Exploring miRNA-target gene pair detection in disease with coRmiT

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Identifying microRNA-target gene pairs (mTPs) is crucial for understanding gene regulation.
• Existing methods for mTP detection from expression data lack a universally optimal strategy.
• Rare diseases present unique challenges for mTP identification due to limited sample sizes.

Purpose of the Study:

• To evaluate multiple strategies for detecting mTPs in rare disease datasets.
• To develop and validate an integrated approach for optimal mTP detection.
• To provide a robust computational tool for miRNA analysis.

Main Methods:

• Applied multiple mTP detection strategies to three rare disease datasets (smallRNA-Seq and RNA-Seq).
• Utilized the DEG_workflow and coRmiT for standardized preprocessing and strategy comparison.
• Investigated overlap with known mTPs from 11 databases.
• Developed a selection-integration method based on the highest odds ratio per miRNA.

Main Results:

• No single mTP detection strategy proved superior across all datasets.
• The proposed selection-integration method demonstrated robustness against variations in mTP databases.
• The integrated approach yielded reliable mTPs associated with disease pathology.

Conclusions:

• An integrated strategy, combining multiple detection methods, is recommended for robust mTP identification.
• The developed method offers a reliable solution for miRNA analysis in rare disease research.
• coRmiT within ExpHunterSuite provides a valuable tool for comparative mTP detection.