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A High Productivity/Low Maintenance Approach to High-performance Computation for Biomedicine: Four Case Studies

Affiliations of the authors: Department of Computer Science (NC, MS), Center for Medical Informatics (MVO, K-HC, PLM), Department of Genetics (HZhao, KWh); Department of Molecular Biophysics and Biochemistry (MG, KWi); Department of Molecular, Cellular, and Developmental Biology (PLM); Department of Statistics (JC); Department of Epidemiology and Public Health (HZhao, BW, HZhan); Department of Ecology and Evolutionary Biology (SR); and W. M. Keck Biotechnology Resource Laboratory (KWi), Yale University, New Haven, CT.

Correspondence and reprints: Nicholas Carriero, Department of Computer Science, Yale University, New Haven, CT 06520-8285; e-mail: <carriero-nicholas{at}yale.edu>.

Received for publication: 03/09/04; accepted for publication: 08/05/04.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.