• About
    • Login
    View Item 
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDepartmentThis CollectionBy Issue DateAuthorsTitlesSubjectsDepartment

    My Account

    LoginRegister

    Machine-Learning-Based Interpretation of Rare Disease Variants Leveraging Genomics and Computational Structural Biology

    Mukherjee, Souhrid
    0000-0002-8355-3000
    : http://hdl.handle.net/1803/17539
    : 2022-06-20

    Abstract

    Rare genetic diseases affect more than 300 million people around the world; however, the causative genes and variants have not been identified for most. The Undiagnosed Diseases Network (UDN) was established to help elucidate the molecular mechanisms underlying rare and undiagnosed diseases. Although this approach has yielded much success, more than half of all UDN cases remain undiagnosed. During my doctoral dissertation I have collaborated extensively with the UDN to help resolve rare disease mechanisms by developing computational tools and techniques for rare variant interpretation. My hypothesis was that in some of the unresolved UDN cases, the phenotypes resulted from effects of rare variants in more than one gene, and I have designed a machine learning classifier (DiGePred) that accurately predicts digenic disease-related gene pairs in individuals suffering from rare diseases. Several novel candidate digenic gene pairs have been identified. Subsequently, I devised a “Personalized Structural Biology” approach to predict the molecular mechanisms underlying diseases caused by individual rare missense variants. I analyzed a couple of proximal rare de novo variants (V469L, V471L) in the potassium ion channel KCNC2 (Kv3.2), suspected to cause developmental epileptic encephalopathy (DEE)-like symptoms in two patients. The two proximal variants were found to have drastically different molecular effects, with V471L being a gain-of-function, while the V469L was a loss-of-function, and I was able to predict heterogenous mechanisms associated with the two variants, using computational structural biology and molecular dynamics (MD) simulations. My goal for my work during my PhD was to study rare diseases and variants, and develop computational methods that facilitate discovery of rare disease molecular mechanisms. This would lead to an improved development of various intervention strategies for individuals suffering from rare diseases.
    Show full item record

    Files in this item

    Icon
    Name:
    MUKHERJEE-DISSERTATION-2022.pdf
    Size:
    9.669Mb
    Format:
    PDF
    View/Open
    Name:
    Dataset D1.csv
    Size:
    9.547Kb
    Format:
    application/
    View/Open
    Name:
    Dataset D2.csv
    Size:
    7.542Kb
    Format:
    application/
    View/Open
    Name:
    Dataset D3.csv
    Size:
    15.64Mb
    Format:
    application/
    View/Open
    Icon
    Name:
    v469l_bottomview(V4).mp4
    Size:
    16.44Mb
    Format:
    MPEG-4 video
    View/Open
    Icon
    Name:
    v469l_sideview(V3).mp4
    Size:
    13.90Mb
    Format:
    MPEG-4 video
    View/Open
    Icon
    Name:
    v471l_bottomview(V6).mp4
    Size:
    16.49Mb
    Format:
    MPEG-4 video
    View/Open
    Icon
    Name:
    v471l_sideview(V5).mp4
    Size:
    12.92Mb
    Format:
    MPEG-4 video
    View/Open
    Icon
    Name:
    wt_bottomview(V2).mp4
    Size:
    16.61Mb
    Format:
    MPEG-4 video
    View/Open
    Icon
    Name:
    wt_sideview(V1).mp4
    Size:
    12.71Mb
    Format:
    MPEG-4 video
    View/Open
    Name:
    Mukherjee-Souhrid-doctoral-dis ...
    Size:
    12.28Mb
    Format:
    Microsoft Word 2007
    View/Open
    Name:
    Mukherjee-Souhrid-doctoral-dis ...
    Size:
    12.38Mb
    Format:
    Microsoft Word 2007
    View/Open
    Name:
    Mukherjee-Souhrid-doctoral-dis ...
    Size:
    12.38Mb
    Format:
    Microsoft Word 2007
    View/Open

    This item appears in the following collection(s):

    • Electronic Theses and Dissertations

    Connect with Vanderbilt Libraries

    Your Vanderbilt

    • Alumni
    • Current Students
    • Faculty & Staff
    • International Students
    • Media
    • Parents & Family
    • Prospective Students
    • Researchers
    • Sports Fans
    • Visitors & Neighbors

    Support the Jean and Alexander Heard Libraries

    Support the Library...Give Now

    Gifts to the Libraries support the learning and research needs of the entire Vanderbilt community. Learn more about giving to the Libraries.

    Become a Friend of the Libraries

    Quick Links

    • Hours
    • About
    • Employment
    • Staff Directory
    • Accessibility Services
    • Contact
    • Vanderbilt Home
    • Privacy Policy