miRQuest: um middleware para investigação de miRNAs
miRNA belongs to the class of small RNAs non-coding (ncRNAs), been the target of several studies in the literature for his role in the regulation of mRNA levels (messenger RNA) in cells. Representing an class of endogenous RNAs of approximately 22 nucleotides (mature), that act as inhibitors/silence...
| Autor principal: | Ambrosio, Rosana Ressa Aguiar |
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| Formato: | Dissertação |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2018
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/2899 |
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| Resumo: |
miRNA belongs to the class of small RNAs non-coding (ncRNAs), been the target of several studies in the literature for his role in the regulation of mRNA levels (messenger RNA) in cells. Representing an class of endogenous RNAs of approximately 22 nucleotides (mature), that act as inhibitors/silencers post-transcriptional. Discovered at the end of last century in Caenorhabditis elegans, miRNAs are now recognized as key regulators of gene expression in plants and animals, among many eukaryotic organisms. According to the scientific literature, this is one of the most studied classes of ncRNAs by the scientific community nowadays. According to the scientific literature, this is one of the most studied classes of ncRNAs by nowadays scientific community. Given its importance, identify this class of ncRNA becomes of great interest as it allows to discover possible new microRNAs, as well as its regulatory role that can be connected to multiple biological processes. Bioinformatics, through in silico analysis of microRNA, either via pattern recognition approaches, for example, greatly contributes to the identification and annotation of this class ncRNA. This allowed the development of new techniques, methods and computational approaches that were able to contribute more effectively to the analysis and interpretation of the great mass of biological data, which has been generated at a higher frequency, especially in recent years. Although there are a variety of computational approaches described for the identification of microRNA, for the most part, these have some sort of limitation (eg outdated, no longer available). Thus, this work presents the miRQuest; an integrated system was built using a standard developing layer, through middleware, in a web platform for miRNA research that has two main functions: (i) the integration of different miRNA prediction tools to identify miRNA in a friendly environment; and (ii) benchmarking between these predictive tools. The miRQuest does not introduce a new computer model to predict miRNAs, but rather, a new methodology that permits simultaneously run different miRNA identification techniques. |
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