RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data.
Brief Bioinform
; 21(6): 2167-2174, 2020 12 01.
Article
en En
| MEDLINE
| ID: mdl-31799597
ABSTRACT
Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity-related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL http//bio-bigdata.hrbmu.edu.cn/RNAactDrug.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Resistencia a Medicamentos
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MicroARNs
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Secuenciación de Nucleótidos de Alto Rendimiento
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ARN Largo no Codificante
Tipo de estudio:
Diagnostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Brief Bioinform
Asunto de la revista:
BIOLOGIA
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INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
China