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Bioinformatics 2006 22(14):e314-e322; doi:10.1093/bioinformatics/btl229
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Identifying cycling genes by combining sequence homology and expression data

Yong Lu 1, Roni Rosenfeld 1 and Ziv Bar-Joseph 1,2,*

1 School of Computer Science, Carnegie Mellon University 5000 Forbes Ave. Pittsburgh PA, 15213
2 Department of Biology, Carnegie Mellon University 5000 Forbes Ave. Pittsburgh PA, 15213

*To whom correspondence should be addressed.

Motivation: The expression of genes during the cell division process has now been studied in many different species. An important goal of these studies is to identify the set of cycling genes. To date, this was done independently for each of the species studied. Due to noise and other data analysis problems, accurately deriving a set of cycling genes from expression data is a hard problem. This is especially true for some of the multicellular organisms, including humans.

Results: Here we present the first algorithm that combines microarray expression data from multiple species for identifying cycling genes. Our algorithm represents genes from multiple species as nodes in a graph. Edges between genes represent sequence similarity. Starting with the measured expression values for each species we use Belief Propagation to determine a posterior score for genes. This posterior is used to determine a new set of cycling genes for each species.

We applied our algorithm to improve the identification of the set of cell cycle genes in budding yeast and humans. As we show, by incorporating sequence similarity information we were able to obtain a more accurate set of genes compared to methods that rely on expression data alone. Our method was especially successful for the human dataset indicating that it can use a high quality dataset from one species to overcome noise problems in another.

Availability: C implementation is available from the supporting website: http://www.cs.cmu.edu/~lyongu/pub/cellcycle/

Contact: zivbj{at}cs.cmu.edu


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Cyclebase.org a comprehensive multi-organism online database of cell-cycle experiments
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[Abstract] [Full Text] [PDF]


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