# Supporting RNA structure: Software for RNA Analysis

> **NIH NIH R01** · UNIVERSITY OF ROCHESTER · 2021 · $346,500

## Abstract

Project Summary:
Rapid sequencing methods identified functional RNA sequences across all domains of life. Additionally,
structure mapping methods demonstrate extensive in vivo structure for RNA, including messenger RNAs.
Determining the roles of RNA structures and their mechanisms of action is central to biology and human
health. RNA secondary structure prediction is one of the tools that is commonly used to aid in understanding
RNA function, and we addressed the need for RNA secondary structure prediction by developing the software
package RNAstructure.
RNAstructure is a user-friendly software package for RNA secondary structure prediction, display, and
analysis. It includes methods for structure prediction of a single sequence, including pseudoknots, structure
prediction for bimolecular interactions, and prediction of the conserved structure for multiple homologous
sequences. It can use structure mapping data, including mapping with chemical agents and enzymes that
reveal unpaired nucleotides, to improve the accuracy of structure prediction. It can also predict unpaired
regions in RNA, and these predictions are essential for siRNA and antisense oligonucleotide design.
Thermodynamic parameters are provided for both RNA and DNA sequences, which extends the structure
predictions to DNA. The programs are available with a graphical user interface (for Windows, Mac OS X, or
Linux), command line interfaces, and also as web servers. The algorithms are also available for use in other
programs as a set of well-documented C++ classes. The package is fully open source, under the GNU Public
License.
For the next period of support, we propose high-impact aims that will keep RNAstructure cutting-edge in its
ability to make new types of structure predictions needed by the community. We will update the nearest
neighbor parameters using the latest experimental results, and expand the parameters to include nucleotides
beyond A, C, G, and U that result from post-transcriptional modification. We will also expand our algorithms to
improve the accuracy of pseudoknot prediction. Finally, we will continue to support our community of users
and developers.

## Key facts

- **NIH application ID:** 10166857
- **Project number:** 5R01GM076485-16
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** DAVID H. MATHEWS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $346,500
- **Award type:** 5
- **Project period:** 2006-07-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10166857

## Citation

> US National Institutes of Health, RePORTER application 10166857, Supporting RNA structure: Software for RNA Analysis (5R01GM076485-16). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10166857. Licensed CC0.

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