GREP: genome for REPositioning drugs

Saori Sakaue; Yukinori Okada

doi:10.1093/bioinformatics/btz166

GREP: genome for REPositioning drugs

Bioinformatics. 2019 Oct 1;35(19):3821-3823. doi: 10.1093/bioinformatics/btz166.

Authors

Saori Sakaue^{1

2

3}, Yukinori Okada^{1

2

4}

Affiliations

¹ Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
² Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
³ Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
⁴ Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.

Abstract

Summary: Making use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics.

Availability and implementation: GREP is available at https://github.com/saorisakaue/GREP as a python source code.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Drug Repositioning*
Genome*
Genome-Wide Association Study*
Genomics
Software