MotifRegressor

MotifRegressor identifies sequence motifs upstream of genes that exhibit expression changes under specific conditions to associate regulatory motifs with transcriptional responses.

Key Features:

• Sequence motif discovery: Identifies sequence motifs upstream of genes showing differential expression.
• Integrated approach: Combines matrix-based motif finding with oligomer motif-expression regression analysis.
• Sensitivity and specificity: Achieves high sensitivity and specificity in motif discovery.
• Motif width and degeneracy: Detects medium to long-width motifs that contain multiple degenerate positions.
• Yeast overexpression results: Identified ROX1 and YAP1 motifs from Rox1p and Yap1p overexpression experiments in Saccharomyces cerevisiae.
• Yeast deletion prediction: Predicted increased Gcn4p activity in YAP1 deletion mutants.
• Amino acid starvation motifs: Reported motifs GCN4, PHO4, MET4, STRE, USR1, RAP1, M3A, and M3B potentially mediating transcriptional responses to amino acid starvation.
• Cell-cycle motif recovery: Identified all known cell-cycle regulation motifs from 18 expression microarrays spanning two cell cycles.
• Input data type: Applied to expression microarrays for motif-expression association analyses.

Scientific Applications:

• Expression-mediated motif discovery: Detecting regulatory motifs that mediate gene expression changes under specific conditions.
• Regulatory response mapping in S. cerevisiae: Linking motif discoveries to overexpression and deletion mutant experiments involving Rox1p, Yap1p, and Gcn4p.
• Stress-response motif identification: Identifying motifs involved in transcriptional responses to amino acid starvation (GCN4, PHO4, MET4, STRE, USR1, RAP1, M3A, M3B).
• Cell-cycle regulation analysis: Recovering known cell-cycle regulatory motifs from time-series expression microarray data.

Methodology:

Integrates matrix-based motif finding with oligomer motif-expression regression analysis.