ArticleFrontiers in microbiology2019
The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes.
Article in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Viral Metagenomic Analysis of Bat Fly Pupae.Pathogens (Basel, Switzerland) · 2026Article
- Meta-virus resource (MetaVR): expanding the frontiers of viral diversity with 24 million uncultivated virus genomes.Nucleic acids research · 2026Article
- Gut Virome of Tibetan Pigs Reveals the Diversity, Composition, and Distribution of Potential Novel Viruses/Variants.Transboundary and emerging diseases · 2025Article
- Disentangling cobionts and contamination in long-read genomic data using sequence composition.G3 (Bethesda, Md.) · 2024Article
- Benchmarking informatics approaches for virus discovery: caution is needed when combiningmSystems · 2024Article
- Hecatomb: an integrated software platform for viral metagenomics.GigaScience · 2024Article
- MArVD2: a machine learning enhanced tool to discriminate between archaeal and bacterial viruses in viral datasets.ISME communications · 2023Article
- Functional biology and biotechnology of thermophilic viruses.Essays in biochemistry · 2023Review
- Gauge your phage: benchmarking of bacteriophage identification tools in metagenomic sequencing data.Microbiome · 2023Article
- IMG/VR v4: an expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata.Nucleic acids research · 2023Article
- Evaluation of computational phage detection tools for metagenomic datasets.Frontiers in microbiology · 2023Article
- Computational Tools for the Analysis of Uncultivated Phage Genomes.Microbiology and molecular biology reviews : MMBR · 2022Review
- Virus genomics: what is being overlooked?Current opinion in virology · 2022Review
- Cyanolichen microbiome contains novel viruses that encode genes to promote microbial metabolism.ISME communications · 2021Article
- Simulation study and comparative evaluation of viral contiguous sequence identification tools.BMC bioinformatics · 2021Article
- VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses.Microbiome · 2021Article
- IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses.Nucleic acids research · 2021Article
- Reads Binning Improves the Assembly of Viral Genome Sequences From Metagenomic Samples.Frontiers in microbiology · 2021Article
- Article
- Development of an NGS-Based Workflow for Improved Monitoring of Circulating Plasmids in Support of Risk Assessment of Antimicrobial Resistance Gene Dissemination.Antibiotics (Basel, Switzerland) · 2020Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.
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