activeTF

activeTF identifies sets of transcription factors (TFs) that are coordinately activated within a gene expression dataset to reconstruct transcriptional regulatory networks and elucidate signal transduction pathways.

Key Features:

• Dynamic Programming Algorithm: activeTF employs a dynamic programming algorithm to efficiently identify subsets of TFs potentially activated in a coordinated manner under specified conditions.
• Utilization of Differentially Expressed Genes: The tool leverages ranked lists of differentially expressed target genes from gene expression data to infer TF activities.
• Distinguishing Co-regulation from Correlated Expression: activeTF provides a methodological framework to distinguish true coordinately activated TFs from TFs that exhibit correlated expression without co-regulation.
• Application to Microarray Disease Datasets: Application to microarray datasets across various diseases enables identification of TF subsets associated with specific disease processes.

Scientific Applications:

• Transcriptional Regulatory Network Reconstruction: Identifies key coordinately activated TFs to aid mapping of transcriptional regulatory networks.
• Signal Transduction Pathway Elucidation: Pinpoints active TFs under specific conditions to help trace signal transduction pathways and cellular responses.
• Disease Mechanism Exploration: Associates sets of TFs with disease processes to support investigation of molecular mechanisms and potential therapeutic targets.

Methodology:

Analysis of gene expression data to identify differentially expressed genes and generate ranked lists of target genes, followed by a dynamic programming approach to determine subsets of TFs likely activated together.