PAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Adenosine to inosine (A-to-I) editing is a prevalent RNA modification impacting numerous biological processes.
• Accurate identification of A-to-I editing sites is crucial for understanding their functional roles.
• Experimental methods for identifying A-to-I sites have limitations.

Purpose of the Study:

• To develop a computational model for accurate identification of A-to-I editing sites in D. melanogaster.
• To provide a valuable tool for researchers investigating RNA editing.

Main Methods:

• A support vector machine (SVM)-based model named PAI was developed.
• RNA sequences were encoded using 'pseudo dinucleotide composition'.
• Six RNA physiochemical properties were integrated into the model.

Main Results:

• The PAI model demonstrated promising performance in jackknife and independent dataset tests.
• The model shows high potential as a tool for A-to-I editing site identification.
• A user-friendly web server for PAI was created and is publicly accessible.

Conclusions:

• PAI is an effective computational tool for identifying A-to-I editing sites in D. melanogaster.
• The developed web server facilitates the application of PAI by experimental scientists.
• This tool can aid in advancing the understanding of A-to-I RNA editing functions.