TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Understanding gene regulatory networks (GRNs) is crucial for defining biological mechanisms in health and disease.
• Current RNA-sequencing analysis pipelines lack time-series models for inferring causality and integrating diverse omics data.
• There is a need for methods that distinguish direct from indirect gene regulatory interactions using time-series and protein-DNA binding data.

Purpose of the Study:

• To introduce TIMEOR, the first adaptive, web-based time-series multi-omics pipeline for inferring gene regulatory relationships over time.
• To address the need for causal inference in GRNs by integrating time-series RNA-seq, motif analysis, and protein-DNA binding data.
• To provide a user-friendly platform for researchers, including non-coders, to generate and validate hypotheses about gene regulation.

Main Methods:

• TIMEOR utilizes time-series RNA-seq data, motif analysis, protein-DNA binding data, and protein-protein interaction networks.
• The pipeline employs time-series models to infer causal relationships within GRNs.
• It is designed to be adaptive to various experimental designs and accessible via a web platform.

Main Results:

• TIMEOR successfully infers relationships between gene regulatory events across time.
• The tool integrates multiple omics data types to distinguish direct and indirect regulatory interactions.
• Application of TIMEOR identified a novel connection between insulin stimulation and the circadian rhythm cycle.

Conclusions:

• TIMEOR is a novel computational tool that advances the analysis of time-series multi-omics data for GRN construction.
• It enables causal inference and mechanistic exploration in biological systems.
• The platform facilitates hypothesis generation and validation, with a demonstrated application in linking metabolic and circadian processes.