TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Time-series gene expression profiles are crucial for understanding complex biological processes.
• Analyzing dynamic regulatory events from these profiles presents significant challenges for current methods.

Purpose of the Study:

• To introduce time-series miner (TSMiner), a novel analytical tool for constructing time-specific regulatory networks.
• To enhance the accuracy and sensitivity of analyzing time-series gene expression data.

Main Methods:

• TSMiner constructs regulatory networks by integrating transcription factor (TF) activity and associated biological pathways.
• The tool analyzes gene expression profiles, identifying TFs activated/repressed at specific times and their interacting pathways.
• It was applied to a mouse liver regeneration (LR) time-course RNA-seq dataset.

Main Results:

• TSMiner identified 389 transcriptional activators and 49 repressors during mouse liver regeneration.
• The tool predicted significant interactions between these regulators and 109 (activators) and 47 (repressors) KEGG pathways.
• Temporal dynamics of critical LR processes, including cell proliferation, metabolism, and immune response, were elucidated.

Conclusions:

• TSMiner offers a highly reliable method for predicting gene regulatory networks from time-series omics data.
• The tool significantly improves the understanding of dynamic biological processes, exemplified by liver regeneration.
• TSMiner demonstrates superior sensitivity and accuracy compared to existing analytical approaches.