Computational Data Analysis Core
- Institution
- UNIVERSITY OF NORTH DAKOTA, ND
- Principal investigator
- Junguk Hur
- Funding
- $65K
- Duration
- 2016–2026
Moonbase score
Still being scored
Abstract
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.
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