ArticleFrontiers in microbiology2023
DETIRE: a hybrid deep learning model for identifying viral sequences from metagenomes.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 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
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- DeepTaxa: a hybrid CNN-BERT framework for 16S rRNA taxonomic classification.Bioinformatics advances · 2026Article
- PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences.BMC bioinformatics · 2025Article
- VirNucPro: an identifier for the identification of viral short sequences using six-frame translation and large language models.Briefings in bioinformatics · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Review
- DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.Briefings in bioinformatics · 2024Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- VirGrapher: a graph-based viral identifier for long sequences from metagenomes.Briefings in bioinformatics · 2024Article
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Unlocking the Viral Universe: Metagenomic Analysis of Bat Samples Using Next-Generation Sequencing.Microorganisms · 2023Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
A metagenome contains all DNA sequences from an environmental sample, including viruses, bacteria, archaea, and eukaryotes. Since viruses are of huge abundance and have caused vast mortality and morbidity to human society in history as a type of major pathogens, detecting viruses from metagenomes plays a crucial role in analyzing the viral component of samples and is the very first step for clinical diagnosis. However, detecting viral fragments directly from the metagenomes is still a tough issue because of the existence of a huge number of short sequences. In this study a hybrid Deep lEarning model for idenTifying vIral sequences fRom mEtagenomes (DETIRE) is proposed to solve the problem. First, the graph-based nucleotide sequence embedding strategy is utilized to enrich the expression of DNA sequences by training an embedding matrix. Then, the spatial and sequential features are extracted by trained CNN and BiLSTM networks, respectively, to enrich the features of short sequences. Finally, the two sets of features are weighted combined for the final decision. Trained by 220,000 sequences of 500 bp subsampled from the Virus and Host RefSeq genomes, DETIRE identifies more short viral sequences (<1,000 bp) than the three latest methods, such as DeepVirFinder, PPR-Meta, and CHEER. DETIRE is freely available at Github (https://github.com/crazyinter/DETIRE).
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