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
Departamento de Ciência da Computação, Universidade de Brasília, Brasília-DF 70910-900, Brasil.
Laboratório de Química e Função de Proteínas e Peptídeos, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes-RJ 28013-602, Brazil.
Noncoding RNA. 2017 Mar 4;3(1):11. doi: 10.3390/ncrna3010011.
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 ( spp.) and in maize (). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.
非编码RNA（ncRNAs）是生物体细胞中产生的一组重要转录本。其中，有大量难以预测的特定类型的长链ncRNAs，即所谓的长链基因间ncRNAs（lincRNAs），它们可能在基因调控和其他细胞过程中发挥重要作用。尽管这些lincRNAs很重要，但仍然缺乏生物学知识，而且目前所考虑的少数计算方法非常特定，以至于无法成功应用于与它们最初设计时所针对的物种不同的其他物种。lncRNAs的预测已经通过机器学习技术进行。特别是，对于lincRNA预测，最近的文献中已经探索了监督学习方法。据我们所知，还没有专门设计用于预测植物中lincRNAs的方法或工作流程。在此背景下，这项工作提出了一种用于预测植物中lincRNAs的工作流程，该工作流程考虑了一个包括已知生物信息学工具以及机器学习技术（这里是支持向量机（SVM））的工作流程。我们讨论了两个案例研究，它们分别在甘蔗（spp.）和玉米（）中鉴定出了新的lincRNAs。从结果中，我们还能够鉴定出受到致病和有益微生物影响的甘蔗和玉米植株中差异表达的lincRNAs。
