Detecting operons in bacterial genomes via visual representation learning

Abstract 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 represen...

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Autores principales:	Rida Assaf, Fangfang Xia, Rick Stevens
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/31c06986a4554e1b96606ca4d9307cb8
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id	oai:doaj.org-article:31c06986a4554e1b96606ca4d9307cb8
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spelling	oai:doaj.org-article:31c06986a4554e1b96606ca4d9307cb82021-12-02T13:51:06ZDetecting operons in bacterial genomes via visual representation learning10.1038/s41598-021-81169-92045-2322https://doaj.org/article/31c06986a4554e1b96606ca4d9307cb82021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81169-9https://doaj.org/toc/2045-2322Abstract 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.Rida AssafFangfang XiaRick StevensNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rida Assaf Fangfang Xia Rick Stevens Detecting operons in bacterial genomes via visual representation learning
description	Abstract 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	article
author	Rida Assaf Fangfang Xia Rick Stevens
author_facet	Rida Assaf Fangfang Xia Rick Stevens
author_sort	Rida Assaf
title	Detecting operons in bacterial genomes via visual representation learning
title_short	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_sort	detecting operons in bacterial genomes via visual representation learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/31c06986a4554e1b96606ca4d9307cb8
work_keys_str_mv	AT ridaassaf detectingoperonsinbacterialgenomesviavisualrepresentationlearning AT fangfangxia detectingoperonsinbacterialgenomesviavisualrepresentationlearning AT rickstevens detectingoperonsinbacterialgenomesviavisualrepresentationlearning
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Detecting operons in bacterial genomes via visual representation learning

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