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Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking

BACKGROUND: The regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns,...

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Autores principales: Zafrir, Zohar, Tuller, Tamir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286865/
https://www.ncbi.nlm.nih.gov/pubmed/28143396
http://dx.doi.org/10.1186/s12859-017-1497-z
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author Zafrir, Zohar
Tuller, Tamir
author_facet Zafrir, Zohar
Tuller, Tamir
author_sort Zafrir, Zohar
collection PubMed
description BACKGROUND: The regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns, etc.). However, the entire gene expression codes and the genomic regions where they are encoded are still unknown. RESULTS: Here, we employ an unsupervised approach to estimate the concentration of gene expression codes in different non-coding parts of genes and transcripts, such as introns and untranslated regions, focusing on three model organisms (Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe). Our analyses support the conjecture that regions adjacent to the beginning and end of ORFs and the beginning and end of introns tend to include higher concentration of gene expression information relatively to regions further away. In addition, we report the exact regions with elevated concentration of gene expression codes. Furthermore, we demonstrate that the concentration of these codes in different genetic regions is correlated with the expression levels of the corresponding genes, and with splicing efficiency measurements and meiotic stage gene expression measurements in S. cerevisiae. CONCLUSION: We suggest that these discoveries improve our understanding of gene expression regulation and evolution; they can also be used for developing improved models of genome/gene evolution and for engineering gene expression in various biotechnological and synthetic biology applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1497-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-52868652017-02-06 Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking Zafrir, Zohar Tuller, Tamir BMC Bioinformatics Research Article BACKGROUND: The regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns, etc.). However, the entire gene expression codes and the genomic regions where they are encoded are still unknown. RESULTS: Here, we employ an unsupervised approach to estimate the concentration of gene expression codes in different non-coding parts of genes and transcripts, such as introns and untranslated regions, focusing on three model organisms (Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe). Our analyses support the conjecture that regions adjacent to the beginning and end of ORFs and the beginning and end of introns tend to include higher concentration of gene expression information relatively to regions further away. In addition, we report the exact regions with elevated concentration of gene expression codes. Furthermore, we demonstrate that the concentration of these codes in different genetic regions is correlated with the expression levels of the corresponding genes, and with splicing efficiency measurements and meiotic stage gene expression measurements in S. cerevisiae. CONCLUSION: We suggest that these discoveries improve our understanding of gene expression regulation and evolution; they can also be used for developing improved models of genome/gene evolution and for engineering gene expression in various biotechnological and synthetic biology applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1497-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-01 /pmc/articles/PMC5286865/ /pubmed/28143396 http://dx.doi.org/10.1186/s12859-017-1497-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Zafrir, Zohar
Tuller, Tamir
Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
title Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
title_full Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
title_fullStr Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
title_full_unstemmed Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
title_short Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
title_sort unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286865/
https://www.ncbi.nlm.nih.gov/pubmed/28143396
http://dx.doi.org/10.1186/s12859-017-1497-z
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