Computational Data Analysis Core

摘要 在生物医学研究中，我们正在见证一场根本性的转变，从一门专注于 单分子或通向信息丰富的数据科学的路径，该科学分析生物系统及其 行为作为一个整体。这种方法被称为系统生物学，它集成了各种复杂程度的数据 通过使用高级计算方法的各种高吞吐量方法进行测量，以研究如何 相互作用的生物成分网络决定了生命系统的性质和活动。使用 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学中 生物研究人员。这些高吞吐量技术提供了一个很好的机会；然而，它们也带来了 巨大的挑战，因为他们快速生成大量不同的数据，并且分析变得更多 很复杂。大多数生物学研究人员没有接受过充分的数据分析方法和培训 经常无法访问适当的数据库或软件来加快分析。因此，至关重要的是 为调查人员提供高质量的高级数据分析服务以及数据分析培训 帮助分析和解释数据的资源。 在此第2阶段应用程序中，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：建立标准化的高级生物信息学服务，用于分析 高通量Omics数据，包括但不限于RNA-Seq、微生物组和芯片测序。目标2： 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：教育教职员工， 计算数据分析技术方面的工作人员、博士后研究员和研究生。BIG的处理 数据对大多数调查人员来说仍然是一个挑战，在各种工具和分析管道方面进行了充分的培训 对于用户来说是至关重要的。核心将开发和提供实践培训研讨会，以帮助 研究人员、学生和博士后学习数据分析的基本步骤。的教育活动 因此，核心将帮助我们的受训人员通过向他们提供生物信息学方面的市场技能来促进他们的职业生涯 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 为调查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.