Skip Navigation

This Article
Right arrow Full Text Freely available
Right arrow Print PDF (486K) Freely available
Right arrow Supplementary Material
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (20)
Right arrowRequest Permissions
Right arrow Commercial Re-use Guidelines
for Open Access NAR Content
Google Scholar
Right arrow Articles by Lu, X.
Right arrow Articles by Liu, J. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lu, X.
Right arrow Articles by Liu, J. S.
Related Collections
Right arrow Computational methods
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Published online 22 January 2004

Nucleic Acids Research, 2004, Vol. 32, No. 2 447-455
© 2004 Oxford University Press

Statistical resynchronization and Bayesian detection of periodically expressed genes

Xin Lu1, Wen Zhang1,2, Zhaohui S. Qin3, Kurt E. Kwast4 and Jun S. Liu*,1

1 Department of Statistics, Harvard University, Cambridge, MA 02138, USA, 2 Department of Biology, Kunming Medical College, Kunming 650031, China, 3 Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA and 4 Department of Molecular and Integrative Physiology, University of Illinois, Urbana, IL 61801, USA

*To whom correspondence should be addressed. Tel: +1 617 495 1600; Fax: +1 617 496 8057; Email: jliu{at}stat.harvard.edu
The authors wish it to be known that, in their opinion, the first two authors should be considered as joint First Authors

We propose a periodic–normal mixture (PNM) model to fit transcription profiles of periodically expressed (PE) genes in cell cycle microarray experiments. The model leads to a principled statistical estimation procedure that produces more accurate estimates of the mean cell cycle length and the gene expression periodicity than existing heuristic approaches. A central component of the proposed procedure is the resynchronization of the observed transcription profile of each PE gene according to the PNM with estimated periodicity parameters. By using a two-component mixture-Beta model to approximate the PNM fitting residuals, we employ an empirical Bayes method to detect PE genes. We estimate that about one-third of the genes in the genome of Saccharomyces cerevisiae are likely to be transcribed periodically, and identify 822 genes whose posterior probabilities of being PE are greater than 0.95. Among these 822 genes, 540 are also in the list of 800 genes detected by Spellman. Gene ontology annotation analysis shows that many of the 822 genes were involved in important cell cycle-related processes, functions and components. When matching the 822 resynchronized expression profiles of three independent experiments, little phase shifts were observed, indicating that the three synchronization methods might have brought cells to the same phase at the time of release.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
Proc. Natl. Acad. Sci. USAHome page
J. E. Lemieux, N. Gomez-Escobar, A. Feller, C. Carret, A. Amambua-Ngwa, R. Pinches, F. Day, S. A. Kyes, D. J. Conway, C. C. Holmes, et al.
Statistical estimation of cell-cycle progression and lineage commitment in Plasmodium falciparum reveals a homogeneous pattern of transcription in ex vivo culture
PNAS, May 5, 2009; 106(18): 7559 - 7564.
[Abstract] [Full Text] [PDF]


Home page
Mol. Cell. ProteomicsHome page
Y. Quan, Z.-L. Ji, X. Wang, A. M. Tartakoff, and T. Tao
Evolutionary and Transcriptional Analysis of Karyopherin {beta} Superfamily Proteins
Mol. Cell. Proteomics, July 1, 2008; 7(7): 1254 - 1269.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
M. E. Futschik and H. Herzel
Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis
Bioinformatics, April 15, 2008; 24(8): 1063 - 1069.
[Abstract] [Full Text] [PDF]


Home page
Proc. Natl. Acad. Sci. USAHome page
Z. Bar-Joseph, Z. Siegfried, M. Brandeis, B. Brors, Y. Lu, R. Eils, B. D. Dynlacht, and I. Simon
Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells
PNAS, January 22, 2008; 105(3): 955 - 960.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
N. P. Gauthier, M. E. Larsen, R. Wernersson, U. de Lichtenberg, L. J. Jensen, S. Brunak, and T. S. Jensen
Cyclebase.org a comprehensive multi-organism online database of cell-cycle experiments
Nucleic Acids Res., January 11, 2008; 36(suppl_1): D854 - D859.
[Abstract] [Full Text] [PDF]


Home page
Genes Dev.Home page
T. Pramila, W. Wu, S. Miles, W. S. Noble, and L. L. Breeden
The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle.
Genes & Dev., August 15, 2006; 20(16): 2266 - 2278.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
P. Qiu, Z. J. Wang, and K. J. R. Liu
Polynomial model approach for resynchronization analysis of cell-cycle gene expression data
Bioinformatics, April 15, 2006; 22(8): 959 - 966.
[Abstract] [Full Text] [PDF]


Home page
Proc. Natl. Acad. Sci. USAHome page
N. A. Heard, C. C. Holmes, D. A. Stephens, D. J. Hand, and G. Dimopoulos
Bayesian coclustering of Anopheles gene expression time series: Study of immune defense response to multiple experimental challenges
PNAS, November 22, 2005; 102(47): 16939 - 16944.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
U. de Lichtenberg, L. J. Jensen, A. Fausboll, T. S. Jensen, P. Bork, and S. Brunak
Comparison of computational methods for the identification of cell cycle-regulated genes
Bioinformatics, April 1, 2005; 21(7): 1164 - 1171.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.