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Mutational analysis in RNAs: comparing programs for RNA deleterious mutation prediction.

Danny Barash¹, Alexander Churkin

¹Department of Computer Science, Ben-Gurion University, 84105 Beer-Shera, Israel. dbarash@cs.bgu.ac.il

Briefings in Bioinformatics

|March 23, 2011

Summary

This review details three RNA mutational analysis programs: RNAMute, RDMAS, and RNAmutants. These tools aid in predicting RNA mutations for various applications, including virus replication and regulatory sequence analysis.

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Area of Science:

Computational Biology
Bioinformatics
Molecular Biology

Background:

Structure-based RNA mutational analysis tools have advanced significantly.
These programs are crucial for understanding RNA virus replication, translation, and regulation.

Purpose of the Study:

To review and compare three freeware programs for RNA mutational analysis: RNAMute, RDMAS, and RNAmutants.
To highlight their capabilities in predicting RNA mutations and their applications.

Main Methods:

Description of RNAMute, RDMAS, and RNAmutants.
Comparison of their underlying methodologies, including energy minimization prediction (RNAfold, Mfold).
Evaluation of their capacity for single-point versus multiple-point mutation predictions.

Main Results:

RNAMute and RDMAS utilize energy minimization prediction.
RNAmutants offers an efficient method for large-scale simulations of k-mutants.
RNAMute (extended version) and RNAmutants can predict multiple-point mutations, while RDMAS is limited to single-point mutations.

Conclusions:

RNAMute excels at accurate prediction of a small number of point mutations.
RNAmutants is suitable for extensive simulations involving numerous mutations.
These programs offer valuable tools for diverse RNA research applications.