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Non-Coding RNA 2017, 3(1), 11; doi:10.3390/ncrna3010011

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

1
Departamento de Ciência da Computação, Universidade de Brasília, Brasília—DF 70910-900, Brasil
2
Laboratório de Química e Função de Proteínas e Peptídeos, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes—RJ 28013-602, Brazil
3
Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro—RJ 21941-901, Brazil
4
Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro—RJ 22451-900, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: Jian-Hua Yang and Liang-Hu Qu
Received: 29 December 2016 / Revised: 19 February 2017 / Accepted: 24 February 2017 / Published: 4 March 2017
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
View Full-Text   |   Download PDF [2086 KB, uploaded 4 March 2017]   |  

Abstract

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. View Full-Text
Keywords: long non-coding RNAs; long intergenic non-coding RNAs; plants; sugarcane; maize; SVM-based workflow; bioinformatics long non-coding RNAs; long intergenic non-coding RNAs; plants; sugarcane; maize; SVM-based workflow; bioinformatics
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MDPI and ACS Style

Vieira, L.M.; Grativol, C.; Thiebaut, F.; Carvalho, T.G.; Hardoim, P.R.; Hemerly, A.; Lifschitz, S.; Ferreira, P.C.G.; Walter, M.E.M.T. PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants. Non-Coding RNA 2017, 3, 11.

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