DynOmics to identify delays and co-expression patterns across time course experiments.

Area of Science:

• Systems biology
• Computational biology
• Bioinformatics

Background:

• Dynamic changes in biological systems are studied by measuring molecular expression over time.
• Integrating multi-omics data aids in identifying co-regulated molecules and biological processes.
• Existing methods struggle with high-dimensionality, noise, and timing differences in omics data.

Purpose of the Study:

• To develop a novel algorithm for accurate integration of dynamic omics data.
• To address limitations of current co-expression identification methods.
• To identify co-regulated molecules and biological processes from time-series omics data.

Main Methods:

• Developed DynOmics, a novel algorithm based on the Fast Fourier Transform.
• DynOmics estimates expression initiation differences between molecular trajectories.
• Trajectories are realigned based on estimated delays to identify high correlations.

Main Results:

• DynOmics demonstrates efficiency and accuracy in extensive simulations compared to existing methods.
• The algorithm successfully identifies regulatory relationships across omics data within an organism.
• DynOmics is effective for comparative gene expression analysis across organisms.

Conclusions:

• DynOmics provides a robust approach for analyzing dynamic multi-omics data.
• The algorithm overcomes challenges of high-dimensionality, noise, and timing differences.
• DynOmics facilitates the discovery of co-regulated molecules and biological pathways.