UAB Pilot Center for Precision Animal Modeling (C-PAM) - Bioinformatics Section

UAB 精密动物建模试点中心 (C-PAM) - 生物信息学部分

• 批准号: 10260617
• 负责人: Elizabeth A Worthey
• 金额: $ 39.47万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10260617
• 关键词: Accounting; Animal Model; Bioinformatics; Catalogs; Clinical; Clinical Research; Code; Collaborations; Communities; Computer Analysis; Computer software; Computing Methodologies; Custom; Data; Data Analytics; Data Science; Data Set; Data Sources; Data Store; Decision Making; Deposition; Development; Diagnosis; Disease; Drug Targeting; Ensure; Etiology; Evaluation; Generations; Genes; Genetic; Genotype; Goals; Human; Impact evaluation; Infrastructure; Knowledge; Life Cycle Stages; Link; Machine Learning; Methodology; Methods; Modeling; Molecular; Molecular Computations; Outcome; Output; Pathogenicity; Patients; Pharmaceutical Preparations; Phase; Phenotype; Physiological; Process; Psychological Transfer; Quality Control; Rare Diseases; Research; Research Personnel; Research Support; Sampling; Standardization; Testing; Therapeutic; Update; Variant; Visualization; animal data; computer framework; computerized tools; cost effective; data access; data modeling; data repository; data sharing; data tools; data warehouse; design; drug candidate; drug repurposing; flexibility; human model; innovation; interest; new therapeutic target; novel; pre-clinical; programs; quality assurance; repository; software development; software infrastructure; targeted treatment; therapeutic candidate; therapeutic target; tool; user-friendly

ABSTRACT (BIOINFORMATICS SECTION) The goal of the UAB Center for Precision Animal Modeling (C-PAM) is efficient analysis and modeling of variants identified as part of rare disease programs to provide a definitive diagnosis, evaluate specific variant impact, and support therapeutic target identification. C-PAM will accomplish this through application of computational, cellular, and clinical methods to assist in the generation of informative animal models. As a component of this Center, the C-PAM Bioinformatics Section (BIS) will provide the computational tools, methods, and analyses supporting the necessary and diverse set of visualization, exploration, and analysis use cases. It supports internal and external C-PAM users with varying skillsets and interests and supports use cases related to both human and animal model data and the intersection between them. By tailoring existing software infrastructure, computational approaches, and analytical capabilities, the BIS will provide the C-PAM computational framework (portal, repository and analyses). The BIS will: support decisions regarding selection of variants to model (Phases I, II), predict phenotypic outcomes (Phase III), collect and disseminate data from the animal model generation process (Phases IV, V), share data and methods with C-PAM sections and the biomedical community (Phases VI, V), and overall support impact evaluation for C-PAM. The outward facing C-PAM portal (Aim 1) will provide data access and analytics and a unifying view of the entire Center including an accounting of the Center's holdings and access to data and tools. Our data schema is completely data driven and highly data condensing, allowing customization and on-demand updates as underlying data sources update. Additionally, it is designed to support the natural workflow of C-PAM, for example by providing users with the ability to add notes and curations, ensuring knowledge is not lost to external notepads or apps. Also building out existing capabilities (Aim 2), the portal will integrate a diverse and distributed set of tools and data stores supporting consideration, prioritization, and selection of variants for model organism study. This suite of features will support the user's ability to predict and understand phenotypic outcomes of the genetic perturbations generated by C-PAM. Finally, the BIS will support and house cutting-edge computational research for selection and prioritization of novel or repurposed drug targets for therapeutic decision-making (Aim 3). Application of these innovative machine learning and other computational methodologies will support comprehensive variant damage and drug prediction preclinical hypothesis testing and prioritization. Across all of these aims the BIS will support the needs of diverse stakeholders, including clinicians, patient groups, and cellular, molecular, and computational biologists. Through the approaches outlined, the BIS will support the C-PAM goals of providing efficient and cost-effective models that can be used to help patients or their clinicians determine the pathogenicity of a variant or aid in identification of possible treatments.

摘要（生物信息学部分）