Computational Biology and High Performance Computing 2000

Tutorial to be presented at Supercomputing 2000, Dallas TX, 6-10 November 2000.This work was supported by the Director, Office of Science, Office of Advanced Scientific computing Research, Mathematical, Information, and Computational Sciences Division of the U.S. Department of Energy under Contract No. DE-AC03-76SF0009

Computational Biology and High Performance Computing 2000

Tutorial to be presented at Supercomputing 2000, Dallas TX, 6-10 November 2000.This work was supported by the Director, Office of Science, Office of Advanced Scientific computing Research, Mathematical, Information, and Computational Sciences Division of the U.S. Department of Energy under Contract No. DE-AC03-76SF0009

Bioinformatics in the information age

There is a well-known story about the blind man examining the elephant: the part of the elephant examined determines his perception of the whole beast. Perhaps bioinformatics--the shotgun marriage between biology and mathematics, computer science, and engineering--is like an elephant that occupies a large chair in the scientific living room. Given the demand for and shortage of researchers with the computer skills to handle large volumes of biological data, where exactly does the bioinformatics elephant sit? There are probably many biologists who feel that a major product of this bioinformatics elephant is large piles of waste material. If you have tried to plow through Web sites and software packages in search of a specific tool for analyzing and collating large amounts of research data, you may well feel the same way. But there has been progress with major initiatives to develop more computing power, educate biologists about computers, increase funding, and set standards. For our purposes, bioinformatics is not simply a biologically inclined rehash of information theory (1) nor is it a hodgepodge of computer science techniques for building, updating, and accessing biological data. Rather bioinformatics incorporates both of these capabilities into a broad interdisciplinary science that involves both conceptual and practical tools for the understanding, generation, processing, and propagation of biological information. As such, bioinformatics is the sine qua non of 21st-century biology. Analyzing gene expression using cDNA microarrays immobilized on slides or other solid supports (gene chips) is set to revolutionize biology and medicine and, in so doing, generate vast quantities of data that have to be accurately interpreted (Fig. 1). As discussed at a meeting a few months ago (Microarray Algorithms and Statistical Analysis: Methods and Standards; Tahoe City, California; 9-12 November 1999), experiments with cDNA arrays must be subjected to quality control. Variables as simple as temperature and illumination differences across a microarray slide can alter readings. Between slides, additional variables add to the difficulty of comparison. For example, John Quackenbush (The Institute for Genomic Research) described the complexities associated with assuring quality control between microarray slides in a presentation both humorous and disquieting in which he demonstrated how air conditioning can affect sample readouts. Manfred Zorn (Lawrence Berkeley National Laboratory, LBNL), chair of a working group on standards, launched a preliminary effort to lay down definitions and standards for microarray analysis with particular emphasis on experimental design, measurement, and analysis documentation

Bioinformatics in the information age

There is a well-known story about the blind man examining the elephant: the part of the elephant examined determines his perception of the whole beast. Perhaps bioinformatics--the shotgun marriage between biology and mathematics, computer science, and engineering--is like an elephant that occupies a large chair in the scientific living room. Given the demand for and shortage of researchers with the computer skills to handle large volumes of biological data, where exactly does the bioinformatics elephant sit? There are probably many biologists who feel that a major product of this bioinformatics elephant is large piles of waste material. If you have tried to plow through Web sites and software packages in search of a specific tool for analyzing and collating large amounts of research data, you may well feel the same way. But there has been progress with major initiatives to develop more computing power, educate biologists about computers, increase funding, and set standards. For our purposes, bioinformatics is not simply a biologically inclined rehash of information theory (1) nor is it a hodgepodge of computer science techniques for building, updating, and accessing biological data. Rather bioinformatics incorporates both of these capabilities into a broad interdisciplinary science that involves both conceptual and practical tools for the understanding, generation, processing, and propagation of biological information. As such, bioinformatics is the sine qua non of 21st-century biology. Analyzing gene expression using cDNA microarrays immobilized on slides or other solid supports (gene chips) is set to revolutionize biology and medicine and, in so doing, generate vast quantities of data that have to be accurately interpreted (Fig. 1). As discussed at a meeting a few months ago (Microarray Algorithms and Statistical Analysis: Methods and Standards; Tahoe City, California; 9-12 November 1999), experiments with cDNA arrays must be subjected to quality control. Variables as simple as temperature and illumination differences across a microarray slide can alter readings. Between slides, additional variables add to the difficulty of comparison. For example, John Quackenbush (The Institute for Genomic Research) described the complexities associated with assuring quality control between microarray slides in a presentation both humorous and disquieting in which he demonstrated how air conditioning can affect sample readouts. Manfred Zorn (Lawrence Berkeley National Laboratory, LBNL), chair of a working group on standards, launched a preliminary effort to lay down definitions and standards for microarray analysis with particular emphasis on experimental design, measurement, and analysis documentation

Computational Biology and High Performance Computing 2000

The pace of extraordinary advances in molecular biology has accelerated in the past decade due in large part to discoveries coming from genome projects on human and model organisms. The advances in the genome project so far, happening well ahead of schedule and under budget, have exceeded any dreams by its protagonists, let alone formal expectations. Biologists expect the next phase of the genome project to be even more startling in terms of dramatic breakthroughs in our understanding of human biology, the biology of health and of disease. Only today can biologists begin to envision the necessary experimental, computational and theoretical steps necessary to exploit genome sequence information for its medical impact, its contribution to biotechnology and economic competitiveness, and its ultimate contribution to environmental quality. High performance computing has become one of the critical enabling technologies, which will help to translate this vision of future advances in biology into reality. Biologists are increasingly becoming aware of the potential of high performance computing. The goal of this tutorial is to introduce the exciting new developments in computational biology and genomics to the high performance computing community