Demystifying Biomedical Big Data: A User’s Guide

With the continuous generation of massive amounts of biomedical data on a daily basis, whether from research laboratories or clinical labs, we need to improve our ability to understand and analyze the data in order to take full advantage of its power in scientific discoveries and patient care. For non-bioinformaticians, “handling” big data remains a daunting task. This course was designed to facilitate the understanding, analysis, and interpretation of biomedical big data to those in the biomedical field with limited or no significant experience in bioinformatics. The goal of this course is to “demystify” the process of analyzing biomedical big data through a series of lectures and online hands-on training sessions and demos. You will learn how to use publicly available online resources and tools for genomic, transcriptomic, and proteomic data analysis, as well as other analytic tools and online resources. This course is funded by a research grant from the US National Institutes of Health (NIH)-Big Data to Knowledge (BD2K) Initiative. 3b:Tdec,

• Understand how biomedical data are being generated and processed
• Learn about various biomedical big data resources (e.g. TCGA, G-DOC, UNIPROT, etc.)
• Explore and analyze genomic, transcriptomic, and proteomic data using various online analysis tools
• Make sense of big data using systems biology resources and tools
• Appreciate the value of big data in biomedical research and clinical practice (e.g. enabling precision medicine)

• Background in biology is suggested (high school or undergraduate level). Review of relevant courses in biology and life sciences (e.g. genetics, genomics, DNA/RNA/Protein structure and function, transcription, and translation) is recommended, but not required.
• Courses to explore include Genomic Medicine Gets Personal (GeorgetownX), AP® Biology Part 1: The Cell (RiceX), AP® Biology Part 2: Genetics (RiceX), and Proteins: Biology's Workforce (RiceX).