Understanding Transcription Factor Binding in Yeast
Author Information
Author(s): Andreas Beyer, Christopher Workman, Jens Hollunder, Dörte Radke, Ulrich Möller, Thomas Wilhelm, Trey Ideker
Primary Institution: University of California San Diego
Hypothesis
Can a probabilistic model reliably assign transcription factors to target genes using various lines of evidence?
Conclusion
The study presents a new framework that accurately predicts transcription factor-target interactions and identifies significant regulatory modules in yeast.
Supporting Evidence
- The model predicts 980 novel binding interactions likely to occur under untested conditions.
- The study identifies 363 significant sets of transcription factors that cooperate to regulate common target genes.
- Experimental validation confirmed several predicted interactions for the factors Rpn4p and Pdr1p under stress conditions.
Takeaway
This study shows how tiny helpers in our cells, called transcription factors, work together to control genes, and it helps us understand how they might act in different situations.
Methodology
The study uses a Bayesian approach to integrate multiple lines of evidence for predicting transcription factor-target interactions.
Potential Biases
Potential biases may arise from the reliance on existing datasets and the conditions under which they were collected.
Limitations
The model's predictions are based on available data, which may not cover all possible conditions affecting transcription factor binding.
Statistical Information
P-Value
1.7 × 10−7
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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