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Demystifying Biomedical Big Data: A User’s Guide

Provided by Georgetown University (GeorgetownX)
Enroll - startsApr 24, 2019
Intermediate
See prerequisites

Whether you are a student, basic scientist, researcher, clinician, or librarian, this course is designed to help you understand, analyze, and interpret biomedical big data.

Start Date:Apr 24, 2019

Before you start

What you will learn

  • 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)

Week 1: Introduction and Overview of Bioinformatics Platforms and Resources

  • Introduction to the Course -Interview with Bioinformatics at Georgetown University Medical Center. Dr. Robert Clarke, Dean for Research, at Georgetown University Medical Center
  • Biomedical Informatics: Enabling Research and Health Care. Interview with Dr. Subha Madhavan, Director of the Innovation Center for Biomedical Informatics (ICBI) at Georgetown University
  • Biomedical Big Data: Enabling Personalized Medicine. Interview with Dr. John Marshall, Chief, Hematology and Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center internationally recognized medical oncologist


Week 2: Translational Research and Big Data

  • Translational Research
  • Translational Research Lecture, Part 1 Part I
  • Translational Research Lecture, Part 2 Part II
  • The Cancer Genome Atlas
  • The Cancer Genome Atlas Lecture
  • The Cancer Genome Atlas Demo
  • The Cancer Genome Atlas Exercise
  • Introduction to G-DOC
  • G-DOC Lecture
  • G-DOC Demo
  • G-DOC Exercise


Week 3: DNA and Big Data

  • DNA Copy Number
  • DNA Copy Number Lecture
  • DNA Copy Number Demo
  • DNA Copy Number Exercise
  • Genome Sequencing
  • Genome Sequencing Lecture, Part 1
  • Genome Sequencing Lecture, Part 2
  • Genome Sequencing Demo
  • Genome Sequencing Exercise


Week 4: RNA and Big Data

  • Gene Expression
  • Gene Expression Lecture
  • Gene Expression Demo
  • Gene Expression Exercise
  • MicroRNA
  • MicroRNA Lecture
  • MicroRNA Demo
  • MicroRNA Exercise


Week 5: Proteins and Big Data Part I

  • Proteomics
  • Protein Sequences Lecture
  • Protein Interactions Lecture
  • Mass Spec Proteomics, Lecture
  • Proteomics Exercise


Week 6: Proteins and Big Data Part II

  • Proteomics (Continued)
  • Data Sharing, Metadata, Data Formats
  • Ontologies
  • Proteomics Demo, Part 1
  • Proteomics Demo, Part 2
  • Proteomics Demo, Part 3
  • Proteomics Exercise


Week 7: Systems Biology and Big Data

  • Systems Biology
  • Systems Biology Lecture, Part 1
  • Systems Biology Lecture, Part 2
  • Systems Biology and Data Analysis Demo
  • Systems Biology Exercise


Week 8: Perspectives from the Field and Course Conclusion

  • Perspectives from the Field
  • Regulatory issues Issues related Related to biomedical Biomedical big Big dataData. Sheila Zimmet, JD, Senior Associate Vice President for Regulatory Affairs, and Ashley Carver, JD, Deputy Conflicts Officer and Regulatory Affairs Associate, Georgetown University Medical Center.
  • Enabling Everyone to Share and Use Public Datasets. Interview with Dr. Ben Busby, genomics Genomics outreach Outreach coordinator Coordinator at the National Center for Biotechnology Information (NCBI)
  • Interview withCancer Moonshot. Dr. Jerry Lee, Deputy Director, Center for Strategic Scientific Initiatives, Office of the Director, National Cancer Institute, National Institutes of Health

Overview

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.

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