From promoter sequence to expression: a probabilistic framework

We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene's promoter region, and the prediction of mRNA expression from combinations of gene regulation events in different settings. Our approach has several advantages. By learning promoter sequence motifs that are directly predictive of expression data, it can improve the identification of binding site patterns. It is also able to identify combinatorial regulation via interactions of different transcription factors. Finally, the general framework allows us to integrate additional data sources, including data from the recent binding localization assays. We demonstrate our approach on the cell cycle data of Spellman et al., combined with the binding localization information of Simon et al. We show t...

• Type: Conference
• Year: 2002
• Where: RECOMB
• Authors: Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
• Keywords: Computational Biology, Gene Regulation Events, RECOMB 2002, Significant Transcription Factor, Valuable Biological Insight