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Open Access Methodology article

Bayesian detection of periodic mRNA time profiles without use of training examples

Claes R Andersson1, Anders Isaksson2 and Mats G Gustafsson23*

Author Affiliations

1 The Linnaeus Centre for Bioinformatics, BMC, Uppsala University, Box 598, S-751 24 Uppsala, Sweden

2 Department of Genetics and Pathology, Rudbecklaboratoriet, Uppsala University, S-751 85 Uppsala, Sweden

3 Department of Engineering Sciences, Uppsala University, Box 528, S-751 20 Uppsala, Sweden

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BMC Bioinformatics 2006, 7:63  doi:10.1186/1471-2105-7-63

Published: 9 February 2006

Abstract

Background

Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time.

Results

A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented.

Conclusion

In comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.