Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge

Authors:
Wentao Zhao;Erchin Serpedin;Edward R. Dougherty
Affiliations:
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX;Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX;Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX and Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ
Venue:
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Year:
2009

Citing 6
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Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data

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Comparison of computational methods for the identification of cell cycle-regulated genes

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Detecting periodic patterns in unevenly spaced gene expression time series using Lomb--Scargle periodograms

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Spectral analysis of irregularly-sampled data: Paralleling the regularly-sampled data approaches

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Nonparametric spectral analysis with missing data via the EM algorithm

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Abstract

Based on time series gene expressions, cyclic genes can be recognized via spectral analysis and statistical periodicity detection tests. These cyclic genes are usually associated with cyclic biological processes, for example, cell cycle and circadian rhythm. The power of a scheme is practically measured by comparing the detected periodically expressed genes with experimentally verified genes participating in a cyclic process. However, in the above mentioned procedure the valuable prior knowledge only serves as an evaluation benchmark, and it is not fully exploited in the implementation of the algorithm. In addition, partial data sets are also disregarded due to their nonstationarity. This paper proposes a novel algorithm to identify cyclic-process-involved genes by integrating the prior knowledge with the gene expression analysis. The proposed algorithm is applied on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, respectively. Biological evidences are found to validate the roles of the discovered genes in cell cycle and circadian rhythm. Dendrograms are presented to cluster the identified genes and to reveal expression patterns. It is corroborated that the proposed novel identification scheme provides a valuable technique for unveiling pathways related to cyclic processes.