NDLI: From promoter sequence to expression: a probabilistic framework

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From promoter sequence to expression: a probabilistic framework

Content Provider	ACM Digital Library
Author	Barash, Yoseph Segal, Eran Friedman, Nir Simon, Itamar Koller, Daphne
Abstract	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 that the learned model predicts expression from sequence, and that it identifies coherent co-regulated groups with significant transcription factor motifs. It also provides valuable biological insight into the domain via these co-regulated "modules" and the combinatorial regulation effects that govern their behavior.
Starting Page	263
Ending Page	272
Page Count	10
File Format	PDF
ISBN	1581134983
DOI	10.1145/565196.565231
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-04-18
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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