Computational Data Analysis Core

总结 在生物医学研究中，我们正在目睹一个根本性的转变，从一门专注于功能的科学， 单分子或通往信息丰富的数据科学的途径，分析生物系统及其 行为作为整体。这种被称为系统生物学的方法整合了来自各个复杂程度的数据 通过各种高通量方法测量，使用先进的计算方法来研究 相互作用的生物成分的网络决定了生命系统的性质和活动。与 过去十年的技术进步，基因组学，表观遗传学， 转录组学，蛋白质组学和代谢组学，现在被纳入日常的方法， 生物学研究者这些高通量技术提供了巨大的机会;然而，它们也构成了 巨大的挑战，因为它们快速生成大量不同的数据，分析变得更加复杂。 复杂.大多数生物学研究人员在数据分析方法方面没有足够的培训， 经常无法使用适当的数据库或软件来加快分析。因此，关键是 为研究人员提供高质量的先进数据分析服务，并进行数据分析培训 资源，以促进分析和解释数据。 在第二阶段的申请中，我们建议建立一个新的核心，即计算数据分析核心 （CDAC），以支持新项目负责人和更大的COBRE社区的大数据分析需求。 提出了三个目标：目标1：建立标准化的先进生物信息学服务， 高通量组学数据，包括但不限于RNA-Seq、微生物组和ChIP测序。目标二： 为COBRE和其他生物医学研究人员提供全面的高通量支持 实验设计、生物信息学数据分析、技术支持和咨询。目标3：教育教师， 工作人员，博士后研究员和研究生在计算数据分析技术。处理大 对大多数调查人员来说，数据仍然是一个挑战，在各种工具和分析管道方面需要进行足够的培训。 对用户来说是至关重要的。核心将开发和提供实践培训讲习班， 研究人员，学生和博士后学习数据分析的基本步骤。教育活动 因此，核心计划将通过向我们的学员提供生物信息学方面的市场技能，帮助他们发展职业生涯 数据分析总体而言，CDAC将提供专门的最先进和准确的高通量数据分析 为研究人员提供服务和培训，这将大大加强HPI COBRE的整体研究。

SUMMARY In biomedical research, we are witnessing a fundamental transition from a science focused on the function of single molecules or pathways to information-rich data science that analyzes biological systems and their behavior as a whole. This approach, known as Systems Biology, integrates data from all levels of complexity measured by various high-throughput approaches using advanced computational methods to study how networks of interacting biological components determine the properties and activities of living systems. With technological advances over the last decade, the ‘Omics’ technologies such as genomics, epigenetics, transcriptomics, proteomics, and metabolomics, are now incorporated into the everyday methodology of biological researchers. These high-throughput technologies offer a great opportunity; however, they also pose great challenges as they rapidly generate large amounts of diverse data and the analysis becomes more complex. Most biological researchers do not have adequate training in data analysis approaches and frequently lack access to appropriate databases or software to expedite analysis. Therefore, it is critical to provide the investigators with high-quality advanced data analysis services as well as training in data analysis resources to facilitate analysis and interpreting the data. In this Phase 2 application, we are proposing to establish a new Core, Computational Data Analysis Core (CDAC), to support the big data analysis needs of the new project leaders and the larger COBRE community. Three aims are proposed: Aim 1: Establish standardized advanced bioinformatics services for the analysis of high-throughput Omics data, including but not limited to RNA-Seq, microbiome, and ChIP-Sequencing. Aim 2: Provide the COBRE and other biomedical researchers with support on comprehensive high-throughput experimental design, bioinformatics data analysis, technical support, and consultation. Aim 3: Educate faculty, staff, postdoctoral fellows, and graduate students in computational data analysis technologies. Handling of big data remains a challenge for most investigators, and adequate training in various tools and analysis pipelines is critically required for users. The Core will develop and offer hands-on training workshops to help investigators, students, and postdocs to learn the fundamental steps of data analysis. Educational activities of the Core will thus help our trainees advance their careers by providing them marketable skills in bioinformatics data analysis. Overall, CDAC will provide dedicated state-of-the-art and accurate high-throughput data analysis services and training to the investigators, which will significantly enhance the overall research of HPI COBRE.