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Published on: September 2, 2025
Identification of self-consistent modulons from bacterial microarray expression data with the help of structured
Elizaveta A Permina1, Yulia A Medvedeva, Pia M Baeck
1Vavilov Institute of General Genetics, 3 Gubkina str. , Moscow, GSP-1, 119991, Russia. elizabeth.permina@vigg.ru
This study introduces a novel method for identifying bacterial modulons using gene expression data and prior regulatory information. The approach enhances the reliability of modulon identification, especially for understudied bacterial species.
Area of Science:
- Microbiology
- Systems Biology
- Bioinformatics
Background:
- Identifying bacterial modulons is crucial for understanding gene regulation, particularly in less-studied species.
- Prior knowledge of regulatory interactions aids in inferring gene regulatory subnetworks.
Purpose of the Study:
- To develop and evaluate a procedure for bacterial modulon construction using gene expression data and seed regulons.
- To assess the impact of data parameters on modulon identification accuracy and stability.
Main Methods:
- Iterative updating of seed regulons with genes exhibiting similar expression patterns.
- Selection of essential genes within a regulon to control the updating process.
- Analysis of Escherichia coli gene expression data to test the procedure's sensitivity to data sets, relevance measures, and seed regulons.
Main Results:
- Modulon identification results are highly sensitive to the chosen data, relevance measures, and seed regulons.
- Correctly identified modulons demonstrate increased stability throughout the iterative identification process.
Conclusions:
- The developed procedure offers a reliable method for identifying bacterial modulons, even with sparse prior regulatory information.
- Modulon stability serves as an indicator of successful identification, enabling application to understudied bacterial species.
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