| Literature DB >> 29194489 |
Tomas Tokar1, Chiara Pastrello1, Andrea E M Rossos1, Mark Abovsky1, Anne-Christin Hauschild1, Mike Tsay1, Richard Lu1, Igor Jurisica1,2,3,4.
Abstract
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/.Entities:
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Year: 2018 PMID: 29194489 PMCID: PMC5753284 DOI: 10.1093/nar/gkx1144
Source DB: PubMed Journal: Nucleic Acids Res ISSN: 0305-1048 Impact factor: 16.971