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PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants

Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene re...

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Autores principales: Vieira, Lucas Maciel, Grativol, Clicia, Thiebaut, Flavia, Carvalho, Thais G., Hardoim, Pablo R., Hemerly, Adriana, Lifschitz, Sergio, Ferreira, Paulo Cavalcanti Gomes, Walter, Maria Emilia M. T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5831995/
https://www.ncbi.nlm.nih.gov/pubmed/29657283
http://dx.doi.org/10.3390/ncrna3010011
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author Vieira, Lucas Maciel
Grativol, Clicia
Thiebaut, Flavia
Carvalho, Thais G.
Hardoim, Pablo R.
Hemerly, Adriana
Lifschitz, Sergio
Ferreira, Paulo Cavalcanti Gomes
Walter, Maria Emilia M. T.
author_facet Vieira, Lucas Maciel
Grativol, Clicia
Thiebaut, Flavia
Carvalho, Thais G.
Hardoim, Pablo R.
Hemerly, Adriana
Lifschitz, Sergio
Ferreira, Paulo Cavalcanti Gomes
Walter, Maria Emilia M. T.
author_sort Vieira, Lucas Maciel
collection PubMed
description Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp.) and in maize (Zea mays). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.
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spelling pubmed-58319952018-04-12 PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants Vieira, Lucas Maciel Grativol, Clicia Thiebaut, Flavia Carvalho, Thais G. Hardoim, Pablo R. Hemerly, Adriana Lifschitz, Sergio Ferreira, Paulo Cavalcanti Gomes Walter, Maria Emilia M. T. Noncoding RNA Article Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp.) and in maize (Zea mays). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms. MDPI 2017-03-04 /pmc/articles/PMC5831995/ /pubmed/29657283 http://dx.doi.org/10.3390/ncrna3010011 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vieira, Lucas Maciel
Grativol, Clicia
Thiebaut, Flavia
Carvalho, Thais G.
Hardoim, Pablo R.
Hemerly, Adriana
Lifschitz, Sergio
Ferreira, Paulo Cavalcanti Gomes
Walter, Maria Emilia M. T.
PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants
title PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants
title_full PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants
title_fullStr PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants
title_full_unstemmed PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants
title_short PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants
title_sort plantrna_sniffer: a svm-based workflow to predict long intergenic non-coding rnas in plants
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5831995/
https://www.ncbi.nlm.nih.gov/pubmed/29657283
http://dx.doi.org/10.3390/ncrna3010011
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