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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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rMSA: A Sequence Search and Alignment Algorithm to Improve RNA Structure Modeling.

Chengxin Zhang¹, Yang Zhang², Anna Marie Pyle³

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.

Journal of Molecular Biology

|June 25, 2023

Summary

This summary is machine-generated.

Generating high-quality multiple sequence alignments (MSAs) for RNA structure modeling is challenging. The new rMSA pipeline offers sensitive search and accurate alignment, significantly improving RNA secondary structure and contact predictions.

Keywords:

RNA secondary structure multiple sequence alignment structure prediction

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Area of Science:

Computational Biology
Bioinformatics
RNA Structure Prediction

Background:

Multiple sequence alignment (MSA) is crucial for RNA structure modeling, including RNA secondary structure (rSS) and contact prediction.
Existing automated methods for generating high-quality RNA MSAs are limited.

Purpose of the Study:

To develop an automated, hierarchical pipeline (rMSA) for sensitive and accurate RNA homolog searching and alignment.
To improve the quality of MSAs for RNA structure prediction tasks.

Main Methods:

Developed rMSA, a hierarchical pipeline for RNA homolog searching and alignment.
Evaluated rMSA performance on a diverse dataset of 365 non-redundant RNA structures.

Main Results:

rMSA significantly outperforms the existing RNAcmap method in generating MSAs.
Achieved approximately 20% and 5% higher F1-scores for rSS and long-range contact prediction, respectively, compared to RNAcmap.
Demonstrated improved accuracy in RNA structure modeling tasks.

Conclusions:

rMSA provides a robust and automated solution for generating high-quality RNA MSAs.
The developed pipeline enhances the accuracy of downstream RNA structure prediction tasks.
rMSA is publicly available for researchers to improve RNA modeling workflows.