ArticleBioinformatics (Oxford, England)2024
ViraLM: empowering virus discovery through the genome foundation model.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bioinformatics Tools and Approaches for Virus Discovery in Genomic Data: A Systematic Review.Viruses · 2025Pooled it
- RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models.NAR genomics and bioinformatics · 2026Article
- ViralQC: a tool for assessing completeness and contamination of predicted viral contigs.Bioinformatics (Oxford, England) · 2026Article
- TPMM: three-component posterior mixture model enables robust inverton detection in low-depth metagenomes and suggests potential viral invertons.Bioinformatics (Oxford, England) · 2026Article
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Benchmarking genomic foundation models for binary classification of gene fusion breakpoints from DNA sequences.BioData mining · 2026Article
- SS-VIME: a single-source virome-microbiome extraction protocol toward comprehensive soil community analysis.Microbiology spectrum · 2026Article
- Influ-BERT: a domain-adaptive genomic language model for advancing influenza A virus research.Briefings in bioinformatics · 2026Article
- Article
- vir2vec: A Viral Genome-Wide Viral Embedding.bioRxiv : the preprint server for biology · 2025Article
- Protein Set Transformer: a protein-based genome language model to power high-diversity viromics.Nature communications · 2025Article
- Large language models for biological sequence analysis in infectious disease research.Biosafety and health · 2025Review
- Novel Adomaviruses Associated with Blotchy Bass Syndrome in Black Basses (bioRxiv : the preprint server for biology · 2025Article
- VirNucPro: an identifier for the identification of viral short sequences using six-frame translation and large language models.Briefings in bioinformatics · 2025Article
- Novel adomaviruses associated with blotchy bass syndrome in black basses (Micropterus spp.).PloS one · 2025Article
- Recent advances in deep learning and language models for studying the microbiome.Frontiers in genetics · 2024Review
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Authors and funding
5 authors.
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Abstract
motivationViruses, with their ubiquitous presence and high diversity, play pivotal roles in ecological systems and public health. Accurate identification of viruses in various ecosystems is essential for comprehending their variety and assessing their ecological influence. Metagenomic sequencing has become a major strategy to survey the viruses in various ecosystems. However, accurate and comprehensive virus detection in metagenomic data remains difficult. Limited reference sequences prevent alignment-based methods from identifying novel viruses. Machine learning-based tools are more promising in novel virus detection but often miss short viral contigs, which are abundant in typical metagenomic data. The inconsistency in virus search results produced by available tools further highlights the urgent need for a more robust tool for virus identification.
resultsIn this work, we develop ViraLM for identifying novel viral contigs in metagenomic data. By using the latest genome foundation model as the backbone and training on a rigorously constructed dataset, the model is able to distinguish viruses from other organisms based on the learned genomic characteristics. We thoroughly tested ViraLM on multiple datasets and the experimental results show that ViraLM outperforms available tools in different scenarios. In particular, ViraLM improves the F1-score on short contigs by 22%. AVAILABILITY AND IMPLEMENTATION: The source code of ViraLM is available via: https://github.com/ChengPENG-wolf/ViraLM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.