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

摘要(生物信息学部分) UAB精确动物建模中心(C-PAM)的目标是有效地分析和建模 被确认为罕见疾病计划一部分的变异，以提供明确的诊断，评估特定的变异 影响，并支持治疗靶点识别。C-PAM将通过应用 计算、细胞和临床方法，以协助生成有信息的动物模型。作为一名 作为该中心的组成部分，C-PAM生物信息学科(BIS)将提供计算工具、方法、 以及支持各种必要的可视化、探索和分析用例集的分析。它 支持具有不同技能和兴趣的内部和外部C-PAM用户，并支持相关用例 人类和动物模型数据以及它们之间的交集。通过定制现有软件 基础设施、计算方法和分析能力，国际清算银行将提供C-PAM 计算框架(门户、存储库和分析)。 国际清算银行将：支持关于选择变种进行建模的决策(阶段I、II)、预测表型 结果(第三阶段)，收集和传播动物模型生成过程(第四、第五阶段)的数据； 与C-PAM部门和生物医学社区(阶段VI、V)和总体共享数据和方法 支持C-PAM的影响评估。面向外的C-PAM门户(AIM 1)将提供数据访问 和分析以及整个中心的统一视图，包括对中心的持有量和访问权限的核算 到数据和工具。我们的数据模式完全是数据驱动的，数据高度浓缩，允许定制 以及在基础数据源更新时按需更新。此外，它还旨在支持 C-PAM的自然工作流，例如通过向用户提供添加备注的能力 和策展，确保知识不会被外部记事本或应用程序丢失。还在现有基础上进行扩建 功能(目标2)，门户将集成一套多样化和分布式的工具和数据存储，以支持 模式生物体研究变种的考虑、优先排序和选择。这套 这些功能将支持用户预测和理解基因的表型结果的能力 C-PAM产生的扰动。最后，国际清算银行将支持和支持尖端计算研究 选择新的或改变用途的药物目标并确定其优先次序，以便作出治疗决策(目标3)。 这些创新的机器学习和其他计算方法的应用将 支持全面的变异损害和药物预测、临床前假设检验和优先排序。 在所有这些目标中，国际清算银行将支持不同利益攸关方的需求，包括临床医生， 患者组，以及细胞、分子和计算生物学家。通过概述的方法，国际清算银行 将支持C-PAM目标，即提供可用于帮助患者或 他们的临床医生确定变种的致病性或帮助确定可能的治疗方法。

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.