variants. Reconstructing high-quality genomes is a crucial part of the metagenomic workflow, as subsequent ecological and metabolic inferences depend on their accuracy, quality, and completeness. In contrast to microbial communities in other ecosystems, there has been no systematic assessment of genome-centric metagenomic workflows for drinking water microbiomes. In this study, we assessed the performance of a combination of assembly and binning strategies for time series drinking water metagenomes that were collected over 6 months. The goal of this study was to identify the combination of assembly and binning approaches that result in high-quality and -quantity metagenome-assembled genomes (MAGs), representing most of the sequenced metagenome. Our findings suggest that the metaSPAdes coassembly strategies had the best performance, as they resulted in larger and less fragmented assemblies, with at least 85% of the sequence data mapping to contigs greater than 1 kbp. Furthermore, a combination of metaSPAdes coassembly strategies and MetaBAT2 produced the highest number of medium-quality MAGs while capturing at least 70% of the metagenomes based on read recruitment. Utilizing different assembly/binning approaches also assists in the reconstruction of unique MAGs from closely related species that would have otherwise collapsed into a single MAG using a single workflow. Overall, our study suggests that leveraging multiple binning approaches with different metaSPAdes coassembly strategies may be required to maximize the recovery of good-quality MAGs. IMPORTANCE Drinking water contains phylogenetic diverse groups of bacteria, archaea, and eukarya that affect the esthetic quality of water, water infrastructure, and public health. Taxonomic, metabolic, and ecological inferences of the drinking water microbiome depend on the accuracy, quality, and completeness of genomes that are reconstructed through the application of genome-resolved metagenomics. Using time series metagenomic data, we present reproducible genome-centric metagenomic workflows that result in high-quality and -quantity genomes, which more accurately signifies the sequenced drinking water microbiome. These genome-centric metagenomic workflows will allow for improved taxonomic and functional potential analysis that offers enhanced insights into the stability and dynamics of drinking water microbial communities. 

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