Abstract
High-throughput sequencing technologies have identified millions of genetic mutations in multiple human diseases. However, the interpretation of the pathogenesis of these mutations and the discovery of driver genes that dominate disease progression is still a major challenge. Combining functional features such as protein post-translational modification (PTM) with genetic mutations is an effective way to predict such alterations. Here, we present PTMsnp, a web server that implements a Bayesian hierarchical model to identify driver genetic mutations targeting PTM sites. PTMsnp accepts genetic mutations in a standard variant call format or tabular format as input and outputs several interactive charts of PTM-related mutations that potentially affect PTMs. Additional functional annotations are performed to evaluate the impact of PTM-related mutations on protein structure and function, as well as to classify variants relevant to Mendelian disease. A total of 4,11,574 modification sites from 33 different types of PTMs and 1,776,848 somatic mutations from TCGA across 33 different cancer types are integrated into the web server, enabling identification of candidate cancer driver genes based on PTM. Applications of PTMsnp to the cancer cohorts and a GWAS dataset of type 2 diabetes identified a set of potential drivers together with several known disease-related genes, indicating its reliability in distinguishing disease-related mutations and providing potential molecular targets for new therapeutic strategies. PTMsnp is freely available at: http://ptmsnp.renlab.org.
Highlights
Large-scale genome sequencing has uncovered a complex landscape of genetic mutations in multiple patient populations
Previous studies have proven that combining mutations with other important functional
YBX supervised this work, designed the PTMsnp algorithm, reviewed, and edited the manuscript
Summary
Large-scale genome sequencing has uncovered a complex landscape of genetic mutations in multiple patient populations. Several approaches that consider recurrent mutations and combine other functional features, such as evolutionary conservation (Reva et al, 2011), known pathway annotation (Wendl et al, 2011) and protein-protein interaction networks (Vandin et al, 2011; Ciriello et al, 2012), have been proposed Among those functional features, one of the most critical factors that can be used in driver gene identification is protein post-translational modifications (PTMs). Genetic mutations that occur around the PTM sites ( known as PTM-related mutations) may potentially alter protein functions and disturb regulatory pathways in vivo, leading to the development of certain serious diseases, such as cancers. Previous studies mainly developed a database to curate PTMrelated mutations obtained by their computational methods for user search, there is still no web-based tool available to annotate rare mutations in new disease research by PTM function. Several known disease-related genes were successfully identified by PTMsnp, demonstrating that it is practicable to discover putative diseaserelated genes and hypothesize how they biochemically function in disease development
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