PTMsnp

PTMsnp identifies driver genetic mutations that affect protein post-translational modification (PTM) sites using a Bayesian hierarchical model to predict PTM alterations caused by genetic variation.

Key Features:

• Bayesian Hierarchical Model: Uses a Bayesian hierarchical model to infer driver mutations targeting PTM sites.
• Variants Input Support: Accepts variant call format (VCF) or tabular variant inputs.
• Functional Annotations: Performs functional annotations to assess impact on protein structure and function, including classification relevant to Mendelian diseases.
• Data Integration: Integrates 4,115,748 modification sites across 33 PTM types and 1,776,848 somatic mutations from TCGA spanning 33 cancer types.

Scientific Applications:

• Cancer Research: Applied to TCGA and other cancer cohorts to identify candidate driver PTM-related mutations across 33 cancer types.
• GWAS (Type 2 Diabetes): Applied to a genome-wide association study dataset for type 2 diabetes to identify PTM-targeting mutations linked to disease genes.
• Therapeutic Target Discovery and Precision Medicine: Annotates mutations by their effects on PTMs to prioritize key proteins and potential therapeutic targets driving disease progression.

Methodology:

Implements a Bayesian hierarchical model to identify driver mutations targeting PTM sites, integrates PTM datasets (4,115,748 sites across 33 PTM types) with TCGA somatic mutations (1,776,848 mutations across 33 cancer types), accepts VCF or tabular variant inputs, and performs functional annotations including Mendelian disease classification.