ReTRN: a retriever of real transcriptional regulatory network and expression data for evaluating structure learning...

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Inferring gene regulatory networks from gene expression data is crucial in systems biology.
• Validating these networks requires benchmark datasets, which are often difficult to obtain.
• Existing synthetic datasets may lack biological realism due to limited knowledge of gene expression profiles.

Purpose of the Study:

• To present ReTRN (Real Transcriptional Regulatory Networks), a computational tool for generating realistic synthetic gene expression data.
• To enable the extraction of subnetworks from known transcription networks and the generation of corresponding gene expression data.
• To provide a valid alternative for testing network inference algorithms.

Main Methods:

• ReTRN extracts subnetworks from established transcription networks.
• It generates synthetic gene expression data that reflects temporal relationships.
• The tool's performance is benchmarked against other implementations.

Main Results:

• Networks generated by ReTRN exhibit scale-free properties, mirroring biological networks.
• The synthetic gene expression data captures temporal dynamics.
• ReTRN demonstrates a valid approach for generating biologically relevant datasets.

Conclusions:

• ReTRN offers a valuable tool for systems biology research by providing realistic synthetic data.
• It facilitates the reproducible testing and validation of gene network inference algorithms.
• The tool enhances the study of transcriptional regulatory networks.