Bioinformatics Section

生物信息学组

• 批准号: 10259807
• 负责人: Aleksandar Milosavljevic
• 金额: $ 60.97万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10259807
• 关键词: Animal Model; Back; Bioinformatics; Biological Assay; Clinical; Communities; Data; Data Reporting; Disease model; Elements; Engineering; Evaluation; Genes; Genetic; Genomics; Human; Informatics; Infrastructure; Internet; Knowledge; Link; Modeling; Molecular; Multiomic Data; Phenotype; Process; Production; Publications; Research Personnel; Resources; Role; Service delivery model; Services; System; Technology; Time; Variant; bioinformatics infrastructure; data integration; data modeling; data portal; digital; experience; experimental study; fly; genetic manipulation; genetic testing; genetic variant; genome sequencing; human disease; information model; insight; interest; pre-clinical; precision medicine; prospective; repository; screening; tool; web portal; web site

ABSTRACT Model organisms are invaluable for understanding the role of genes in human disease and will be key to assessing the impact of millions of specific genetic variants uncovered by high-throughput genome sequencing. The modeling of specific genetic variants is predicated on model organisms engineered using precisely targeted genetic manipulations, careful and accurate variant annotation, and precise phenotyping. The modeling will also require advanced informatics capabilities to integrate variant information across species, support variant nomination and assessment, track model production and associated experimental data via a LIMS system, and to disseminate the information and engage the community via a web portal. Variant information from model organisms may provide insight into the molecular mechanism underlying a phenotype of interest, inform the specific genetic targeting strategy, and influence choice of model organism. As variant annotation resources continue to add information and new resources come online, informatics workflows must be able to accommodate and incorporate them into the model production process. The Bioinformatics Section of the BCM Center for Precision Medicine Modeling will develop these informatics capabilities by leveraging our extensive experience in multi-omic data integration and coordination, cross-species phenotype matching via the MARRVEL system, extension of our KOMP2 LIMS system implementation, and by leveraging key elements of the ClinGen Resource, an FDA-recognized repository for genomic information that we co-developed. By linking human and model information within integrated workflows, the infrastructure will facilitate precision modeling in one direction, and in the other direction will feed model-derived information and knowledge back to the community. By integrating variant resources and providing mechanisms for variant evaluation and information sharing, the Pre- Clinical/Co-Clinical and Disease Modeling Unit will more efficiently develop and evaluate model organisms. Bioinformatics infrastructure will support the variant evaluation process through final variant selection and presentation to the Steering Committee.

摘要 模式生物对于理解基因在人类疾病中的作用是非常宝贵的， 评估高通量基因组测序发现的数百万种特定遗传变异的影响。 特定遗传变异的建模是基于使用精确靶向的基因工程设计的模型生物。 基因操作、仔细和准确的变异注释以及精确的表型分析。建模还将 需要先进的信息学能力，以整合跨物种的变异信息，支持变异 提名和评估，通过LIMS系统跟踪模型生产和相关实验数据，以及 透过入门网站发放有关资料及鼓励市民参与。来自模型的变体信息 生物体可以提供对感兴趣的表型的分子机制的洞察， 特异性遗传靶向策略，并影响模式生物的选择。作为变体注释资源 继续添加信息和新资源上线，信息学工作流必须能够 并将其纳入模型生产过程。生物信息学部 精准医学建模中心将利用我们广泛的 多组学数据整合和协调经验，通过MARRVEL进行跨物种表型匹配 系统，我们的KOMP 2 LIMS系统实施的扩展，并通过利用ClinGen的关键要素 资源，一个FDA认可的基因组信息库，我们共同开发。通过连接人类和 集成工作流程中的模型信息，基础设施将促进在一个方向上的精确建模， 而在另一个方向上，将把模型导出的信息和知识反馈给社区。通过 整合变异资源，提供变异评价和信息共享机制， 临床/协同临床和疾病建模股将更有效地开发和评估模型生物。 生物信息学基础设施将通过最终变异选择支持变异评估过程， 向指导委员会介绍。