HVSeeker: a deep-learning-based method for identification of host and viral DNA sequences
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.
Authors
- Omer S. Alkhnbashi (ORCID: https://orcid.org/0000-0001-8088-590X)
- Sven Hauns (ORCID: https://orcid.org/0000-0002-5492-1871)
- Abdullatif Alnajim (ORCID: https://orcid.org/0009-0008-2180-6552)
- Rolf Backofen (ORCID: https://orcid.org/0000-0001-8231-3323)
- Tran Van Dinh (ORCID: https://orcid.org/0000-0002-7357-4959)
Institutions
- King Fahd University of Petroleum and Minerals (SA)
- University of Freiburg (DE)
- Kohler (New Zealand) (NZ)
- Mohammed Bin Rashid University of Medicine and Health Sciences (AE)
Publication Details
- Journal
- GigaScience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/gigascience/giaf037
- Citations
- 1
- Primary Topic
- Bacteriophages and microbial interactions
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- Deutsche Forschungsgemeinschaft
- Albert-Ludwigs-Universität Freiburg