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Nucleic Acids Res. 2018 Jan 4;46(D1):D901-D910. doi: 10.1093/nar/gkx973.

ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins.

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Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada.
Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Québec, Québec, Canada.


Interpretation of genetic variation is needed for deciphering genotype-phenotype associations, mechanisms of inherited disease, and cancer driver mutations. Millions of single nucleotide variants (SNVs) in human genomes are known and thousands are associated with disease. An estimated 21% of disease-associated amino acid substitutions corresponding to missense SNVs are located in protein sites of post-translational modifications (PTMs), chemical modifications of amino acids that extend protein function. ActiveDriverDB is a comprehensive human proteo-genomics database that annotates disease mutations and population variants through the lens of PTMs. We integrated >385,000 published PTM sites with ∼3.6 million substitutions from The Cancer Genome Atlas (TCGA), the ClinVar database of disease genes, and human genome sequencing projects. The database includes site-specific interaction networks of proteins, upstream enzymes such as kinases, and drugs targeting these enzymes. We also predicted network-rewiring impact of mutations by analyzing gains and losses of kinase-bound sequence motifs. ActiveDriverDB provides detailed visualization, filtering, browsing and searching options for studying PTM-associated mutations. Users can upload mutation datasets interactively and use our application programming interface in pipelines. Integrative analysis of mutations and PTMs may help decipher molecular mechanisms of phenotypes and disease, as exemplified by case studies of TP53, BRCA2 and VHL. The open-source database is available at

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