11 months ago

2002 Scientific Report

  • Text
  • Institute
  • Report
  • Tumors
  • Protein
  • Signaling
  • Michigan
  • Molecular
  • Proteins
  • Laboratory

External Collaborators

External Collaborators Jiahuai Han, Scripps Research Institute, San Diego, California Stephen Leppla, National Institute of Dental and Craniofacial Research, Bethesda, Maryland Robert Liddington, Burnham Institute, La Jolla, California Angel Nebreda, European Molecular Biology Laboratory, Heidelberg, Germany David Waugh, National Cancer Institute, Bethesda, Maryland Publications Bodart, Jean-François, Arun P. Chopra, Xudong Liang, and Nicholas S. Duesbery. 2002. Anthrax, MEK, and cancer. Cell Cycle 1(1): 10–15. Bodart, Jean-François, Davina V. Gutierrez, James H. Resau, Bree D. Buckner, Angel R. Nebreda, and Nicholas S. Duesbery. 2002. Characterization of MPF and MAPK activities during meiotic maturation of Xenopus tropicalis oocytes. Developmental Biology 245: 348–361. Koo, Han-Mo, Nicholas S. Duesbery, and George F. Vande Woude. 2002. Anthrax toxins, mitogenactivated protein kinase pathway, and melanoma treatment. Directions in Science 1: 123–126. Koo, Han-Mo, Matt VanBrocklin, Mary Jane McWilliams, Stephan H. Leppla, Nicholas S. Duesbery, and George F. Vande Woude. 2002. Apoptosis and melanogenesis in human melanoma cells induced by anthrax lethal factor inactivation of mitogen-activated protein kinase kinase. Proceedings of the National Academy of Sciences USA 99(5): 3052–3057. From left to right: Chopra, Bodart, Liang, Boone, Douglas, Duesbery 23

Bioinformatics Core 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. He did his postdoctoral work in the laboratory of George Vande Woude and became a Bioinformatics Scientist at VARI in June 2001. Laboratory Members Staff Ed Dere, B.S., B.Eng. Student Joe Crawley Research Projects As high-throughput biotechnologies such as DNA sequencing, gene expression microarray, and genotyping become more accessible to researchers, analysis of the data produced by these technologies becomes increasingly difficult. A relatively new field, termed bioinformatics, has emerged to store, distribute, integrate, and analyze this flood of biological data. Bioinformatics is a field that encompasses aspects of several disciplines, including information technology, computer science, statistics, and molecular biology/genetics. The bioinformatics program at VARI focuses on using a computational approach 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 VARI and as part of external collaborations. Assembled DNA sequence information for humans and mice has recently become available. To allow investigators at VARI to take advantage of this knowledge, we have downloaded the Ensembl version of the public human sequence database. In addition, we have several subscriptions to the Celera human and mouse databases. As sequence annotations are constantly being updated by the European Bioinformatics Institute, the National Center for Biological Information, and other institutes, we collect the sequence information from the various sources and summarize and distribute the results. In addition, we constantly monitor public gene sequence databases to ensure that as more gene sequences have cellular functions attributed to them, this information is available to our researchers. We also have active areas of research in the analysis of DNA microarray data. The DNA microarray technology allows the measurement of expression levels for tens of thousands of genes in a single experiment. To help determine gene expression values that change in a significant way between two sample groups (i.e., normal tissue versus tumor tissue), we have developed several programs to perform specialized statistical analysis on microarray data sets. Of special interest is a new technology we have developed to identify tumor cell chromosomal abnormalities from gene expression microarray data. This technique organizes genes by their genome mapping location and then scans for genomic regions that contain a disproportionate number of genes that show either increased or decreased expression (Figure 1). We have termed this analysis comparative genomic microarray analysis, or CGMA, as regional gene expression biases often indicate chromosomal losses or gains. We hope to develop this technology further to allow more in-depth analysis of chromosomal changes in cancer cells and to identify candidate genes whose expression changes most in regions of frequent cytogenetic change. Because many types of data analysis are computationally intensive, we are developing an infrastructure (as part of a collaboration) that will allow more-sophisticated computational analysis. This infrastructure, called cluster or grid computing, distributes a large computational workloads over many low-cost computers. Following completion of the 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. 24

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