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Inferring condition-specific transcription factor function from DNA binding and gene expression data.

Rachel Patton McCord¹, Michael F Berger, Anthony A Philippakis

¹Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.

Molecular Systems Biology

|April 18, 2007

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This study introduces a new method to predict how yeast transcription factors (TFs) function under specific conditions by analyzing gene expression and TF binding data. This approach enhances our understanding of gene regulation and TF roles in Saccharomyces cerevisiae.

Area of Science:

Molecular Biology
Genomics
Systems Biology

Background:

Understanding transcriptional regulatory networks in Saccharomyces cerevisiae is crucial for deciphering cellular mechanisms.
Current methods for identifying protein-DNA interactions are often limited to a few cellular states, hindering the prediction of condition-dependent regulatory functions.

Framework:

A novel computational framework is proposed to predict condition-specific transcription factor (TF) functions.
The method integrates extensive gene expression datasets with TF binding information from protein binding microarrays (PBMs) or in silico motif data.

Implementation:

The approach leverages 1327 microarray gene expression datasets for Saccharomyces cerevisiae.
It incorporates comprehensive TF binding site data, avoiding arbitrary thresholds for gene expression or binding events.

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Implications:

The method successfully identifies environmental, physical, and genetic interactions influencing TF function.
It predicts distinct gene sets activated or repressed by single TFs under specific conditions, guiding future in vivo experiments and TF function prediction.