DES-Mutation: System for Exploring Links of Mutations and Diseases

Vasiliki Kordopati, Adil Salhi, Rozaimi Razali, Aleksandar Radovanovic, Faroug Tifratene, Mahmut Uludag, Yu Li, Ameerah Bokhari, Ahdab AlSaieedi, Arwa A. Bin Res, Christophe Marc Van Neste, Magbubah Essack, Vladimir B. Bajic

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


During cellular division DNA replicates and this process is the basis for passing genetic information to the next generation. However, the DNA copy process sometimes produces a copy that is not perfect, that is, one with mutations. The collection of all such mutations in the DNA copy of an organism makes it unique and determines the organism's phenotype. However, mutations are often the cause of diseases. Thus, it is useful to have the capability to explore links between mutations and disease. We approached this problem by analyzing a vast amount of published information linking mutations to disease states. Based on such information, we developed the DES-Mutation knowledgebase which allows for exploration of not only mutation-disease links, but also links between mutations and concepts from 27 topic-specific dictionaries such as human genes/proteins, toxins, pathogens, etc. This allows for a more detailed insight into mutation-disease links and context. On a sample of 600 mutation-disease associations predicted and curated, our system achieves precision of 72.83%. To demonstrate the utility of DES-Mutation, we provide case studies related to known or potentially novel information involving disease mutations. To our knowledge, this is the first mutation-disease knowledgebase dedicated to the exploration of this topic through text-mining and data-mining of different mutation types and their associations with terms from multiple thematic dictionaries.
Original languageEnglish (US)
JournalScientific Reports
Issue number1
StatePublished - Sep 6 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): FCC/1/1976-02, BAS/1/1606-01-01
Acknowledgements: The computational analysis for this study was performed on Dragon and Snapdragon compute clusters of the Computational Bioscience Research Center (CBRC) at King Abdullah University ’of Science and Technology (KAUST). This work has been supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No FCC/1/1976-02, and KAUST Base Research Fund (BAS/1/1606-01-01) to VBB.


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