| DC Field | Value | Language |
|---|
| dc.contributor.author | Vieira, Lucas Maciel | - |
| dc.contributor.author | Grativol, Clicia | - |
| dc.contributor.author | Thiebaut, Flavia | - |
| dc.contributor.author | Carvalho, Thais G. | - |
| dc.contributor.author | Hardoim, Pablo R. | - |
| dc.contributor.author | Hemerly, Adriana | - |
| dc.contributor.author | Lifschitz, Sergio | - |
| dc.contributor.author | Ferreira, Paulo Cavalcanti Gomes | - |
| dc.contributor.author | Walter, Maria Emília Machado Telles | - |
| dc.date.accessioned | 2018-06-26T14:23:06Z | - |
| dc.date.available | 2018-06-26T14:23:06Z | - |
| dc.date.issued | 2017-03-04 | - |
| dc.identifier.citation | VIEIRA, Lucas Maciel et al. PlantRNA_sniffer : a SVM-based workflow to predict long intergenic non-coding RNAs in plants. Non-coding RNA v. 3, n.1, 11, 2017. doi: 10.3390/ncrna3010011. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/32107 | - |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | MDFI | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | PlantRNA_sniffer : a SVM-based workflow to predict long intergenic non-coding RNAs in plants | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Ácido ribonucléico | pt_BR |
| dc.subject.keyword | Plantas | pt_BR |
| dc.subject.keyword | Cana-de-açúcar | pt_BR |
| dc.subject.keyword | Milho | pt_BR |
| dc.subject.keyword | Biologia computacional | pt_BR |
| dc.rights.license | © 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/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.3390/ncrna3010011 | - |
| dc.description.abstract1 | 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. | pt_BR |
| Appears in Collections: | CIC - Artigos publicados em periódicos
|
