Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
Nucleic Acids Res. 2018 Jan 4;46(D1):D360-D370. doi: 10.1093/nar/gkx1144.
MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.
微小 RNA 是基因表达的重要调节因子,通过与待调节的基因结合来实现。即使使用现代高通量技术,也很难且昂贵地检测到所有可能的微小 RNA 靶标。出于这个原因,已经开发了几种计算微小 RNA 靶标预测工具,每个工具都有其自身的优势和局限性。整合不同的工具是一种成功的方法,可以最大限度地减少单个数据库的缺点。在这里,我们展示了 mirDIP v4.1,它提供了近 1.52 亿个人类微小 RNA 靶标预测,这些预测是从 30 个不同的资源中收集的。我们还介绍了一个综合得分,该得分是从获得的预测中统计推断出来的,并分配给每个独特的微小 RNA-靶标相互作用,以提供置信度的统一度量。我们证明,整合来自多个资源的预测不会导致对生物过程或途径的预测偏向。mirDIP v4.1 可在 http://ophid.utoronto.ca/mirDIP/ 免费获得。