Abstract
Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA ( www.cbrc.kaust.edu.sa/des_ncrna ). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer's disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 963-971 |
| Number of pages | 9 |
| Journal | RNA Biology |
| Volume | 14 |
| Issue number | 7 |
| DOIs | |
| State | Published - Apr 7 2017 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): BAS/1/1606-01-01
Acknowledgements: The computational analysis for this study was performed on Dragon and Snapdragon compute clusters of the Computational Bioscience Research Center (CBRC) at King Abdullah University of Science and Technology (KAUST). The King Abdullah University of Science and Technology (KAUST) Base Research Funds [BAS/1/1606-01-01] to VBB supported research reported in this publication. This work was also supported by grants from Strategic Priority Research Program of the Chinese Academy of Sciences [XDB13040500 to ZZ], International Partnership Program of the Chinese Academy of Sciences [153F11KYSB20160008], National Programs for High Technology Research and Development [2015AA020108 to ZZ] and The 100 Talent Program of the Chinese Academy of Sciences (to ZZ). Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No 173034 support VPB.
