1
|
Chaturvedi M, Rashid MA, Paliwal KK. RNA structure prediction using deep learning - A comprehensive review. Comput Biol Med 2025; 188:109845. [PMID: 39983363 DOI: 10.1016/j.compbiomed.2025.109845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 02/09/2025] [Accepted: 02/10/2025] [Indexed: 02/23/2025]
Abstract
In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led tremendous progress in this field, resulting in significant improvements in prediction accuracy. This comprehensive review aims to provide an overview of the diverse strategies employed in predicting RNA secondary structures, emphasizing deep learning methods. The article categorizes the discussion into three main dimensions: feature extraction methods, existing state-of-the-art learning model architectures, and prediction approaches. We present a comparative analysis of various techniques and models highlighting their strengths and weaknesses. Finally, we identify gaps in the literature, discuss current challenges, and suggest future approaches to enhance model performance and applicability in RNA structure prediction tasks. This review provides a deeper insight into the subject and paves the way for further progress in this dynamic intersection of life sciences and artificial intelligence.
Collapse
Affiliation(s)
- Mayank Chaturvedi
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD, 4111, Australia.
| | - Mahmood A Rashid
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD, 4111, Australia.
| | - Kuldip K Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD, 4111, Australia.
| |
Collapse
|
2
|
Schmidt MD, Kirkpatrick A, Heitsch C. RNAStructViz: Graphical base pairing analysis. Bioinformatics 2021; 37:3660-3661. [PMID: 33823536 PMCID: PMC8545337 DOI: 10.1093/bioinformatics/btab197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/18/2020] [Indexed: 11/13/2022] Open
Abstract
SUMMARY We present a new graphical tool for RNA secondary structure analysis. The central feature is the ability to visually compare/contrast up to three base pairing configurations for a given sequence in a compact, standardized circular arc diagram layout. This is complemented by a built-in CT-style file viewer and radial layout substructure viewer which are directly linked to the arc diagram window via the zoom selection tool. Additional functionality includes the computation of some numerical information, and the ability to export images and data for later use. This tool should be of use to researchers seeking to better understand similarities and differences between structural alternatives for an RNA sequence. AVAILABILITY AND IMPLEMENTATION https://github.com/gtDMMB/RNAStructViz/wiki.
Collapse
Affiliation(s)
- Maxie Dion Schmidt
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA, 30332-0160, United States of America
| | - Anna Kirkpatrick
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA, 30332-0160, United States of America
| | - Christine Heitsch
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA, 30332-0160, United States of America
| |
Collapse
|
3
|
Lu JS, Bindewald E, Kasprzak WK, Shapiro BA. RiboSketch: versatile visualization of multi-stranded RNA and DNA secondary structure. Bioinformatics 2019; 34:4297-4299. [PMID: 29912310 DOI: 10.1093/bioinformatics/bty468] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 06/12/2018] [Indexed: 02/02/2023] Open
Abstract
Summary Creating clear, visually pleasing 2D depictions of RNA and DNA strands and their interactions is important to facilitate and communicate insights related to nucleic acid structure. Here we present RiboSketch, a secondary structure image production application that enables the visualization of multistranded structures via layout algorithms, comprehensive editing capabilities, and a multitude of simulation modes. These interactive features allow RiboSketch to create publication quality diagrams for structures with a wide range of composition, size and complexity. The program may be run in any web browser without the need for installation, or as a standalone Java application. Availability and implementation https://rnastructure.cancer.gov/ribosketch.
Collapse
Affiliation(s)
- Jacob S Lu
- RNA Biology Laboratory, National Cancer Institute, Frederick, MD, USA
| | - Eckart Bindewald
- Basic Science Program, RNA Biology Laboratory, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA
| | - Wojciech K Kasprzak
- Basic Science Program, RNA Biology Laboratory, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA
| | - Bruce A Shapiro
- RNA Biology Laboratory, National Cancer Institute, Frederick, MD, USA
| |
Collapse
|
4
|
Shabash B, Wiese KC. jViz.RNA 4.0-Visualizing pseudoknots and RNA editing employing compressed tree graphs. PLoS One 2019; 14:e0210281. [PMID: 31059508 PMCID: PMC6502502 DOI: 10.1371/journal.pone.0210281] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Accepted: 12/19/2018] [Indexed: 11/18/2022] Open
Abstract
Previously, we have introduced an improved version of jViz.RNA which enabled faster and more stable RNA visualization by employing compressed tree graphs. However, the new RNA representation and visualization method required a sophisticated mechanism of pseudoknot visualization. In this work, we present our novel pseudoknot classification and implementation of pseudoknot visualization in the context of the new RNA graph model. We then compare our approach with other RNA visualization software, and demonstrate jViz.RNA 4.0's benefits compared to other software. Additionally, we introduce interactive editing functionality into jViz.RNA and demonstrate its benefits in exploring and building RNA structures. The results presented highlight the new high degree of utility jViz.RNA 4.0 now offers. Users are now able to visualize pseudoknotted RNA, manipulate the resulting automatic layouts to suit their individual needs, and change both positioning and connectivity of the RNA molecules examined. Care was taken to limit overlap between structural elements, particularly in the case of pseudoknots to ensure an intuitive and informative layout of the final RNA structure. Availability: The software is freely available at: https://jviz.cs.sfu.ca/.
Collapse
Affiliation(s)
- Boris Shabash
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Kay C. Wiese
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
- * E-mail:
| |
Collapse
|
5
|
Abstract
Abstract
Motivation
RNA secondary structure is a useful representation for studying the function of RNA, which captures most of the free energy of RNA folding. Using empirically determined energy parameters, secondary structures of nucleic acids can be efficiently computed by recursive algorithms. Several software packages supporting this task are readily available. As RNA secondary structures are outerplanar graphs, they can be drawn without intersection in the plane. Interpretation by the practitioner is eased when these drawings conform to a series of additional constraints beyond outerplanarity. These constraints are the reason why RNA drawing is difficult. Many RNA drawing algorithms therefore do not always produce intersection-free (outerplanar) drawings.
Results
To remedy this shortcoming we propose here the RNApuzzler algorithm which is guaranteed to produce intersection-free drawings. It is based on a drawing algorithm respecting constraints based on nucleotide distances (RNAturtle). We investigate relaxations of these constraints allowing for intersection-free drawings. Based on these relaxations, we implemented a fully automated, simple, and robust algorithm that produces aesthetic drawings adhering to previously established guidelines. We tested our algorithm using the RFAM database and found that we can compute intersection-free drawings of all RNAs therein efficiently.
Availability and implementation
The software can be accessed freely at: https://github.com/dwiegreffe/RNApuzzler.
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Daniel Wiegreffe
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany
- Bioinformatics Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| | - Daniel Alexander
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| | - Dirk Zeckzer
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| |
Collapse
|
6
|
Wiegreffe D, Alexander D, Stadler PF, Zeckzer D. RNApuzzler: efficient outerplanar drawing of RNA-secondary structures. Bioinformatics 2018; 35:1342-1349. [DOI: 10.1093/bioinformatics/bty817] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 08/09/2018] [Accepted: 09/18/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Daniel Wiegreffe
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany
- Bioinformatics Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| | - Daniel Alexander
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| | - Dirk Zeckzer
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany
| |
Collapse
|