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Detecting operons in bacterial genomes via visual representation learning
Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822928/ https://www.ncbi.nlm.nih.gov/pubmed/33483546 http://dx.doi.org/10.1038/s41598-021-81169-9 |
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author | Assaf, Rida Xia, Fangfang Stevens, Rick |
author_facet | Assaf, Rida Xia, Fangfang Stevens, Rick |
author_sort | Assaf, Rida |
collection | PubMed |
description | Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts. |
format | Online Article Text |
id | pubmed-7822928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78229282021-01-27 Detecting operons in bacterial genomes via visual representation learning Assaf, Rida Xia, Fangfang Stevens, Rick Sci Rep Article Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts. Nature Publishing Group UK 2021-01-22 /pmc/articles/PMC7822928/ /pubmed/33483546 http://dx.doi.org/10.1038/s41598-021-81169-9 Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Assaf, Rida Xia, Fangfang Stevens, Rick Detecting operons in bacterial genomes via visual representation learning |
title | Detecting operons in bacterial genomes via visual representation learning |
title_full | Detecting operons in bacterial genomes via visual representation learning |
title_fullStr | Detecting operons in bacterial genomes via visual representation learning |
title_full_unstemmed | Detecting operons in bacterial genomes via visual representation learning |
title_short | Detecting operons in bacterial genomes via visual representation learning |
title_sort | detecting operons in bacterial genomes via visual representation learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822928/ https://www.ncbi.nlm.nih.gov/pubmed/33483546 http://dx.doi.org/10.1038/s41598-021-81169-9 |
work_keys_str_mv | AT assafrida detectingoperonsinbacterialgenomesviavisualrepresentationlearning AT xiafangfang detectingoperonsinbacterialgenomesviavisualrepresentationlearning AT stevensrick detectingoperonsinbacterialgenomesviavisualrepresentationlearning |