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Exploiting plant transcriptomic databases: Resources, tools, and approaches
There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284291/ https://www.ncbi.nlm.nih.gov/pubmed/35605200 http://dx.doi.org/10.1016/j.xplc.2022.100323 |
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author | Lim, Peng Ken Zheng, Xinghai Goh, Jong Ching Mutwil, Marek |
author_facet | Lim, Peng Ken Zheng, Xinghai Goh, Jong Ching Mutwil, Marek |
author_sort | Lim, Peng Ken |
collection | PubMed |
description | There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by others to ask and answer questions that go beyond the aims of the study that generated the data. Because gene expression provides essential clues to where and when a gene is active, the data provide powerful tools for predicting gene function, and comparative analyses allow us to study plant evolution from a new perspective. This review describes how we can gain new knowledge from gene expression profiles, expression specificities, co-expression networks, differential gene expression, and experiment correlation. We also introduce and demonstrate databases that provide user-friendly access to these tools. |
format | Online Article Text |
id | pubmed-9284291 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-92842912022-07-16 Exploiting plant transcriptomic databases: Resources, tools, and approaches Lim, Peng Ken Zheng, Xinghai Goh, Jong Ching Mutwil, Marek Plant Commun Review Article There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by others to ask and answer questions that go beyond the aims of the study that generated the data. Because gene expression provides essential clues to where and when a gene is active, the data provide powerful tools for predicting gene function, and comparative analyses allow us to study plant evolution from a new perspective. This review describes how we can gain new knowledge from gene expression profiles, expression specificities, co-expression networks, differential gene expression, and experiment correlation. We also introduce and demonstrate databases that provide user-friendly access to these tools. Elsevier 2022-04-09 /pmc/articles/PMC9284291/ /pubmed/35605200 http://dx.doi.org/10.1016/j.xplc.2022.100323 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review Article Lim, Peng Ken Zheng, Xinghai Goh, Jong Ching Mutwil, Marek Exploiting plant transcriptomic databases: Resources, tools, and approaches |
title | Exploiting plant transcriptomic databases: Resources, tools, and approaches |
title_full | Exploiting plant transcriptomic databases: Resources, tools, and approaches |
title_fullStr | Exploiting plant transcriptomic databases: Resources, tools, and approaches |
title_full_unstemmed | Exploiting plant transcriptomic databases: Resources, tools, and approaches |
title_short | Exploiting plant transcriptomic databases: Resources, tools, and approaches |
title_sort | exploiting plant transcriptomic databases: resources, tools, and approaches |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284291/ https://www.ncbi.nlm.nih.gov/pubmed/35605200 http://dx.doi.org/10.1016/j.xplc.2022.100323 |
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