TSMiner

TSMiner constructs time-specific regulatory networks from time-series gene expression profiles to identify transcription factors and their temporally associated pathway interactions for studying dynamic regulatory events.

Key Features:

• Time-specific network construction: Constructs regulatory networks from time-series gene expression and RNA-seq data to capture temporal regulatory relationships.
• Transcription factor identification: Identifies transcription factors (TFs) that are activated or repressed at specific time points.
• Pathway interaction analysis: Identifies genes in biologically relevant pathways that exhibit significant mutual interactions with TFs and predicts TF-pathway associations including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
• Quantitative performance: Demonstrates enhanced sensitivity and accuracy compared to existing methods in constructing regulatory networks.

Scientific Applications:

• Mouse liver regeneration (LR) RNA-seq analysis: Applied to a mouse LR RNA-seq time-series dataset, identifying 389 transcriptional activators, 49 repressors, and predicting interactions with 109 KEGG pathways for activators and 47 KEGG pathways for repressors.
• Temporal dynamics in biological processes: Reveals temporal regulation in processes such as cell proliferation, metabolism, and immune response from time-series omics data.

Methodology:

Analyzes time-series expression data to discern patterns and relationships between transcription factors and pathway genes to construct time-specific regulatory networks.