%0 Journal Article
%T Identifying Genes Involved in Cyclic Processes by Combining Gene Expression Analysis and Prior Knowledge
%A Wentao Zhao
%A Erchin Serpedin
%A Edward R Dougherty
%J EURASIP Journal on Bioinformatics and Systems Biology
%D 2009
%I BioMed Central
%R 10.1155/2009/683463
%X The eukaryotic cell hosts several cyclic molecular processes, for example, cell cycle and circadian rhythm. The transcriptional events in these processes can be quantitatively observed by measuring the concentration of the messenger RNA (mRNA), which is transcribed from DNA and serves as the template for synthesizing the corresponding protein. To achieve this goal, the microarray experiments exploit high-throughput gene chips to snapshot genome-wide gene expressions sequentially at discrete time points. The sampled time series data present three main characteristics. First, most data sets present small sample size, for example, no more than 50 data points. Obtaining large sample size data sets is not financially affordable, and besides, in the long run the cell culture loses synchronization and the data become meaningless if they are sampled much later on. Second, the data might not be evenly sampled, and many time points could be missing. In order to capture critical events with minimal cost, biologists usually conduct microarray experiments and make measurements when these events happen. Third, the data are highly corrupted by experimental noise, and a robust stochastic analysis is desired.Based on time series data, various approaches have been proposed to identify periodically expressed genes, which are sometimes believed to be involved in the cell cycle. Assuming the cell cycle signal to be a simple sinusoid, Spellman et al. [1] and Whitfield et al. [2] performed Fourier transformations on the data sampled with different synchronization methods, Wichert et al. [3] applied the traditional periodogram and Fisher's test, while Ahdesm£¿ki et al. [4] implemented a robust periodicity test assuming non-Gaussian noise. In [5], Giurc¨£neanu explored the stochastic complexity of detecting periodically expressed genes by means of generalized Gaussian distributions. Alternatively, Luan and Li [6] employed guide genes and constructed cubic B-spline-based periodic functions for
%U http://bsb.eurasipjournals.com/content/2009/1/683463