ArticleGigaScience2025
HVSeeker: a deep-learning-based method for identification of host and viral DNA sequences.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- Bioinformatics Tools and Approaches for Virus Discovery in Genomic Data: A Systematic Review.Viruses · 2025Pooled it
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- HVSeeker: a deep-learning-based method for identification of host and viral DNA sequences.GigaScience · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundBacteriophages are among the most abundant organisms on Earth, significantly impacting ecosystems and human society. The identification of viral sequences, especially novel ones, from mixed metagenomes is a critical first step in analyzing the viral components of host samples. This plays a key role in many downstream tasks. However, this is a challenging task due to their rapid evolution rate. The identification process typically involves two steps: distinguishing viral sequences from the host and identifying if they come from novel viral genomes. Traditional metagenomic techniques that rely on sequence similarity with known entities often fall short, especially when dealing with short or novel genomes. Meanwhile, deep learning has demonstrated its efficacy across various domains, including the bioinformatics field.
resultsWe have developed HVSeeker-a host/virus seeker method-based on deep learning to distinguish between bacterial and phage sequences. HVSeeker consists of two separate models: one analyzing DNA sequences and the other focusing on proteins. In addition to the robust architecture of HVSeeker, three distinct preprocessing methods were introduced to enhance the learning process: padding, contigs assembly, and sliding window. This method has shown promising results on sequences with various lengths, ranging from 200 to 1,500 base pairs. Tested on both NCBI and IMGVR databases, HVSeeker outperformed several methods from the literature such as Seeker, Rnn-VirSeeker, DeepVirFinder, and PPR-Meta. Moreover, when compared with other methods on benchmark datasets, HVSeeker has shown better performance, establishing its effectiveness in identifying unknown phage genomes.
conclusionsThese results demonstrate the exceptional structure of HVSeeker, which encompasses both the preprocessing methods and the model design. The advancements provided by HVSeeker are significant for identifying viral genomes and developing new therapeutic approaches, such as phage therapy. Therefore, HVSeeker serves as an essential tool in prokaryotic and phage taxonomy, offering a crucial first step toward analyzing the host-viral component of samples by identifying the host and viral sequences in mixed metagenomes.
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