Identification of direct target genes using joint sequence and expression likelihood with application to DAF-16.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Reconstructing gene regulatory networks is crucial in the post-genome era.
• Identifying direct target genes of transcription factors (TFs) is essential for understanding gene regulation.
• Existing methods for TF target identification can be arbitrary or lack comprehensive data integration.

Purpose of the Study:

• To develop and validate a probabilistic model, TRANSMODIS, for inferring direct TF targets.
• To integrate sequence motif, gene expression, and ChIP-chip data for robust target prediction.
• To apply TRANSMODIS to identify TF targets in model organisms like Saccharomyces cerevisiae and Caenorhabditis elegans.

Main Methods:

• Developed TRANSMODIS, a probabilistic model for TF target gene inference.
• Integrated sequence motif, gene expression, and ChIP-chip data.
• Validated TRANSMODIS using transcription factor perturbation experiments (TFPEs) and genome-wide comparisons in S. cerevisiae.
• Applied TRANSMODIS to identify DAF-16 targets in C. elegans.

Main Results:

• TRANSMODIS successfully identified direct target genes, reducing arbitrariness in manual selection.
• Validated performance on Pho4p in S. cerevisiae and genome-wide comparisons.
• Identified 189 genes tightly regulated by DAF-16 in C. elegans.
• Observed differential motif preference for DAF-16 as an activator versus repressor.

Conclusions:

• TRANSMODIS is an efficient and robust probabilistic framework for identifying immediate TF targets.
• The tool aids in reconstructing gene regulatory networks and understanding TF functions.
• Findings on DAF-16 provide insights into aging regulation and suggest areas for experimental verification.