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2004 Scientific Report

Bioinformatics Special

Bioinformatics Special Program Kyle A. Furge, Ph.D. Dr. Furge received his Ph.D. in biochemistry from the Vanderbilt University School of Medicine in 2000. Prior to obtaining his degree, he worked as a software engineer at YSI Inc., where he wrote operating systems for embedded computer devices. Dr. Furge did his postdoctoral work in the laboratory of Dr. George Vande Woude and became a Bioinformatics Scientist at VARI in June of 2001. Staff Karl Dykema, B.A. Laboratory Members Student Dechrisha Bates Research Interests As high-throughput biotechnologies such as DNA sequencing, gene expression microarrays, and genotyping become more available to researchers, analysis of the data produced by these technologies becomes increasingly difficult. Computational disciplines such as bioinformatics and computational biology have recently emerged to help develop methods that assist in the storage, distribution, integration, and analysis of these data sets. The bioinformatics program at VARI is currently focused on using computational approaches to understand how cancer cells differ from normal cells at the molecular level. In addition, we assist in the analysis of large and small data sets that are generated both within the Institute and as part of larger international collaborations. Currently, we have a special focus on genomes, including DNA sequence data, gene expression microarray data, and cytogenetic data. The bioinformatics program at VARI is also part of the overall bioinformatics effort in the state of Michigan through the Michigan Center for Biological Information. To allow investigators at VARI to take advantage of the most recent DNA sequence information, we maintain a local mirror of the Ensembl version of the public human sequence database augmented with the JEMBOSS tool set. In addition, we support the use of the Informax data platform for sequence analysis. As sequence annotations are constantly being updated by the European Bioinformatics Institute, the National Center for Biological Information, and other institutes, we collect this information, summarize it, and distribute the results. Microarray technology allows us to measure expression levels for tens of thousands of genes in a single experiment. To handle the amount of gene expression data such experiments produce, we have expertise in several robust data analysis and statistical packages, including GeneSpring and BioConductor. BioConductor is a international research collaboration having a goal of providing access to a wide range of powerful statistical and graphical methods for the analysis of genomic data. Details can be found at . Of special interest to our group is integrating gene expression data with traditional cytogenetic data. The goal is to use computational approaches to identify candidate genes for which expression is altered the most within regions of frequent cytogenetic change. We are developing several types of algorithms that can both identify probable cytogenetic abnormalities from gene expression data and identify candidate genes within these abnormal regions (Fig. 1). Because many types of data analysis are computationally intensive, we are developing an infrastructure that will allow more-sophisticated computational analysis to be used. This infrastructure—termed cluster computing, or grid computing—distributes a large computational workload over many lowcost computers. Following analysis, a monitoring computer collects all of the data from the smaller computers and assembles the results. This type of computing is beneficial, as a relatively small group of low-cost computers can efficiently process a large computational workload. 26

Figure 1. A sliding-window algorithm for regional gene expression biases. The top panel shows gene expression values of tumor tissue relative to adjacent noncancerous tissue in a chromosomal region. In the middle panel, an exhaustive sliding-window algorithm identifies regions of more-significant downward gene expression bias (blue) or more-significant upward bias (red); white indicates regions of less significance. The size of the sliding window is varied and is smallest at the top to largest at the bottom. The lower panel summarizes the results and indicates where the most significant regional expression bias occurs, in this case in the far right chromosomal region. Recent Publications Gray, S.G., C.-N. Qian, K. Furge, X. Guo, and B.T. Teh. 2004. Microarray profiling of the effects of histone deacetylase inhibitors on gene expression in cancer cell lines. International Journal of Oncology 24(4): 773–795. Takahashi, Masayuki, Jun Sugimura, Ximing Yang, Nicholas J. Vogelzang, Bin S. Teh, Kyle A. Furge, and Bin T. Teh. 2003. Gene expression profiling of renal cell carcinoma and its implications in diagnosis, prognosis, and therapeutics. Advances in Cancer Research 89: 157–181. Takahashi, Masayuki, Ximing J. Yang, Jun Sugimura, Jesper Backdahl, Maria Tretiakova, Chao-Nan Qian, Steven G. Gray, Robert Knapp, John Anema, Richard Kahnoski, David Nicol, Nicholas J. Vogelzang, Kyle A. Furge, Hiroomi Kanayama, Susumu Kagawa, and Bin Tean Teh. 2003. Molecular subclassification of kidney cancer and the discovery of new diagnostic markers. Oncogene 22(43): 6810–6818. Yang, Ximing J., Jun Sugimura, Maria S. Tretiakova, Kyle Furge, Gregory Zagaja, Mitchell Sokoloff, Michael Pins, Raymond Bergan, David J. Grignon, Walter M. Stadler, Nicholas J. Vogelzang, and Bin Tean Teh. 2003. Gene expression profiling of renal medullary carcinoma. Cancer 100(5): 976–985. Crawley, Joseph J., and Furge, Kyle A. 2002. Identification of frequent cytogenetic aberrations in hepatocellular carcinoma using gene expression data. Genome Biology 3: 0075.1–0075.8. Furge, Kyle A., Ramsi Haddad, Jeremy Miller, Brian B. Haab, Jacqueline Schoumans, Bin T. Teh, Lonson L. Barr, and Craig P. Webb. 2002. Genomic profiling and cDNA microarray analysis of human colon adenocarcinoma and associated peritoneal metastases reveals consistent cytogenetic and transcriptional aberrations associated with progression of multiple metastases. Applied Genomics and Proteomics 1(2): 123–134. 27

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