PSB 2002 Tutorial

PSB 2002

Functional Genomics in 3 Hours: A Practical Guide to Creating Your Own High-Throughput Pipeline

The massively parallel acquisition of RNA expression data is rapidly becoming streamlined and dropping in price. In the near future we can expect that biologists and clinicians in many institutions will be routinely measuring such data. Therefore analysis of these data sets to characterize biological systems, identify high-yield candidate genes/EST's for further biological investigation, or quantify a patient's health risks, to just name a few tasks, will become a standard part of the investigational armamentarium. Many algorithms have been developed to take RNA expression data sets and generate clusters that are putatively reflective of functional dependencies. These algorithms range in complexity from simple fold-difference calculations to comprehensive pair-wise comparisons and model construction. This tutorial is designed to teach the basics of the various bioinformatics methodologies available to analyze RNA expression data sets, yet will approach the subject from a practical standpoint, so that attendees can immediately put these algorithms to use.

Content Description (4 parts):

1. Review

• Nature and format of the expression data files generated by the two most common technologies will be described. Particular emphasis will be placed on the different characteristics of these measurement systems, including noise profiles, and how normalization of the data sets can be approached (and common mistakes).
• Typical flow of an investigation of the functional genomics of a biological domain going from hypothesis generation to hypothesis validation.
• Description of the most frequently used clustering techniques. Their strengths and weakness will be summarized. The questions for which each might be better suited will be addressed as well as reasonable approaches to the interpretation of results generated by these techniques.
• Review of several instances of the clustering techniques applied to various biological systems.

2. Questions and Answers

3. Example Analysis

4. Human Genome Project

Biographical Sketches:

Isaac (Zak) Kohane is the director of the Children's Hospital Informatics Program and Associate Professor of Pediatrics at Harvard Medical School. Dr. Kohane is leading multiple collaborations at Harvard Medical School and its hospital affiliates in the elucidation of regulatory networks of genes and the interaction between genotype and phenotype using a variety of bioinformatics techniques. Application domains he is currently involved in include tumorigenesis, neurodevelopment, neuro-endocrinology and transplantation biology. Dr. Kohane's research builds on his doctoral work in computer science on decision support and subsequent research in machine learning applied to biomedicine. Dr. Kohane has also led the development of cryptographic health identification systems and automated personal health records. He has published over 50 papers in biomedical informatics. Dr. Kohane has chaired several national meetings including the two most recent Spring Symposia on Artificial Intelligence in Medicine at Stanford University and the session on Linking Phenotype to Genotype at the Pacific Symposium on Biocomputing. He is also a founder of the Center for Outcomes and Policy Research at the Dana Farber Cancer Institute, founder and Associate Director for the Center for Genetic Epidemiology at Harvard Medical School. He is a Fellow of the American College of Medical Informatics and a Fellow of the Society for Pediatric Research. He is Associate Editor for Bioinformatics for the Journal of Biomedical Informatics and on the editorial board of the Journal of the American Medical Informatics Associations. Dr. Kohane is also a practicing pediatric endocrinologist at Children's Hospital in Boston.

Atul Butte, MD
Instructor in Pediatrics
Harvard Medical School
Fellow in Bioinformatics and Endocrinology
Children's Hospital, Boston

Atul Butte is currently on staff in the Children's Hospital Informatics Program, is a practicing pediatric endocrinology at Children's Hospital, Boston, and is an Instructor at Harvard Medical School. Dr. Butte received his undergraduate degree in Computer Science from Brown University in 1991, and worked in several stints as a software engineer at Apple Computer (on the System 7 team) and Microsoft Corporation (on the Excel team). He graduated from the Brown University School of Medicine in 1995, during which he worked as a research fellow at NIDDK through the Howard Hughes/NIH Research Scholars Program. He completed his residency in Pediatrics and Fellowship in Pediatric Endocrinology in 2001, both at Children's Hospital, Boston. During his research work under Dr. Isaac Kohane, he developed a novel methodology for analyzing large data sets of RNA expression, called Relevance Networks. This technique was published in the Proceedings of the National Academy of Science (2000, 97:12182) and Children's Hospital has applied for a patent for this process. Dr. Butte's recent awards include the 2001 American Association for Cancer Research / Pharmacia Scholar-In-Training Award and the 2001 Lawson Wilkins Pediatric Endocrine Society NovoNordisk Clinical Scholar Award. Dr. Butte's research is supported by grants from NIDDK, NHLBI, NINDS, NLM, the Endocrine Fellows Foundation, the Genentech Center for Clinical Research and Education, the Lawson Wilkins Pediatric Endocrinology Society, and Merck.