DrugThatGene: Integrative analysis to streamline the identification of druggable genes, pathways and protein complexes from CRISPR screens

Matthew C. Canver, Daniel E. Bauer, Takahiro Maeda, Luca Pinello

Research output: Contribution to journalArticle

Abstract

Motivation: The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-Associated (Cas) nuclease system has allowed for high-Throughput, large scale pooled screens for functional genomic studies. To aid in the translation of functional genomics to therapeutics, we developed DrugThatGene (DTG) as a web-based application that streamlines analysis of potential therapeutic targets identified from functional genetic screens. Results: Starting from a gene list as input, DTG offers automated identification of small molecules along with supporting information from human genetic and other relevant databases. Furthermore, DTG aids in the identification of common biological pathways and protein complexes in conjunction with associated small molecule inhibitors. Taken together, DTG aims to expedite the identification of small molecules from the abundance of functional genetic data generated from CRISPR screens. Availability and implementation: DTG is an open-source and free software available as a website at http://drugthatgene.pinellolab.org. Source code is available at: https://github.com/pinellolab/DrugThatGene, which can be downloaded in order to run DTG locally.

Original languageEnglish
Article numberbty913
Pages (from-to)1981-1984
Number of pages4
JournalBioinformatics
Volume35
Issue number11
DOIs
Publication statusPublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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