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Nucleic Acids Res. 2014;42(17):e133. doi: 10.1093/nar/gku631. Epub 2014 Jul 24.

The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations.

Author information

1
Department of Surgery, School of Medicine, University of Colorado Denver, Aurora, CO 80045, USA.
2
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA Katerina.Kechris@ucdenver.edu.
3
Department of Pharmaceutical Sciences, School of Pharmacy, University of Colorado Denver, Aurora, CO 80045, USA.
4
Department of Pharmacology, School of Medicine, University of Colorado Denver, Aurora, CO 80045, USA.
5
Department of Medicine, National Jewish Health, Denver, CO 80206, USA.
6
Department of Experimental Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
7
Department of Biomedical Sciences, University of Minnesota Medical School Duluth Campus, Duluth, MN 55812, USA.
8
Department of Surgery, School of Medicine, University of Colorado Denver, Aurora, CO 80045, USA Department of Pharmacology, School of Medicine, University of Colorado Denver, Aurora, CO 80045, USA University of Colorado Comprehensive Cancer Center, Aurora, CO 80045, USA Dan.Theodorescu@ucdenver.edu.

Abstract

microRNAs (miRNAs) regulate expression by promoting degradation or repressing translation of target transcripts. miRNA target sites have been catalogued in databases based on experimental validation and computational prediction using various algorithms. Several online resources provide collections of multiple databases but need to be imported into other software, such as R, for processing, tabulation, graphing and computation. Currently available miRNA target site packages in R are limited in the number of databases, types of databases and flexibility. We present multiMiR, a new miRNA-target interaction R package and database, which includes several novel features not available in existing R packages: (i) compilation of nearly 50 million records in human and mouse from 14 different databases, more than any other collection; (ii) expansion of databases to those based on disease annotation and drug microRNAresponse, in addition to many experimental and computational databases; and (iii) user-defined cutoffs for predicted binding strength to provide the most confident selection. Case studies are reported on various biomedical applications including mouse models of alcohol consumption, studies of chronic obstructive pulmonary disease in human subjects, and human cell line models of bladder cancer metastasis. We also demonstrate how multiMiR was used to generate testable hypotheses that were pursued experimentally.

PMID:
25063298
PMCID:
PMC4176155
DOI:
10.1093/nar/gku631
[Indexed for MEDLINE]
Free PMC Article

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