Dr. Sumeet Dua

Max P. and Robbie L. Watson Eminent Scholar Chair

  • Full Screen
  • Wide Screen
  • Narrow Screen
  • Increase font size
  • Default font size
  • Decrease font size

Alan E. Alex (2009)

E-mail Print PDF

An Integrated Approach for Identification of Cell-cyclic Genes in the Saccharomyces Cerevisiae

The identification of genes expressed in a cell-cycle-specific periodical manner is important and has recently attracted significant interest. DNA microarrays have been widely used to study the cell cycle transcription program in model organisms. A number of computational methods, ranging from magnitude dependent methods to magnitude independent methods have been widely used on the Saccharomyces cerevisiae data namely - Alpha, cdc15, and cdc28 for the correct identification of periodically expressed (i.e., cell-cyclic) genes. However, the identification of periodically expressed genes by microarray gene analysis is complicated by both synchronization loss and by the presence of noise, and, therefore, remains a challenge.

In this thesis, we propose a novel technique for the integrated analysis of several synchronization experiments fused with a unique gene ranking scheme that is exploited to identify periodically expressed genes in the budding yeast (i.e., Saccharomyces cerevisiae) genome. In order to find a more representative subset of features based on periodicity, feature vectors for the three integrated synchronization experiments were extracted using the fourth order of moments and were ranked based on the statistical measure of standard deviation, used to measure the regulation of genes. The evolved feature space that carried discriminatory properties is then evaluated using a two-fold validation scheme. The two-fold validation scheme is used to list the cell-cyclic genes by comparing them with periodically expressed genes previously identified both by traditional methods and by the Spellman et al. method. Furthermore, the significance of the genes identified is investigated using the MIPS (Munich Information Center for Protein Sequences) database.



You are here: Research Student Thesis