The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/15022410

Bongers, Brandon; Gorostiola Gonzalez, Marina; Wang, Xuesong; W. T. van Vlijmen, Herman; Jespers, Willem et. al. (2021): Data underlying the article: Pan-cancer in silico analysis of somatic mutations in G-protein coupled receptors: The effect of evolutionary conservation and natural variance. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/15022410.v1

Dataset

• Theoretical and Computational Chemistry
• Genetics

1000 Genomes, Cancer, GDC, GPCR, Multi-objective, Mutations, Natural variance, Pareto optimization

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Brandon Bongers , Marina Gorostiola Gonzalez , Xuesong Wang , Herman W. T. van Vlijmen , Willem Jespers , Hugo Gutiérrez-de-Terán , Kai Ye , Adriaan P. IJzerman , Laura H. Heitman , Gerard van Westen

This repository contains the datasets and source code supporting the conclusions of the manuscript "Pan-cancer in silico analysis of somatic mutations in G-protein coupled receptors: The effect of evolutionary conservation and natural variance". G protein-coupled receptors (GPCRs) form the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used an aggregated dataset of mutations found in cancer patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 Genomes project. We investigated the location of these mutations across the protein domains and conserved residues in GPCRs such as the “DRY” motif. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). In conclusion, this study identifies a list of GPCRs that are prioritized for experimental follow up characterization to elucidate their role in cancer. The computational approach here described can be adapted to investigate the roles in cancer of any protein family.

• 2021-10-26 first online, published, posted

4TU.ResearchData

/data contains .txt tab-separated files and .csv comma-separated files /GDC_dataset contains 7a compressed .sql file /PP_scripts contains .xml files

• STW-Veni #14410
• Oncode Institute funding

Leiden Academic Centre for Drug Research (LACDR), The Netherlands
Leiden University, The Netherlands
Oncode Institute, The Netherlands
Janssen Pharmaceutica NV, Beerse, Belgium
Uppsala University, Sweden
Xi'an Jiaotong University, China