ArticleProceedings of the National Academy of Sciences of the United States of America2022
Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47 citing papers in PubMed.
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- seqLens: Optimizing Language Models for Genomic Predictions.Molecular biology and evolution · 2026Article
- Harnessing the microbiome for cancer therapy.Nature reviews. Microbiology · 2026Review
- 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026Article
- Scalable homology detection with ERAST.Nature biotechnology · 2026Article
- Sedimentary Metagenomics Reveal Avian Community Transitions From the Last Glacial Maximum to the Holocene.Ecology and evolution · 2026Article
- Draft genomes of two Lethrus species.Scientific data · 2026Article
- DeepTaxa: a hybrid CNN-BERT framework for 16S rRNA taxonomic classification.Bioinformatics advances · 2026Article
- Advancing biological taxonomy in the AI era: deep learning applications, challenges, and future directions.Science China. Life sciences · 2026Review
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- Report of the fourth conference on next-generation sequencing (NGS) for adventitious virus detection in biologics for humans and animals: Validation and implementation of NGS.Biologicals : journal of the International Association of Biological Standardization · 2025Article
- The effect of taxonomic, host-dependent features and sample bias on virus host prediction using machine learning and short sequence k-mers.Scientific reports · 2025Article
- Environmental adaptations in metagenomes revealed by deep learning.BMC biology · 2025Article
- PhyloTune: An efficient method to accelerate phylogenetic updates using a pretrained DNA language model.Nature communications · 2025Article
- SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing.Bioinformatics (Oxford, England) · 2025Article
- PCVR: a pre-trained contextualized visual representation for DNA sequence classification.BMC bioinformatics · 2025Article
- Alignment-free viral sequence classification at scale.BMC genomics · 2025Article
- Single specimen genome assembly of Culicoides stellifer shows evidence of a non-retroviral endogenous viral element.BMC genomics · 2025Article
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5 authors.
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Abstract
Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the database, since representative relatives must be present. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a deep neural network program based on natural language processing to precisely classify the superkingdom and phylum of DNA sequences taxonomically without the need for a known representative relative from a database. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. For novel organisms, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality in almost all cases. Since BERTax is not based on similar entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences, thus increasing the overall information gain.
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