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Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks
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....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436379/ https://www.ncbi.nlm.nih.gov/pubmed/36018838 http://dx.doi.org/10.1073/pnas.2122636119 |
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author | Mock, Florian Kretschmer, Fleming Kriese, Anton Böcker, Sebastian Marz, Manja |
author_facet | Mock, Florian Kretschmer, Fleming Kriese, Anton Böcker, Sebastian Marz, Manja |
author_sort | Mock, Florian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9436379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-94363792023-02-26 Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Mock, Florian Kretschmer, Fleming Kriese, Anton Böcker, Sebastian Marz, Manja Proc Natl Acad Sci U S A Biological Sciences 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. National Academy of Sciences 2022-08-26 2022-08-30 /pmc/articles/PMC9436379/ /pubmed/36018838 http://dx.doi.org/10.1073/pnas.2122636119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Mock, Florian Kretschmer, Fleming Kriese, Anton Böcker, Sebastian Marz, Manja Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks |
title | Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks |
title_full | Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks |
title_fullStr | Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks |
title_full_unstemmed | Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks |
title_short | Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks |
title_sort | taxonomic classification of dna sequences beyond sequence similarity using deep neural networks |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436379/ https://www.ncbi.nlm.nih.gov/pubmed/36018838 http://dx.doi.org/10.1073/pnas.2122636119 |
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