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Updated: Jul 13, 2026
07:23
Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
Published on: June 15, 2016
Predictive models of gene regulation: application of regression methods to microarray data.
Debopriya Das1, Michael Q Zhang
1Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Methods in Molecular Biology (Clifton, N.J.)
|July 20, 2007
Summary
This study uses regression techniques to identify functional cis-regulatory elements and their interactions from microarray data, offering insights into gene regulation and the yeast cell cycle.
Area of Science:
- Molecular Biology
- Systems Biology
- Bioinformatics
Background:
- Eukaryotic transcription involves complex interactions of activators and repressors with cis-regulatory elements on DNA.
- These interactions are crucial for recruiting the basal transcription machinery and initiating gene expression.
Purpose of the Study:
- To demonstrate the application of regression techniques for inferring functional cis-regulatory elements from microarray data.
- To explore the cooperativity of these elements and their role in gene regulation.
Main Methods:
- Utilized regression techniques, specifically regression splines, to analyze microarray data.
- Applied methods to yeast cell cycle data to infer regulatory elements and their combinatorial logic.
Main Results:
- Successfully inferred functional cis-regulatory elements and their cooperative binding from gene expression data.
- Demonstrated the effectiveness of regression-based approaches in understanding complex gene regulation.
Conclusions:
- Regression techniques provide a powerful framework for dissecting cis-regulatory element function and cooperativity.
- These methods offer valuable insights into periodic gene regulation, such as in the yeast cell cycle.
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