Are we Overestimating the Number of Cell-Cycling Genes? The Impact of Background Models

MOTIVATION Periodic processes play fundamental roles in organisms. Prominent examples are the cell cycle and the circadian clock. Microarray array technology has enabled us to screen complete sets of transcripts for possible association with such fundamental periodic processes on a system-wide level. Frequently, quite large numbers of genes have been detected as periodically expressed. However, the small overlap between genes identified in different studies has cast some doubts on the reliability of the periodic expression detected. RESULTS In this study, comparative analysis suggests that the lacking agreement between different cell-cycle studies might be due to inadequate background models for the determination of significance. We demonstrate that the choice of background model has considerable impact on the statistical significance of periodic expression. For illustration, we reanalyzed two microarray studies of the yeast cell cycle. Our evaluation strongly indicates that the results of previous analyses might have been overoptimistic and that the use of more suitable background model promises to give more realistic results. AVAILABILITY R scripts are available on request from the corresponding author.

[1]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[2]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[3]  L. P. Zhao,et al.  Statistical modeling of large microarray data sets to identify stimulus-response profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Kerby Shedden,et al.  Analysis of cell-cycle gene expression in Saccharomyces cerevisiae using microarrays and multiple synchronization methods , 2002, Nucleic Acids Res..

[5]  Ronald W. Davis,et al.  Transcriptional regulation and function during the human cell cycle , 2001, Nature Genetics.

[6]  Zhaohui S. Qin,et al.  Statistical resynchronization and Bayesian detection of periodically expressed genes. , 2004, Nucleic acids research.

[7]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[8]  Kai-Florian Storch,et al.  Extensive and divergent circadian gene expression in liver and heart , 2002, Nature.

[9]  Ronald W. Davis,et al.  Systematic screen for human disease genes in yeast , 2002, Nature Genetics.

[10]  K. Shedden,et al.  Analysis of cell-cycle-specific gene expression in human cells as determined by microarrays and double-thymidine block synchronization , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Korbinian Strimmer,et al.  Identifying periodically expressed transcripts in microarray time series data , 2008, Bioinform..

[12]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[14]  Anders Berglund,et al.  A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription , 2003, Bioinform..

[15]  M. Futschik,et al.  Model selection and efficiency testing for normalization of cDNA microarray data , 2004, Genome Biology.

[16]  Elizabeth Bradley.  Analysis of time series , 2003 .

[17]  Peer Bork,et al.  Comparison of computational methods for the identification of cell cycle-regulated genes , 2005, Bioinform..

[18]  Matthias E. Futschik,et al.  Noise-robust Soft Clustering of Gene Expression Time-course Data , 2005, J. Bioinform. Comput. Biol..

[19]  M. Gerstein,et al.  Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. , 2004, Current opinion in microbiology.

[20]  Hanspeter Herzel,et al.  Promoter analysis of Mammalian clock controlled genes. , 2007, Genome informatics. International Conference on Genome Informatics.