Dataset Information

A binning tool to reconstruct viral haplotypes from assembled contigs.

ABSTRACT: BACKGROUND:Infections by RNA viruses such as Influenza, HIV still pose a serious threat to human health despite extensive research on viral diseases. One challenge for producing effective prevention and treatment strategies is high intra-species genetic diversity. As different strains may have different biological properties, characterizing the genetic diversity is thus important to vaccine and drug design. Next-generation sequencing technology enables comprehensive characterization of both known and novel strains and has been widely adopted for sequencing viral populations. However, genome-scale reconstruction of haplotypes is still a challenging problem. In particular, haplotype assembly programs often produce contigs rather than full genomes. As a mutation in one gene can mask the phenotypic effects of a mutation at another locus, clustering these contigs into genome-scale haplotypes is still needed. RESULTS:We developed a contig binning tool, VirBin, which clusters contigs into different groups so that each group represents a haplotype. Commonly used features based on sequence composition and contig coverage cannot effectively distinguish viral haplotypes because of their high sequence similarity and heterogeneous sequencing coverage for RNA viruses. VirBin applied prototype-based clustering to cluster regions that are more likely to contain mutations specific to a haplotype. The tool was tested on multiple simulated sequencing data with different haplotype abundance distributions and contig sizes, and also on mock quasispecies sequencing data. The benchmark results with other contig binning tools demonstrated the superior sensitivity and precision of VirBin in contig binning for viral haplotype reconstruction. CONCLUSIONS:In this work, we presented VirBin, a new contig binning tool for distinguishing contigs from different viral haplotypes with high sequence similarity. It competes favorably with other tools on viral contig binning. The source codes are available at: https://github.com/chjiao/VirBin .

SUBMITTER: Chen J

PROVIDER: S-EPMC6829986 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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A binning tool to reconstruct viral haplotypes from assembled contigs.

Chen Jiao J Shang Jiayu J Wang Jianrong J Sun Yanni Y

BMC bioinformatics 20191104 1

<h4>Background</h4>Infections by RNA viruses such as Influenza, HIV still pose a serious threat to human health despite extensive research on viral diseases. One challenge for producing effective prevention and treatment strategies is high intra-species genetic diversity. As different strains may have different biological properties, characterizing the genetic diversity is thus important to vaccine and drug design. Next-generation sequencing technology enables comprehensive characterization of b ...[more]

PMID: 31684876

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Project description:Identifying viral sequences in mixed metagenomes containing both viral and host contigs is a critical first step in analyzing the viral component of samples. Current tools for distinguishing prokaryotic virus and host contigs primarily use gene-based similarity approaches. Such approaches can significantly limit results especially for short contigs that have few predicted proteins or lack proteins with similarity to previously known viruses.We have developed VirFinder, the first k-mer frequency based, machine learning method for virus contig identification that entirely avoids gene-based similarity searches. VirFinder instead identifies viral sequences based on our empirical observation that viruses and hosts have discernibly different k-mer signatures. VirFinder's performance in correctly identifying viral sequences was tested by training its machine learning model on sequences from host and viral genomes sequenced before 1 January 2014 and evaluating on sequences obtained after 1 January 2014.VirFinder had significantly better rates of identifying true viral contigs (true positive rates (TPRs)) than VirSorter, the current state-of-the-art gene-based virus classification tool, when evaluated with either contigs subsampled from complete genomes or assembled from a simulated human gut metagenome. For example, for contigs subsampled from complete genomes, VirFinder had 78-, 2.4-, and 1.8-fold higher TPRs than VirSorter for 1, 3, and 5 kb contigs, respectively, at the same false positive rates as VirSorter (0, 0.003, and 0.006, respectively), thus VirFinder works considerably better for small contigs than VirSorter. VirFinder furthermore identified several recently sequenced virus genomes (after 1 January 2014) that VirSorter did not and that have no nucleotide similarity to previously sequenced viruses, demonstrating VirFinder's potential advantage in identifying novel viral sequences. Application of VirFinder to a set of human gut metagenomes from healthy and liver cirrhosis patients reveals higher viral diversity in healthy individuals than cirrhosis patients. We also identified contig bins containing crAssphage-like contigs with higher abundance in healthy patients and a putative Veillonella genus prophage associated with cirrhosis patients.This innovative k-mer based tool complements gene-based approaches and will significantly improve prokaryotic viral sequence identification, especially for metagenomic-based studies of viral ecology.

Project description:BackgroundSo far, a lot of binning approaches have been intensively developed for untangling metagenome-assembled genomes (MAGs) and evaluated by two main strategies. The strategy by comparison to known genomes prevails over the other strategy by using single-copy genes. However, there is still no dataset with all known genomes for a real (not simulated) bacterial consortium yet.ResultsHere, we continue investigating the real bacterial consortium F1RT enriched and sequenced by us previously, considering the high possibility to unearth all MAGs, due to its low complexity. The improved F1RT metagenome reassembled by metaSPAdes here utilizes about 98.62% of reads, and a series of analyses for the remaining reads suggests that the possibility of containing other low-abundance organisms in F1RT is greatly low, demonstrating that almost all MAGs are successfully assembled. Then, 4 isolates are obtained and individually sequenced. Based on the 4 isolate genomes and the entire metagenome, an elaborate pipeline is then in-house developed to construct all F1RT MAGs. A series of assessments extensively prove the high reliability of the herein reconstruction. Next, our findings further show that this dataset harbors several properties challenging for binning and thus is suitable to compare advanced binning tools available now or benchmark novel binners. Using this dataset, 8 advanced binning algorithms are assessed, giving useful insights for developing novel approaches. In addition, compared with our previous study, two novel MAGs termed FC8 and FC9 are discovered here, and 7 MAGs are solidly unearthed for species without any available genomes.ConclusionTo our knowledge, it is the first time to construct a dataset with almost all known MAGs for a not simulated consortium. We hope that this dataset will be used as a routine toolkit to complement mock datasets for evaluating binning methods to further facilitate binning and metagenomic studies in the future.

Dataset Information

A binning tool to reconstruct viral haplotypes from assembled contigs.

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A binning tool to reconstruct viral haplotypes from assembled contigs.

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