Article
Biochemistry & Molecular Biology
Barbara Szutkowska, Klaudia Wieczorek, Ryszard Kierzek, Pawel Zmora, Jake M. Peterson, Walter N. Moss, David H. Mathews, Elzbieta Kierzek
Summary: This study proposes a secondary structure for segment 8 vRNA of A/California/04/2009 (H1N1) for the first time and highlights the location of conserved structural motifs within IAV strains. The research has implications for understanding the functionality of viral RNA structures and developing inhibition methods against influenza A virus.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemical Research Methods
Kengo Sato, Yuki Kato
Summary: Pseudoknots are important RNA structural elements involved in various biological phenomena. Current methods for secondary structure prediction considering pseudoknots are not widely available. We propose an improved version of IPknot that enables linear time calculation and automatic selection of optimal threshold parameters.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Margherita A. G. Matarrese, Alessandro Loppini, Martina Nicoletti, Simonetta Filippi, Letizia Chiodo
Summary: The study of RNA structure is crucial in understanding RNA molecular functioning. With the flexibility of RNA, the large number of expressed RNAs, and the diverse functions they have, it is difficult to obtain structural information on the same scale as is available for proteins. In silico prediction of RNA 3D structures is particularly important to understand the relationship between structure and function, as the 3D structure plays a significant role in molecular interactions with DNA or protein complexes. The accuracy of RNA 3D structure prediction relies on a properly predicted or measured secondary structure. This paper comparatively evaluates computational tools for modeling RNA secondary structure, focusing on freely available web-server versions for more accessible use. The evaluation focuses on the performance for long sequences and aims to select the best methods for investigating long non-coding RNAs (lncRNAs), which are of special relevance due to their involvement in regulatory mechanisms.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2023)
Article
Biochemistry & Molecular Biology
Barbara Mirska, Tomasz Wozniak, Dagny Lorent, Agnieszka Ruszkowska, Jake M. Peterson, Walter N. Moss, David H. Mathews, Ryszard Kierzek, Elzbieta Kierzek
Summary: Studying the RNA secondary structure of the respiratory virus influenza A virus (IAV) is important for understanding its biology and developing new antiviral drugs. In this study, we used chemical RNA mapping and mutational profiling techniques to analyze the RNA secondary structure of IAV in both in virio and in cellulo environments. The experimental data accurately predicted the structures of all eight viral RNA segments in virio and revealed the structures of three segments in cellulo for the first time. Further analysis identified highly conserved motifs in the predicted viral RNA structures, which could be potential targets for new antiviral strategies against IAV.
CELLULAR AND MOLECULAR LIFE SCIENCES
(2023)
Review
Biochemical Research Methods
L. A. Bugnon, A. A. Edera, S. Prochetto, M. Gerard, J. Raad, E. Fenoy, M. Rubiolo, U. Chorostecki, T. Gabaldon, F. Ariel, L. E. Di Persia, D. H. Milone, G. Stegmayer
Summary: This study compares the performance of classical methods and recently proposed approaches for predicting RNA secondary structure, and introduces a new metric based on chemical probing data to assess their predictive performance. The results provide a comprehensive assessment and a benchmark resource for future approaches.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Mehdi Saman Booy, Alexander Ilin, Pekka Orponen
Summary: This paper presents a simple yet effective data-driven approach for predicting the secondary structure of RNA strands. By using a convolutional neural network and three-dimensional tensors representation, the method achieves significant accuracy improvements on experimental datasets for 10 RNA families and performs well across a wide range of sequence lengths.
BMC BIOINFORMATICS
(2022)
Review
Biochemical Research Methods
Kengo Sato, Michiaki Hamada
Summary: Computational analysis of RNA sequences plays a crucial role in RNA biology. In recent years, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction. Machine learning-based approaches have shown remarkable advancements, enhancing the precision of sequence analysis related to RNA secondary structures. Furthermore, artificial intelligence and machine learning innovations are also applied in the analysis of RNA-small molecule interactions, RNA drug discovery, and the design of RNA aptamers.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Mengyi Tang, Kumbit Hwang, Sung Ha Kang
Summary: This study proposes a new deterministic methodology for predicting the secondary structure of RNA sequences, which uses a simple algorithm to predict the structure of short RNA and tRNA sequences and provides a deterministic answer.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Chun-Chi Chen, Yi-Ming Chan
Summary: In this paper, a deep learning-based method called REDfold is proposed for RNA secondary structure prediction. The method utilizes a CNN-based encoder-decoder network to learn the dependencies between RNA sequences and employs symmetric skip connections to efficiently propagate activation information. Additionally, the network output is post-processed with constrained optimization for accurate predictions, even for RNAs with pseudoknots. Experimental results demonstrate that REDfold outperforms contemporary state-of-the-art methods in terms of efficiency and accuracy.
BMC BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Kangkun Mao, Jun Wang, Yi Xiao
Summary: Deep learning methods have shown better performance than traditional methods in RNA secondary structure prediction, but there is still room for improvement. This is because the length and secondary structures of RNAs vary significantly. Existing deep learning models can't learn very different secondary structures since they are length-independent. In this study, we propose a length-dependent model that further trains the length-independent model using transfer learning for different length ranges of RNAs. The benchmarking results demonstrate that the length-dependent model outperforms the usual length-independent model.
Article
Biochemical Research Methods
Mandy Ibene, Audrey Legendre, Guillaume Postic, Eric Angel, Fariza Tahi
Summary: RNAs can interact with other molecules to form complexes and predicting the structure of these complexes is important but challenging. This study focuses on RNA complexes composed of multiple interacting RNAs and shows how existing knowledge and probing data can help predict their secondary structure. The researchers developed an interactive tool called C-RCPRed, based on a multi-objective optimization algorithm, and demonstrated its efficiency and the positive impact of considering user knowledge and probing data through extensive benchmarking. C-RCPRed is freely available as an open-source program and web server on the EvryRNA website.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Plant Sciences
Haihe Shi, Xiaoqian Jing
Summary: Prediction of RNA secondary structure is an essential part of bioinformatics genomics research, which helps to reveal the genetic laws of organisms. Current RNA secondary structure prediction algorithms often use dynamic programming algorithm due to their high time and space consumption and complex data structure. This article deeply analyzes the domain of DP-SSP algorithm based on dynamic programming and models its features, and designs the algorithm components interactively using the generative programming method, improving the efficiency and reliability of algorithm development.
FRONTIERS IN PLANT SCIENCE
(2022)
Review
Biochemical Research Methods
Junkang Wei, Siyuan Chen, Licheng Zong, Xin Gao, Yu Li
Summary: Protein-RNA interactions play a vital role in cellular activities. Previous computational methods heavily rely on sequence data due to the lack of protein structure data. However, the emergence of AlphaFold is set to revolutionize protein-RNA interaction prediction. In this review, we provide a comprehensive overview of the field, covering binding site and binding preference prediction, as well as commonly used datasets, features, and models. We also discuss potential challenges and opportunities in this area.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Genetics & Heredity
Manato Akiyama, Yasubumi Sakakibara, Kengo Sato
Summary: This study proposes a new algorithm for directly inferring base-pairing probabilities of RNA secondary structures using neural networks, independent of their architecture. The algorithm outperforms existing methods in prediction accuracy, as demonstrated by benchmarks with and without pseudoknots.
Article
Chemistry, Medicinal
Shamsudin S. Nasaev, Artem R. Mukanov, Ivan I. Kuznetsov, Alexander V. Veselovsky
Summary: This study presents a deep learning-based method for predicting RNA secondary structures, which successfully predicts non-homologous RNA data by utilizing data augmentation techniques. The proposed method shows high prediction quality across different benchmarks, including pseudoknots.
MOLECULAR INFORMATICS
(2023)
Article
Virology
Nathan A. Ungerleider, Vaibhav Jain, Yiping Wang, Nicholas J. Maness, Robert Blair, Xavier Alvarez, Cecily Midkiff, Dennis Kolson, Shanshan Bai, Claire Roberts, Walter N. Moss, Xia Wang, Jacqueline Serfecz, Michael Seddon, Terri Lehman, Tianfang Ma, Yan Dong, Rolf Renne, Scott A. Tibbetts, Erik K. Flemington
JOURNAL OF VIROLOGY
(2019)
Review
Biochemistry & Molecular Biology
Ryan J. Andrews, Walter N. Moss
BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS
(2019)
Article
Multidisciplinary Sciences
Alicia J. Angelbello, Suzanne G. Rzuczek, Kendra K. Mckee, Jonathan L. Chen, Hailey Olafson, Michael D. Cameron, Walter N. Moss, Eric T. Wang, Matthew D. Disney
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2019)
Article
Multidisciplinary Sciences
Collin A. O'Leary, Ryan J. Andrews, Van S. Tompkins, Jonathan L. Chen, Jessica L. Childs-Disney, Matthew D. Disney, Walter N. Moss
Article
Multidisciplinary Sciences
Jonathan L. Chen, Walter N. Moss, Adam Spencer, Peiyuan Zhang, Jessica L. Childs-Disney, Matthew D. Disney
Article
Biochemical Research Methods
Ryan J. Andrews, Levi Baber, Walter N. Moss
Article
Biochemistry & Molecular Biology
Raphael Benhamou, Alicia J. Angelbello, Ryan J. Andrews, Eric T. Wang, Walter N. Moss, Matthew D. Disney
ACS CHEMICAL BIOLOGY
(2020)
Article
Multidisciplinary Sciences
Peiyuan Zhang, Hye-Jin Park, Jie Zhang, Eunsung Junn, Ryan J. Andrews, Sai Pradeep Velagapudi, Daniel Abegg, Kamalakannan Vishnu, Matthew G. Costales, Jessica L. Childs-Disney, Alexander Adibekian, Walter N. Moss, M. Maral Mouradian, Matthew D. Disney
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2020)
Article
Biology
Slavica Pavlovic Djuranovic, Jessey Erath, Ryan J. Andrews, Peter O. Bayguinov, Joyce J. Chung, Douglas L. Chalker, James A. J. Fitzpatrick, Walter N. Moss, Pawel Szczesny, Sergej Djuranovic
Article
Multidisciplinary Sciences
Ryan J. Andrews, Collin A. O'Leary, Walter N. Moss
Article
Chemistry, Multidisciplinary
Hafeez S. Haniff, Yuquan Tong, Xiaohui Liu, Jonathan L. Chen, Blessy M. Suresh, Ryan J. Andrews, Jake M. Peterson, Collin A. O'Leary, Raphael Benhamou, Walter N. Moss, Matthew D. Disney
ACS CENTRAL SCIENCE
(2020)
Article
Biochemistry & Molecular Biology
Lumbini Moss, Van S. Tompkins, Walter N. Moss
NON-CODING RNA RESEARCH
(2020)
Review
Chemistry, Multidisciplinary
Andrei Ursu, Jessica L. Childs-Disney, Ryan J. Andrews, Collin A. O'Leary, Samantha M. Meyer, Alicia J. Angelbello, Walter N. Moss, Matthew D. Disney
CHEMICAL SOCIETY REVIEWS
(2020)
Meeting Abstract
Chemistry, Multidisciplinary
Walter Moss, Collin O'Leary, Ryan Andrews
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Article
Multidisciplinary Sciences
Paula Michalak, Marta Soszynska-Jozwiak, Ewa Biala, Walter N. Moss, Julita Kesy, Barbara Szutkowska, Elzbieta Lenartowicz, Ryszard Kierzek, Elzbieta Kierzek
SCIENTIFIC REPORTS
(2019)