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Computational recognition for long non-coding RNA (lncRNA): Software and databases.
Brief Bioinform ; 18(1): 9-27, 2017 01.
Article em En | MEDLINE | ID: mdl-26839320
ABSTRACT
Since the completion of the Human Genome Project, it has been widely established that most DNA is not transcribed into proteins. These non-protein-coding regions are believed to be moderators within transcriptional and post-transcriptional processes, which play key roles in the onset of diseases. Long non-coding RNAs (lncRNAs) are generally lacking in conserved motifs typically used for detection and thus hard to identify, but nonetheless present certain characteristic features that can be exploited by bioinformatics methods. By combining lncRNA detection with known miRNA, RNA-binding protein and chromatin interaction, current tools are able to recognize and functionally annotate large number of lncRNAs. This review discusses databases and platforms dedicated to cataloging and annotating lncRNAs, as well as tools geared at discovering novel sequences. We emphasize the issues posed by the diversity of lncRNAs and their complex interaction mechanisms, as well as technical issues such as lack of unified nomenclature. We hope that this wide overview of existing platforms and databases might help guide biologists toward the tools they need to analyze their experimental data, while our discussion of limitations and of current lncRNA-related methods may assist in the development of new computational tools.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2017 Tipo de documento: Article
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