Center for non-coding RNA in Technology and Health, Department of Veterinary Clinical and Animal Sciences, Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research and The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark.
Bioinformatics. 2014 Feb 1;30(3):392-7. doi: 10.1093/bioinformatics/btt677. Epub 2013 Nov 21.
MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA-disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction methods do not explicitly facilitate forming hypotheses about the possible molecular causes of the association, thereby making the path to experimental follow-up longer.
Here we present miRPD in which miRNA-Protein-Disease associations are explicitly inferred. Besides linking miRNAs to diseases, it directly suggests the underlying proteins involved, which can be used to form hypotheses that can be experimentally tested. The inference of miRNAs and diseases is made by coupling known and predicted miRNA-protein associations with protein-disease associations text mined from the literature. We present scoring schemes that allow us to rank miRNA-disease associations inferred from both curated and predicted miRNA targets by reliability and thereby to create high- and medium-confidence sets of associations. Analyzing these, we find statistically significant enrichment for proteins involved in pathways related to cancer and type I diabetes mellitus, suggesting either a literature bias or a genuine biological trend. We show by example how the associations can be used to extract proteins for disease hypothesis.
All datasets, software and a searchable Web site are available at http://mirpd.jensenlab.org.
MicroRNAs (miRNAs) 是一类高度丰富的非编码 RNA 基因，参与细胞调节，因此也与疾病有关。尽管 miRNAs 是重要的疾病因素，但 miRNA-疾病关联的数量仍然很少，且可靠性也各不相同。此外，现有的数据库和预测方法并没有明确地促进对关联的可能分子原因形成假设，从而使实验后续的路径更长。
在这里，我们提出了 miRPD，其中明确推断了 miRNA-蛋白-疾病的关联。除了将 miRNA 与疾病联系起来，它还直接提示了涉及的潜在蛋白质，可用于形成可通过实验测试的假设。miRNA 和疾病的推断是通过将已知和预测的 miRNA-蛋白关联与从文献中挖掘的蛋白-疾病关联进行耦合来实现的。我们提出了评分方案，允许我们根据可靠性对从已编辑和预测的 miRNA 靶标推断出的 miRNA-疾病关联进行排名，并创建高可信度和中可信度的关联集。分析这些关联，我们发现与癌症和 I 型糖尿病相关途径相关的蛋白质存在统计学上显著的富集，这表明存在文献偏见或真实的生物学趋势。我们通过示例展示了如何使用关联来提取疾病假设中的蛋白质。
所有数据集、软件和可搜索的网站都可在 http://mirpd.jensenlab.org 上获得。
