ArticleNAR genomics and bioinformatics2023
MetaTransformer: deep metagenomic sequencing read classification using self-attention models.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- seqLens: Optimizing Language Models for Genomic Predictions.Molecular biology and evolution · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026Article
- Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- Large language models in bioinformatics: a comprehensive survey.Frontiers in genetics · 2026Review
- Charting the virosphere: computational synergies of AI and bioinformatics in viral discovery and evolution.Journal of virology · 2025Review
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- NextVir: Enabling classification of tumor-causing viruses with genomic foundation models.PLoS computational biology · 2025Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- HFTC: a hierarchical fungal taxonomic classification model for ITS sequences using low-dimensional embedding features.Frontiers in genetics · 2025Article
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- Taxometer: Improving taxonomic classification of metagenomics contigs.Nature communications · 2024Article
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Transformer technology in molecular science.Wiley interdisciplinary reviews. Computational molecular scienceArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Deep learning has emerged as a paradigm that revolutionizes numerous domains of scientific research. Transformers have been utilized in language modeling outperforming previous approaches. Therefore, the utilization of deep learning as a tool for analyzing the genomic sequences is promising, yielding convincing results in fields such as motif identification and variant calling. DeepMicrobes, a machine learning-based classifier, has recently been introduced for taxonomic prediction at species and genus level. However, it relies on complex models based on bidirectional long short-term memory cells resulting in slow runtimes and excessive memory requirements, hampering its effective usability. We present MetaTransformer, a self-attention-based deep learning metagenomic analysis tool. Our transformer-encoder-based models enable efficient parallelization while outperforming DeepMicrobes in terms of species and genus classification abilities. Furthermore, we investigate approaches to reduce memory consumption and boost performance using different embedding schemes. As a result, we are able to achieve 2× to 5× speedup for inference compared to DeepMicrobes while keeping a significantly smaller memory footprint. MetaTransformer can be trained in 9 hours for genus and 16 hours for species prediction. Our results demonstrate performance improvements due to self-attention models and the impact of embedding schemes in deep learning on metagenomic sequencing data.
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Registered trials
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.