PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences

Area of Science:

• Computational Biology
• Bioinformatics
• Molecular Biology

Background:

• Predicting RNA-RNA interactions is vital for elucidating non-coding RNA functions.
• Current prediction methods rely solely on single sequences.
• Comparative genomics approaches have proven effective for RNA structure determination.

Purpose of the Study:

• To develop and implement a novel method, PETcofold, for predicting RNA-RNA interactions and secondary structures.
• To incorporate evolutionary covariance information into RNA interaction and structure prediction.
• To improve the accuracy of RNA-RNA interaction predictions by leveraging comparative sequence analysis.

Main Methods:

• Implementation of PETcofold, a method that accounts for covariance in intra- and inter-molecular base pairs.
• Prediction of interactions and secondary structures for two multiple alignments of RNA sequences.
• Utilizing comparative sequence analysis to identify conserved RNA-RNA interactions.

Main Results:

• PETcofold evaluated on a dataset of 32 bacterial small RNAs and their targets.
• Demonstrated improved prediction accuracy for RNA-RNA interactions and structures using a comparative approach on high-quality examples.
• Observed enhanced performance with increased covariance in controlled, phylogenetically simulated data.

Conclusions:

• The comparative approach, as implemented in PETcofold, enhances the prediction of RNA-RNA interactions and secondary structures.
• PETcofold's performance improves with greater amounts of evolutionary covariance.
• The PETcofold program is available as open-source software for the research community.