Evidence map›Paper›PMID 40372723›Full record

ArticleGigaScience2025

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

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdullatif Al-NajimInformation and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 34462, Saudi Arabia.ORCID 0009-0008-2180-6552
Sven HaunsBioinformatics group,Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 101, 79110 Freiburg, Germany.ORCID 0000-0002-5492-1871
Van Dinh TranInformation and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 34462, Saudi Arabia.ORCID 0000-0002-7357-4959
Rolf BackofenBioinformatics group,Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 101, 79110 Freiburg, Germany.ORCID 0000-0001-8231-3323
Omer S AlkhnbashiCenter for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences(MBRU), Dubai Healthcare City, 505055 Dubai, United Arab Emirates.ORCID 0000-0001-8088-590X

Funding

Deutsche Forschungsgemeinschaft 2168/23Deutsche Forschungsgemeinschaft 2168/25-1University of Freiburg
6 · The paper itself

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.

Indexed as

BacteriophagesComputational BiologyDeep LearningDNA, ViralMetagenomicsSequence Analysis, DNASoftwareGenome, ViralHumansMetagenomeDNA, Viralbacteriadeep learninggenomicsmetagenomicphages

Identifiers

PMID40372723
PMCPMC12080225

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Registered trials

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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.