A central issue in molecular biology is understanding the regulatory mechanisms that control gene expression. The recent flood of genomic and postgenomic data opens the way for computational methods elucidating the key components that play a role in these mechanisms. One important consequence is the ability to recognize groups of genes that are co-expressed using microarray expression data. We then wish to identify in-silico putative transcription factor binding sites in the promoter regions of these gene, that might explain the coregulation, and hint at possible regulators. In this paper we describe a simple and fast, yet powerful, two stages approach to this task. Using a rigorous hypergeometric statistical analysis and a straightforward computational procedure we find small conserved sequence kernels. These are then stochastically expanded into PSSMs using an EM-like procedure. We demonstrate the utility and speed of our methods by applying them to several data sets from recent literature. We also compare these results with those of MEME when run on the same sets.
|Original language||American English|
|Title of host publication||Algorithms in Bioinformatics - First International Workshop, WABI 2001 Århus Denmark, August 28-31, 2001 Proceedings|
|Editors||Bernard M. E. Moret, Olivier Gascuel|
|Number of pages||16|
|State||Published - 2001|
|Event||1st International Workshop on Algorithms in Bioinformatics, WABI 2001 - Arhus, Denmark|
Duration: 28 Aug 2001 → 31 Aug 2001
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||1st International Workshop on Algorithms in Bioinformatics, WABI 2001|
|Period||28/08/01 → 31/08/01|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.