TIMEOR

TIMEOR infers causal temporal regulatory mechanisms from time series multi-omics data to reconstruct gene regulatory networks (GRNs).

Key Features:

• Adaptive Time Series Analysis: Employs time series models to assign cause-and-effect relationships within GRNs.
• Integration of Ordered RNA-seq and TF Binding: Integrates ordered RNA-seq data with transcription factor (TF) binding information to connect DNA, RNA, and protein regulatory layers over time.
• Causal Network Inference between TFs: Predicts causal regulatory mechanism networks between transcription factors (TFs) from time series multi-omics data.
• Trajectory Inference: Infers the progression of gene regulatory events over time.
• Time Series Differential Gene Expression Analysis: Analyzes time series differential gene expression and other multi-omics datasets.

Scientific Applications:

• Gene Regulation Dynamics: Studying temporal gene regulation dynamics in normal and diseased states by identifying regulatory mechanisms over time.
• Causal GRN Discovery: Identifying causal links within gene regulatory networks to elucidate regulatory pathways and interactions among TFs.
• Regulatory Interaction Discovery (example): Has been used to reveal a connection between insulin stimulation and the circadian rhythm cycle.

Methodology:

Analyzes time series differential gene expression and other multi-omics data using trajectory inference, mechanism exploration, and assignment of causal relationships within GRNs via time series models.