VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data

By Jie Jessie Ren, Nathan A. Ahlgren, Yang Young Lu, Jed A. Fuhrman, Fengzhu Sun. Microbiome, 2017

580 citations recorded by the source corpus.

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.

Subfields: Animal Science and Zoology, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1d44252fedf570882a3daf558a91cd3ce9c7a0a4

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Citations in the corpus

CheckV assesses the quality and completeness of metagenome-assembled viral genomes (2020, methodology): “A contig was classified as viral if the number of viral genes exceeded that of microbial and plasmid genes ( n = 99,345), or VirFinder reported a P 0.01 with no plasmid genes and no more than one identified microbial gene ( n = 36,084).”
INfrastructure for a PHAge REference Database: Identification of Large-Scale Biases in the Current Collection of Cultured Phage Genomes (2021, unknown): “…of phage genomes available results in common analyses, including (1) comparative genomic analyses, 18,19 (2) taxonomic classiﬁcation, 20–23 (3) software for prediction of novel phages, 24–29 and (4) often the ﬁrst step in analysis of viromes is the comparison of sequences with a known database.”
VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences (2020, methodology): “VirFinder was shown to greatly improve the ability to recover viruses compared to Vir-Sorter, but it also demonstrated substantial host and source environment biases in predicting diverse viruses, likely due to reference database-associated biases while training the machine learning model [41].”
Marine DNA viral macro- and micro-diversity from pole to pole (2019, background): “Following assembly, contigs R 1.5kb were piped through VirSorter (Roux et al., 2015) and VirFinder (Ren et al., 2017) and those that mapped to the human, cat or dog genomes were removed.”
IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses (2020, background): “1 (32) and custom marker genes (27) (hereafter ‘CheckV DTR’).”
Identifying viruses from metagenomic data using deep learning (2020, methodology): “Because of the our limited understanding of viral genomes, deep learning model can ﬁ t better and achieve higher accuracy than other pre-de ﬁ ned feature-based machine learning models, such as k -mer frequencies logistic regression [5], and gene-based random forest model [6,44].”