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. 2017 Jul 3;14(7):963-971.
doi: 10.1080/15476286.2017.1312243. Epub 2017 Apr 7.

DES-ncRNA: A Knowledgebase for Exploring Information About Human Micro and Long Noncoding RNAs Based on Literature-Mining

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Free PMC article

DES-ncRNA: A Knowledgebase for Exploring Information About Human Micro and Long Noncoding RNAs Based on Literature-Mining

Adil Salhi et al. RNA Biol. .
Free PMC article

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 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.

Keywords: Alzheimer disease; Noncoding RNA; bioinformatics; data-mining; information integration; knowledgebase; literature-mining; long noncoding RNA; microRNA; text-mining.

Figures

Figure 1.
Figure 1.
DES-ncRNA 3-tier server architecture.
Figure 2.
Figure 2.
Step-by-step illustration of how DES-ncRNA can be used to identify ncRNA-related components involved in AD progression. The green circles represent the “Human MicroRNAs” dictionary; the gray upside-down triangles represent the “Cellular Component” dictionary; the yellow parallelograms represent ““Disease Ontology”” dictionary; the yellow squares represent the “Human Genes and Proteins” dictionary; and the lime circles represent “Human Long Non-Coding RNAs” dictionary. The edge color is distributed across a color spectrum from hot/red (high frequency co-occurrence/strong association) to cold/blue (small number of co-occurrences, weaker association). The numbers on the edges provide the number of publications that link the associated nodes.

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