From promoter sequence to expression: a probabilistic framework
Proceedings of the sixth annual international conference on Computational biology
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A considerable fraction of yeast gene promoters are bound by multiple transcription factors. To study the combinatorial interactions of multiple transcription factors is thus important in understanding gene regulation. In this paper, we propose a computational method to identify the co-regulated gene groups and regulatory programs of multiple transcription factors from protein-DNA binding and gene expression data. The key concept is to characterize a regulatory program in terms of two properties of individual transcription factors: the function of a regulator as an activator or a repressor, and its direction of effectiveness as necessary or sufficient. We apply a greedy algorithm to find the regulatory models which optimally fit the data. Empirical analysis indicates the inferred regulatory models agree with the known combinatorial interactions between regulators and are robust against the settings of various free parameters.