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HVSeeker: a deep-learning-based method for identification of host and viral DNA sequences

  • Abdullatif Al-Najim
  • , Sven Hauns
  • , Van Dinh Tran
  • , Rolf Backofen
  • , Omer S. Alkhnbashi*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Bacteriophages 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. Results: We 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. Conclusions: These 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.

Original languageEnglish
Article numbergiaf037
JournalGigaScience
Volume14
DOIs
StatePublished - 2025

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

Keywords

  • bacteria
  • deep learning
  • genomics
  • metagenomic
  • phages

ASJC Scopus subject areas

  • General Medicine

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