RNAthor - fast, accurate normalization, visualization and statistical analysis of RNA probing data resolved by capillary electrophoresis.

Julita Gumna,Kacper Figurski,Katarzyna Pachulska-Wieczorek,Tomasz Zok,Marta Szachniuk,Danny Barash

doi:10.1371/journal.pone.0239287

Abstract

RNAs adopt specific structures to perform their functions, which are critical to fundamental cellular processes. For decades, these structures have been determined and modeled with strong support from computational methods. Still, the accuracy of the latter ones depends on the availability of experimental data, for example, chemical probing information that can define pseudo-energy constraints for RNA folding algorithms. At the same time, diverse computational tools have been developed to facilitate analysis and visualization of data from RNA structure probing experiments followed by capillary electrophoresis or next-generation sequencing. RNAthor, a new software tool for the fully automated normalization of SHAPE and DMS probing data resolved by capillary electrophoresis, has recently joined this collection. RNAthor automatically identifies unreliable probing data. It normalizes the reactivity information to a uniform scale and uses it in the RNA secondary structure prediction. Our web server also provides tools for fast and easy RNA probing data visualization and statistical analysis that facilitates the comparison of multiple data sets. RNAthor is freely available at http://rnathor.cs.put.poznan.pl/.

Highlights

Structural features are of importance for the biological functions of RNA molecules
RNAthor allows for efficient, automated processing and analysis of RNA probing data from SHAPE-CE and DMS-CE experiments and their use in data-driven RNA secondary structure prediction. It was tested on multiple datasets, containing data from SHAPE and DMS probing experiments resolved by capillary electrophoresis
We chose SHAPE-CE and DMS-CE probing data obtained for RNA of yeast retrotransposon Ty1

Summary

Introduction

Structural features are of importance for the biological functions of RNA molecules. Specific RNA structures are recognized by RNA binding proteins, ligands, and other RNAs—these interactions impact almost every aspect of cell life or viral replication. There is a great interest in developing novel approaches for proper and rapid RNA structure modeling. The computational methods enable the obtaining of good quality models of short RNAs based on sequence only, but the accuracy of structure prediction decreases with the length of RNA molecules [1,2,3,4,5]. The inclusion of RNA structure probing data as pseudo-energy constraints into the thermodynamic folding algorithms significantly improves the accuracy of RNA structure prediction [6, 7].

Methods

Results

Conclusion