DDGni: dynamic delay gene-network inference from high-temporal data using gapped local alignment.

Area of Science:

• Systems Biology
• Molecular Biology
• Bioinformatics

Background:

• Gene regulatory network inference is crucial for understanding biological systems.
• Gene regulation involves complex reaction series, leading to delays, especially with dynamic factors and cellular noise.
• Existing methods often overlook dynamic time delays present in high-temporal data.

Purpose of the Study:

• To propose a novel algorithm, DDGni (dynamic delay gene-network inference), to address the challenge of dynamic time delays in gene regulatory network inference.
• To develop a method capable of detecting short-term gene regulations often missed by traditional approaches.

Main Methods:

• DDGni utilizes gapped local alignment of gene-expression profiles.
• The algorithm incorporates 'gaps' to effectively handle dynamic delays and non-uniform sampling in high-temporal data.
• Local alignment enables detection of short-term gene regulations.

Main Results:

• DDGni was evaluated on synthetic, yeast cell cycle, and Caenorhabditis elegans live cell imaging data.
• The algorithm demonstrated superior performance compared to prominent existing methods.
• DDGni achieved a significantly higher area under the curve across all tested datasets.

Conclusions:

• DDGni is an effective and novel algorithm for inferring gene regulatory networks, particularly in the presence of dynamic time delays.
• The method's ability to handle complex, high-temporal data opens new avenues for biological systems analysis.
• The developed algorithm and associated resources are publicly available.