DGIdb: mining the druggable genome
Malachi Griffith,Obi L. Griffith,Adam C. Coffman,James V. Weible,Josh F McMichael,Nicholas C. Spies,James Koval,Indraniel Das,Matthew B. Callaway,James M. Eldred,Christopher A. Miller,Janakiraman Subramanian,Ramaswamy Govindan,Runjun D. Kumar,Ron Bose,Li Ding,Jason Walker,David E. Larson,David J. Dooling,Scott M. Smith,Timothy J. Ley,Elaine R. Mardis,Richard K. Wilson +22 more
TLDR
The Drug-Gene Interaction database (DGIdb) provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially 'druggable' genes.Abstract:
The Drug-Gene Interaction database (DGIdb) mines existing resources that generate hypotheses about how mutated genes might be targeted therapeutically or prioritized for drug development. It provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially 'druggable' genes. DGIdb can be accessed at http://dgidb.org/.read more
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Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions
David M. Howard,Mark Adams,Toni-Kim Clarke,Jonathan D. Hafferty,Jude Gibson,Masoud Shirali,Jonathan R. I. Coleman,Jonathan R. I. Coleman,Saskia P. Hagenaars,Saskia P. Hagenaars,Joey Ward,Eleanor M. Wigmore,Clara Alloza,Xueyi Shen,Miruna C. Barbu,Eileen Y. Xu,Heather C. Whalley,Riccardo E. Marioni,David J. Porteous,Gail Davies,Ian J. Deary,Gibran Hemani,Klaus Berger,Henning Teismann,Rajesh Rawal,Volker Arolt,Bernhard T. Baune,Udo Dannlowski,Katharina Domschke,Chao Tian,David A. Hinds,Maciej Trzaskowski,Enda M. Byrne,Stephan Ripke,Stephan Ripke,Stephan Ripke,Daniel J. Smith,Patrick F. Sullivan,Patrick F. Sullivan,Naomi R. Wray,Gerome Breen,Gerome Breen,Cathryn M. Lewis,Cathryn M. Lewis,Andrew M. McIntosh,Andrew M. McIntosh +45 more
TL;DR: A genetic meta-analysis of depression found 269 associated genes that highlight several potential drug repositioning opportunities, and relationships with depression were found for neuroticism and smoking.
Journal ArticleDOI
The BioGRID Interaction Database: 2011 update
Chris Stark,Bobby-Joe Breitkreutz,Andrew Chatr-aryamontri,Lorrie Boucher,Rose Oughtred,Michael S. Livstone,Julie Nixon,Kimberly Van Auken,Xiaodong Wang,Xiaoqi Shi,Teresa Reguly,Jennifer M. Rust,Andrew G. Winter,Kara Dolinski,Mike Tyers +14 more
TL;DR: The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources that enable insights into conserved networks and pathways that are relevant to human health.
Journal ArticleDOI
The BioGRID interaction database: 2017 update
Andrew Chatr-aryamontri,Rose Oughtred,Lorrie Boucher,Jennifer M. Rust,Christie S. Chang,Nadine Kolas,Lara O'Donnell,Sara Oster,Chandra L. Theesfeld,Adnane Sellam,Chris Stark,Bobby-Joe Breitkreutz,Kara Dolinski,Mike Tyers +13 more
TL;DR: To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database.
Journal ArticleDOI
Pan-cancer analysis of the extent and consequences of intratumor heterogeneity
Noemi Andor,Trevor A. Graham,Marnix Jansen,Li C. Xia,C. Athena Aktipis,C. Athena Aktipis,Claudia Petritsch,Hanlee P. Ji,Hanlee P. Ji,Carlo C. Maley,Carlo C. Maley +10 more
TL;DR: Intratumor heterogeneity and genomic instability have the potential to be useful measures that can universally be applied to all cancers.
Journal ArticleDOI
DGIdb 3.0: a redesign and expansion of the drug-gene interaction database.
Kelsy C. Cotto,Alex H. Wagner,Yang-Yang Feng,Susanna Kiwala,Adam C. Coffman,Gregory C. Spies,Alex Wollam,Nicholas C. Spies,Obi L. Griffith,Malachi Griffith +9 more
TL;DR: DGIdb v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework.
References
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TL;DR: The results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents and the generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.
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