BERTax: taxonomic classification of DNA sequences with Deep Neural Networks Article Swipe
Florian Mock
,
Fleming Kretschmer
,
Anton Kriese
,
Sebastian Böcker
,
Manja Marz
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.07.09.451778
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.07.09.451778
Taxonomic classification, i.e., the identification and assignment to groups of biological organisms with the same origin and characteristics, is a common task in genetics. Nowadays, taxonomic classification is mainly based on genome similarity search to large genome databases. In this process, the classification quality depends heavily on the database since representative relatives have to be known already. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax , a program that uses a deep neural network to pre-cisely classify the superkingdom, phylum, and genus of DNA sequences taxonomically without the need for a known representative relative from a database. For this, BERTax uses the natural language processing model BERT trained to represent DNA. 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. In case of an entirely novel organism, 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. Since BERTax is not based on homologous entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences. This leads to a higher number of correctly classified sequences and thus increases the overall information gain.
Related Topics
Concepts
Taxonomic rank
Identification (biology)
Artificial intelligence
Genome
Range (aeronautics)
Artificial neural network
Similarity (geometry)
DNA sequencing
Computer science
Biological classification
Biology
Evolutionary biology
DNA
Gene
Genetics
Ecology
Taxon
Composite material
Image (mathematics)
Materials science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.07.09.451778
- https://www.biorxiv.org/content/biorxiv/early/2021/07/10/2021.07.09.451778.full.pdf
- OA Status
- green
- Cited By
- 21
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3179640609
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3179640609Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.07.09.451778Digital Object Identifier
- Title
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BERTax: taxonomic classification of DNA sequences with Deep Neural NetworksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-07-10Full publication date if available
- Authors
-
Florian Mock, Fleming Kretschmer, Anton Kriese, Sebastian Böcker, Manja MarzList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.07.09.451778Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/07/10/2021.07.09.451778.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2021/07/10/2021.07.09.451778.full.pdfDirect OA link when available
- Concepts
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Taxonomic rank, Identification (biology), Artificial intelligence, Genome, Range (aeronautics), Artificial neural network, Similarity (geometry), DNA sequencing, Computer science, Biological classification, Biology, Evolutionary biology, DNA, Gene, Genetics, Ecology, Taxon, Composite material, Image (mathematics), Materials scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 9, 2023: 5, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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