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Computational tools for plant small RNA detection and categorization

Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cel...

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Detalles Bibliográficos
Autores principales: Morgado, Lionel, Johannes, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781577/
https://www.ncbi.nlm.nih.gov/pubmed/29059285
http://dx.doi.org/10.1093/bib/bbx136
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author Morgado, Lionel
Johannes, Frank
author_facet Morgado, Lionel
Johannes, Frank
author_sort Morgado, Lionel
collection PubMed
description Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.
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spelling pubmed-67815772019-10-18 Computational tools for plant small RNA detection and categorization Morgado, Lionel Johannes, Frank Brief Bioinform Paper Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization. Oxford University Press 2017-10-20 /pmc/articles/PMC6781577/ /pubmed/29059285 http://dx.doi.org/10.1093/bib/bbx136 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Paper
Morgado, Lionel
Johannes, Frank
Computational tools for plant small RNA detection and categorization
title Computational tools for plant small RNA detection and categorization
title_full Computational tools for plant small RNA detection and categorization
title_fullStr Computational tools for plant small RNA detection and categorization
title_full_unstemmed Computational tools for plant small RNA detection and categorization
title_short Computational tools for plant small RNA detection and categorization
title_sort computational tools for plant small rna detection and categorization
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781577/
https://www.ncbi.nlm.nih.gov/pubmed/29059285
http://dx.doi.org/10.1093/bib/bbx136
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