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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9009326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT hellerphilip dielgeneexpressionimprovessoftwarepredictionofcyanobacterialoperons |