IRFinder-S: a comprehensive suite to discover and explore intron retention

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Accurate quantification of intron retention is crucial for understanding gene regulation.
• Existing software may lack the sensitivity or features for comprehensive analysis.
• Analyzing intron retention across multiple samples presents significant computational challenges.

Purpose of the Study:

• To introduce IRFinder-S, a novel suite of tools for intron retention analysis.
• To enhance the identification and exploration of intron retention events in multiple biological samples.
• To facilitate the sharing and comparison of intron retention data between research groups.

Main Methods:

• Development of IRFinder-S, building upon previous intron retention analysis software.
• Integration of a convolutional neural network for improved identification of true intron retention events.
• Implementation of a dynamic database for sample exploration and comparison.
• Validation of a method for detecting differential intron retention levels.

Main Results:

• IRFinder-S enables more accurate identification of intron retention events through a convolutional neural network.
• The software facilitates the sharing of intron retention results, promoting collaboration.
• A dynamic database allows for effective exploration and contrast of multiple samples.
• A validated method for detecting differential intron retention is provided.

Conclusions:

• IRFinder-S offers a robust and integrated solution for intron retention analysis.
• The software improves the accuracy and efficiency of identifying and analyzing intron retention events.
• IRFinder-S supports collaborative research by enabling data sharing and comparison.