Post-translational modifications (PTMs) are covalent modifications that proteins might undergo following or sometimes during the process of translation. Together with gene diversity, PTMs contribute to the overall variety of possible protein function for a given organism. Single-nucleotide polymorphisms (SNPs) are the most common form of variations found in the human genome, and have been found to be associated with diseases like Alzheimer's disease (AD) and Parkinson's disease (PD), among many others. Studies have also shown that non-synonymous single-nucleotide variation (nsSNV) at the PTM site, which alters the corresponding encoded amino acid in the translated protein sequence, can lead to abnormal activity of a protein and can contribute to a disease phenotype. Significant advances in next-generation sequencing (NGS) technologies and high-throughput proteomics have resulted in the generation of a huge amount of data for both SNPs and PTMs. However, these data are unsystematically distributed across a number of diverse databases. Thus, there is a need for efforts toward data standardization and validation of bioinformatics algorithms that can fully leverage SNP and PTM information for biomedical research. In this book chapter, we will present some of the commonly used databases for both SNVs and PTMs and describe a broad approach that can be applied to many scenarios for studying the impact of nsSNVs on PTM sites of human proteins.
Keywords: Disease association; PTM databases; Pan-cancer variomes; Post-translational modifications (PTMs); SNP databases; Single-nucleotide variations; nsSNVs.