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1.
Bioinformatics ; 31(9): 1502-4, 2015 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-25527833

RESUMO

SUMMARY: Identifying, amongst millions of publications available in MEDLINE, those that are relevant to specific microRNAs (miRNAs) of interest based on keyword search faces major obstacles. References to miRNA names in the literature often deviate from standard nomenclature for various reasons, since even the official nomenclature evolves. For instance, a single miRNA name may identify two completely different molecules or two different names may refer to the same molecule. mirPub is a database with a powerful and intuitive interface, which facilitates searching for miRNA literature, addressing the aforementioned issues. To provide effective search services, mirPub applies text mining techniques on MEDLINE, integrates data from several curated databases and exploits data from its user community following a crowdsourcing approach. Other key features include an interactive visualization service that illustrates intuitively the evolution of miRNA data, tag clouds summarizing the relevance of publications to particular diseases, cell types or tissues and access to TarBase 6.0 data to oversee genes related to miRNA publications. AVAILABILITY AND IMPLEMENTATION: mirPub is freely available at http://www.microrna.gr/mirpub/. CONTACT: vergoulis@imis.athena-innovation.gr or dalamag@imis.athena-innovation.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Bases de Dados Bibliográficas , MicroRNAs , Mineração de Dados , MEDLINE , Publicações
2.
Nucleic Acids Res ; 38(Database issue): D137-41, 2010 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-19850714

RESUMO

MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3'UTR region of mRNAs. MicroRNAs are produced from longer transcripts which can code for more than one mature miRNAs. miRGen 2.0 is a database that aims to provide comprehensive information about the position of human and mouse microRNA coding transcripts and their regulation by transcription factors, including a unique compilation of both predicted and experimentally supported data. Expression profiles of microRNAs in several tissues and cell lines, single nucleotide polymorphism locations, microRNA target prediction on protein coding genes and mapping of miRNA targets of co-regulated miRNAs on biological pathways are also integrated into the database and user interface. The miRGen database will be continuously maintained and freely available at http://www.microrna.gr/mirgen/.


Assuntos
Regiões 3' não Traduzidas , Biologia Computacional/métodos , Bases de Dados Genéticas , Bases de Dados de Ácidos Nucleicos , MicroRNAs , Fatores de Transcrição/genética , Algoritmos , Animais , Linhagem Celular Tumoral , Biologia Computacional/tendências , Humanos , Armazenamento e Recuperação da Informação/métodos , Internet , Camundongos , MicroRNAs/metabolismo , Polimorfismo de Nucleotídeo Único , Software
3.
BMC Bioinformatics ; 10: 295, 2009 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-19765283

RESUMO

BACKGROUND: MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. RESULTS: DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. CONCLUSION: Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT.


Assuntos
Algoritmos , MicroRNAs/química , Proteínas/metabolismo , Análise de Sequência de RNA/métodos , Sítios de Ligação , Biologia Computacional/métodos , MicroRNAs/metabolismo , Proteínas/química
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