Understanding gene regulation is a critical step towards deciphering the complexities of transcription [1, 2]. Although microarray technology can reveal patterns of gene expression [3, 4], the regulation mechanism driving gene expression is still unknown in most cases [5]. A reasonable hypothesis is that similar gene promoters may imply the possibility that similar transcription factors can bind and be potentially functional determinants of gene regulation [2, 6-8]. Thus, two critical questions emerge: (i) what constitutes a set of coexpressed versus non-coexpressed genes, and (ii) how do we handle the fact that a gene may have multiple promoters [7, 9] corresponding to different transcription start sites?

The first question is important since the silent underlying assumption of regulation analysis is that genes that have the same regulation mechanism ought to respond in a similar manner under similar stimuli. The second question is very critical as well. Since it is known that genes can be co-expressed under certain conditions and not under others it clearly implies that alternative regulation mechanisms must be available to each gene, otherwise the problem would be trivial. We therefore computationally examine possible combinations of alternative promoters with the hope of clarifying the mechanisms characterizing regulation.

In this presentation, we will first discuss a novel methodology based on a multi-clustering approach [10-13] which, instead of simply clustering the entire data set, attempts to identify within the data a subset of highly coexpressed and non-coexpressed genes i.e. with a confidence level d identify those genes which are either co-expressed or non co-expressed. Subsequently, we consider whether some combinations from alternative promoter sequences for each gene [14] in this subset can disclose alternative regulation mechanisms. Under the assumption that the more similar the promoter sequences are, the more probable is that the corresponding genes are co-regulated [8, 15-17], we propose a novel index of promoter similarity that takes into account all possibilities of regulatory elements e.g. TFs, miRNAs [1, 18] to computationally represent the coregulation level between any two genes. Instead of scanning for common patterns e.g. conserved motifs [19, 20], TFBSs [21, 22] in promoter regions of coexpressed genes and then calculating the coregulation level between two genes [15-17], the measure here is estimated directly from their sequences [6, 8, 23, 24] regardless as to whether genes coexpressed or not. Using the proposed measure of promoter similarity, the pool of promoter sequences is hierarchically clustered [24, 25] with a similarity threshold calculated from the background set of sequences and mapped onto groups of coregulated genes. Clustering results are then rationalized in the context of expression under different experimental conditions to identify possible combinations of alternative promoters activated under a specific condition. Representative promoters in each cluster of coregulated genes now can be considered as the specific promoters for those genes involved in a specific regulation mechanism.

Consequently, our work aims at identifying the possibility that alternative promoters, thus regulation mechanisms, are activated under different conditions. Therefore, we wish to determine whether the combination of co-expression and co-regulation analyses can reveal condition-specific regulation mechanisms.

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