PlantRNA_sniffer : a SVM-based workflow to predict long intergenic non-coding RNAs in plants
Salvato in:
| Autore principale: | |
|---|---|
| Data di pubblicazione: | 2017 |
| Altri autori: | , , , , , , , |
| Natura: | Article |
| Lingua: | eng |
| Fonte: | Repositório Institucional da UnB |
| DOI: | https://doi.org/10.3390/ncrna3010011 |
| Download full: | http://repositorio.unb.br/handle/10482/32107 https://doi.org/10.3390/ncrna3010011 |
Riassunto: | 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. |
Documenti analoghi: PlantRNA_sniffer : a SVM-based workflow to predict long intergenic non-coding RNAs in plants
- A support vector machine based method to distinguish long non-coding RNAs from protein coding transcripts
- Roles of non-coding RNA in sugarcane-microbe interaction
- Identificação de RNAs não-codificadores por modelos de covariância com prioris Dirichlet adaptadas a grupos de ncRNAs com estruturas secundárias similares = Identifying non-coding RNAs using covariance models with Dirichlet priors specific to groups of ncRNAs of similar secondary structures
- Distinguishing long non-coding RNAs from protein coding transcripts based on machine learning techniques
- Análise de microRNAs músculo específicos em frações plasmáticas antes e após corrida de meia maratona
- SnoReport 2.0 : new features and a refined Support Vector Machine to improve snoRNA identification
