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Diel gene expression improves software prediction of cyanobacterial operons

Cyanobacteria are important participants in global biogeochemical process, but their metabolic processes and genomic functions are incompletely understood. In particular, operon structure, which can provide valuable metabolic and genomic insight, is difficult to determine experimentally, and algorit...

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Detalles Bibliográficos
Autor principal: Heller, Philip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9009326/
https://www.ncbi.nlm.nih.gov/pubmed/35433132
http://dx.doi.org/10.7717/peerj.13259
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author Heller, Philip
author_facet Heller, Philip
author_sort Heller, Philip
collection PubMed
description Cyanobacteria are important participants in global biogeochemical process, but their metabolic processes and genomic functions are incompletely understood. In particular, operon structure, which can provide valuable metabolic and genomic insight, is difficult to determine experimentally, and algorithmic operon predictions probably underestimate actual operon extent. A software method is presented for enhancing current operon predictions by incorporating information from whole-genome time-series expression studies, using a Machine Learning classifier. Results are presented for the marine cyanobacterium Crocosphaera watsonii. A total of 15 operon enhancements are proposed. The source code is publicly available.
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spelling pubmed-90093262022-04-15 Diel gene expression improves software prediction of cyanobacterial operons Heller, Philip PeerJ Bioinformatics Cyanobacteria are important participants in global biogeochemical process, but their metabolic processes and genomic functions are incompletely understood. In particular, operon structure, which can provide valuable metabolic and genomic insight, is difficult to determine experimentally, and algorithmic operon predictions probably underestimate actual operon extent. A software method is presented for enhancing current operon predictions by incorporating information from whole-genome time-series expression studies, using a Machine Learning classifier. Results are presented for the marine cyanobacterium Crocosphaera watsonii. A total of 15 operon enhancements are proposed. The source code is publicly available. PeerJ Inc. 2022-04-11 /pmc/articles/PMC9009326/ /pubmed/35433132 http://dx.doi.org/10.7717/peerj.13259 Text en © 2022 Heller https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Heller, Philip
Diel gene expression improves software prediction of cyanobacterial operons
title Diel gene expression improves software prediction of cyanobacterial operons
title_full Diel gene expression improves software prediction of cyanobacterial operons
title_fullStr Diel gene expression improves software prediction of cyanobacterial operons
title_full_unstemmed Diel gene expression improves software prediction of cyanobacterial operons
title_short Diel gene expression improves software prediction of cyanobacterial operons
title_sort diel gene expression improves software prediction of cyanobacterial operons
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9009326/
https://www.ncbi.nlm.nih.gov/pubmed/35433132
http://dx.doi.org/10.7717/peerj.13259
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