Indra Neil Sarkar, University of Vermont and Jessica Tenenbaum, Duke University
In 2005, Dr. Elias Zerhouni, Director of the National Institutes of Health (NIH), wrote “It is the responsibility of those of us involved in today’s biomedical research enterprise to translate the remarkable scientific innovations we are witnessing into health gains for the nation… At no other time has the need for a robust, bidirectional information flow between basic and translational scientists been so necessary.” Clearly evident in Dr. Zerhouni’s quote is the role biomedical informatics needs to play in facilitating translational medicine. American Medical Informatics Association (AMIA) now hosts the Joint Summits on Translational Science of which the Summit on Translational Bioinformatics is one of the two components. This tutorial is designed to teach the basics of the various types of molecular data and methodologies currently used in bioinformatics and genomics research, and how these can interface with clinical data. This tutorial will address the hypotheses one can start with by integrating molecular biological data with clinical data, and will show how to implement systems to address these hypotheses. The tutorial will cover real-world case-studies of how genetic, genomics, and proteomic data has been integrated with clinical data.
By the end of the tutorial, participants will be able to:
- Understand why biologists and clinicians use each measurement technology, and the advantages of each.
- List high-level requirements for an infrastructure relating research and clinical genetic and genomic data.
Outline of Topics:
- Basic understanding of various genome-scale measurement modalities: sequencing, polymorphisms, haplotypes, proteomics, gene expression, metabolomics, and others
- Crucial difference between genetic and genomic data
- Nature and format of expression, polymorphism, proteomics, and sequencing data
- Overview of the most commonly used structured vocabularies, taxonomies, and ontologies used in genomics research
- Description of the most frequently used analysis and clustering techniques
- How the genetic predisposition to disease is studied
- Use of genetic information across medical specialties
- How to find clinical genetic tests
- Genomic and clinical data to study patient disease-free status and survival
- How informatics can be used to identify potential drug targets
- Types of biomarkers
- Parallels between research methods in medical informatics and bioinformatics
- Relating clinical measurements with molecular measurements
Intended Audience: Academic faculty or professionals setting up bioinformatics facilities and/or relating these to clinical data repositories, or to data from General Clinical Research Centers or Clinical and Translational Science Awards; health information professionals responsible for clinical databases or data warehouses and tying these to researchers; informaticians, clinicians, and scientists interested in genetics, functional genomics, and microarray analysis; physicians interested in how medicine is advancing through the use of genomics and genetics; and students.
Content level: 20% Basic, 50% Intermediate, 30% Advanced