PTMsnp: A Web Server for the Identification of Driver Mutations That Affect Protein Post-translational Modification

Frontiers in Cell and Developmental Biology

Shaofeng Lin¹  Yu Xue¹  Bosu Hu²  Di Peng²  Huiqin Li²  Li Chen²  Yubin Xie²  Zhixiang Zuo³  Hongwan Zhang³  Jian Ren³ 

[1] Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China;Precision Medicine Institute, The First Affiliated Hospital, School of Life Sciences, Sun Yat-sen University, Guangzhou, China;State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China;

关键词: protein post-translational modification;    genetic mutations;    Bayesian hierarchical model;    driver genes;    disease;   

DOI  :  10.3389/fcell.2020.593661

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown