STOPGAP
Computational Tool
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General Information
DescriptionA database for systematic target opportunity assessment by genetic association predictions
Homepagehttps://github.com/StatGenPRD/STOPGAP
Publications
STOPGAP: a database for systematic target opportunity assessment by genetic association predictions
Judong Shen; Kijoung Song; Andrew J. Slater; Enrico Ferrero; Matthew R. Nelson
Summary: We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications.
Availability and implementation: Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP. Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com.
Contact: matthew.r.nelson@gsk.com
Supplementary information: Supplementary data are available at Bioinformatics online.
DOI: https://doi.org/10.1093/bioinformatics/btx274
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