PcircRNA_finder: a software for circRNA prediction in plants

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Non-coding circular RNAs (circRNAs) play crucial roles in cellular processes.
• Existing circRNA prediction tools, developed for animal and human genomes, exhibit poor performance in plants due to genomic organizational differences.

Purpose of the Study:

• To develop a sensitive and accurate circRNA prediction software specifically for plants.
• To enhance the identification of plant circRNAs, overcoming limitations of existing algorithms.

Main Methods:

• Development of a novel circRNA prediction software named PcircRNA_finder.
• Evaluation of PcircRNA_finder using simulated and real RNA-Seq data from *Arabidopsis thaliana* and rice.
• Comparison of PcircRNA_finder's sensitivity and accuracy against established programs like find_circ and CIRCexplorer.

Main Results:

• PcircRNA_finder demonstrates higher sensitivity in detecting circRNAs in plants compared to frequently used programs.
• The software provides a more comprehensive, sensitive, and precise prediction method for plant circRNAs.
• Analysis of both simulated and real RNA-Seq data validated the tool's performance.

Conclusions:

• PcircRNA_finder represents a significant advancement in plant circRNA prediction.
• The tool offers improved accuracy and sensitivity for plant circRNA identification.
• This development facilitates deeper research into the functions of circRNAs in plant biology.