1
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Ferrer Florensa A, Almagro Armenteros J, Nielsen H, Aarestrup F, Clausen P. SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning projects. NAR Genom Bioinform 2024; 6:lqae106. [PMID: 39157582 PMCID: PMC11327874 DOI: 10.1093/nargab/lqae106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 07/26/2024] [Accepted: 08/05/2024] [Indexed: 08/20/2024] Open
Abstract
The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to split the available data randomly into development (train/validation) and test sets. This procedure, although standard, has been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of two state-of-the-art models on bioinformatics, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available at https://github.com/genomicepidemiology/SpanSeq.
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Affiliation(s)
- Alfred Ferrer Florensa
- Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
| | - Jose Juan Almagro Armenteros
- Informatics and Predictive Sciences Research, Bristol Myers Squibb Company, Calle Isaac Newton 4, 41092 Sevilla, Spain
| | - Henrik Nielsen
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
| | - Frank Møller Aarestrup
- Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
| | - Philip Thomas Lanken Conradsen Clausen
- Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
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2
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Nandana V, Al-Husini N, Vaishnav A, Dilrangi KH, Schrader JM. Caulobacter crescentus RNase E condensation contributes to autoregulation and fitness. Mol Biol Cell 2024; 35:ar104. [PMID: 38865176 PMCID: PMC11321048 DOI: 10.1091/mbc.e23-12-0493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/13/2024] Open
Abstract
RNase E is the most common RNA decay nuclease in bacteria, setting the global mRNA decay rate and scaffolding formation of the RNA degradosome complex and BR-bodies. To properly set the global mRNA decay rate, RNase E from Escherichia coli and neighboring γ-proteobacteria were found to autoregulate RNase E levels via the decay of its mRNA's 5' untranslated region (UTR). While the 5' UTR is absent from other groups of bacteria in the Rfam database, we identified that the α-proteobacterium Caulobacter crescentus RNase E contains a similar 5' UTR structure that promotes RNase E autoregulation. In both bacteria, the C-terminal intrinsically disordered region (IDR) of RNase E is required for proper autoregulation to occur, and this IDR is also necessary and sufficient for RNase E to phase-separate, generating BR-bodies. Using in vitro purified RNase E, we find that the IDR's ability to promote phase separation correlates with enhanced 5' UTR cleavage, suggesting that phase separation of RNase E with the 5' UTR enhances autoregulation. Finally, using growth competition experiments, we find that a strain capable of autoregulation rapidly outcompetes a strain with a 5' UTR mutation that cannot autoregulate, suggesting autoregulation promotes optimal cellular fitness.
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Affiliation(s)
- Vidhyadhar Nandana
- Department of Biological Sciences, Wayne State University, Detroit, MI 48202
| | - Nadra Al-Husini
- Department of Biological Sciences, Wayne State University, Detroit, MI 48202
| | - Arti Vaishnav
- Department of Biological Sciences, Wayne State University, Detroit, MI 48202
| | | | - Jared M. Schrader
- Department of Biological Sciences, Wayne State University, Detroit, MI 48202
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3
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Shih M, Zou Y, Ferreira T, Suzuki N, Kim E, Chuang CF, Chang C. The kpc-1 3'UTR facilitates dendritic transport and translation efficiency of mRNAs for dendrite arborization of a mechanosensory neuron important for male courtship. PLoS Genet 2024; 20:e1011362. [PMID: 39110773 DOI: 10.1371/journal.pgen.1011362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 08/19/2024] [Accepted: 07/05/2024] [Indexed: 08/21/2024] Open
Abstract
A recently reported Schizophrenia-associated genetic variant in the 3'UTR of the human furin gene, a homolog of C. elegans kpc-1, highlights an important role of the furin 3'UTR in neuronal development. We isolate three kpc-1 mutants that display abnormal dendrite arborization in PVD neurons and defective male mating behaviors. We show that the kpc-1 3'UTR participates in dendrite branching and self-avoidance. The kpc-1 3'UTR facilitates mRNA localization to branching points and contact points between sibling dendrites and promotes translation efficiency. A predicted secondary structural motif in the kpc-1 3'UTR is required for dendrite self-avoidance. Animals with over-expression of DMA-1, a PVD dendrite receptor, exhibit similar dendrite branching and self-avoidance defects that are suppressed with kpc-1 over-expression. Our results support a model in which KPC-1 proteins are synthesized at branching points and contact points to locally down-regulate DMA-1 receptors to promote dendrite branching and self-avoidance of a mechanosensory neuron important for male courtship.
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Affiliation(s)
- Mushaine Shih
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Yan Zou
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, United States of America
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Tarsis Ferreira
- Division of Developmental Biology, Cincinnati Children's Hospital Research Foundation, Cincinnati, Ohio, United States of America
| | - Nobuko Suzuki
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Eunseo Kim
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Chiou-Fen Chuang
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Chieh Chang
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, United States of America
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4
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Malik A, Zhang L, Gautam M, Dai N, Li S, Zhang H, Mathews DH, Huang L. LinearAlifold: Linear-time consensus structure prediction for RNA alignments. J Mol Biol 2024:168694. [PMID: 38971557 DOI: 10.1016/j.jmb.2024.168694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 07/08/2024]
Abstract
Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (∼30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 h on the above 400 genomes, or ∼36× speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at: https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).
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Affiliation(s)
- Apoorv Malik
- School of EECS, Oregon State University, Corvallis, OR 97330, USA
| | - Liang Zhang
- School of EECS, Oregon State University, Corvallis, OR 97330, USA
| | - Milan Gautam
- School of EECS, Oregon State University, Corvallis, OR 97330, USA
| | - Ning Dai
- School of EECS, Oregon State University, Corvallis, OR 97330, USA
| | - Sizhen Li
- School of EECS, Oregon State University, Corvallis, OR 97330, USA
| | - He Zhang
- School of EECS, Oregon State University, Corvallis, OR 97330, USA
| | - David H Mathews
- Dept. of Biochemistry & Biophysics, University of Rochester Medical Center, Rochester, NY 14642, USA; Center for RNA Biology, University of Rochester Medical Center, Rochester, NY 14642, USA; Dept. of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Liang Huang
- School of EECS, Oregon State University, Corvallis, OR 97330, USA; Dept. of Biochemistry & Biophysics, Oregon State University, Corvallis, OR 97330, USA.
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5
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Bugnon LA, Di Persia L, Gerard M, Raad J, Prochetto S, Fenoy E, Chorostecki U, Ariel F, Stegmayer G, Milone DH. sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure. Brief Bioinform 2024; 25:bbae271. [PMID: 38855913 PMCID: PMC11163250 DOI: 10.1093/bib/bbae271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/03/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024] Open
Abstract
MOTIVATION Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. RESULTS In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.
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Affiliation(s)
- Leandro A Bugnon
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Leandro Di Persia
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Matias Gerard
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Jonathan Raad
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Santiago Prochetto
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
- Instituto de Agrobiotecnología del Litoral, CONICET-UNL, CCT-Santa Fe, Ruta Nacional N° 168 Km 0, s/n, Paraje el Pozo, 3000, Santa Fe, Argentina
| | - Emilio Fenoy
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Uciel Chorostecki
- Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Federico Ariel
- Instituto de Agrobiotecnología del Litoral, CONICET-UNL, CCT-Santa Fe, Ruta Nacional N° 168 Km 0, s/n, Paraje el Pozo, 3000, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
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Rinaldi S, Moroni E, Rozza R, Magistrato A. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. J Chem Theory Comput 2024; 20:993-1018. [PMID: 38287883 DOI: 10.1021/acs.jctc.3c01239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.
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Affiliation(s)
- Silvia Rinaldi
- National Research Council of Italy (CNR) - Institute of Chemistry of OrganoMetallic Compounds (ICCOM), c/o Area di Ricerca CNR di Firenze Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Elisabetta Moroni
- National Research Council of Italy (CNR) - Institute of Chemical Sciences and Technologies (SCITEC), via Mario Bianco 9, 20131 Milano, Italy
| | - Riccardo Rozza
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
| | - Alessandra Magistrato
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
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7
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Zhang S, Li J, Chen SJ. Machine learning in RNA structure prediction: Advances and challenges. Biophys J 2024:S0006-3495(24)00067-5. [PMID: 38297836 DOI: 10.1016/j.bpj.2024.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.
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Affiliation(s)
- Sicheng Zhang
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Jun Li
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Shi-Jie Chen
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri; Department of Biochemistry, University of Missouri, Columbia, Missouri.
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8
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Nandana V, Al-Husini N, Vaishnav A, Dilrangi KH, Schrader JM. Caulobacter crescentus RNase E condensation contributes to autoregulation and fitness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571756. [PMID: 38168245 PMCID: PMC10760160 DOI: 10.1101/2023.12.15.571756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
RNase E is the most common RNA decay nuclease in bacteria, setting the global mRNA decay rate and scaffolding formation of the RNA degradosome complex and BR-bodies. To properly set the global mRNA decay rate, RNase E from Escherichia coli and neighboring γ-proteobacteria were found to autoregulate RNase E levels via the decay of its mRNA's 5' UTR. While the 5' UTR is absent from other groups of bacteria in the Rfam database, we identified that the α-proteobacterium Caulobacter crescentus RNase E contains a similar 5' UTR structure that promotes RNase E autoregulation. In both bacteria, the C-terminal IDR of RNase E is required for proper autoregulation to occur, and this IDR is also necessary and sufficient for RNase E to phase-separate, generating BR-bodies. Using in vitro purified RNase E, we find that the IDR's ability to promote phase-separation correlates with enhanced 5' UTR cleavage, suggesting that phase-separation of RNase E with the 5' UTR enhances autoregulation. Finally, using growth competition experiments we find that a strain capable of autoregulation rapidly outcompetes a strain with a 5' UTR mutation that cannot autoregulate, suggesting autoregulation promotes optimal cellular fitness.
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Affiliation(s)
- Vidhyadhar Nandana
- Department of Biological Sciences, Wayne State University, Detroit, MI 48202
| | - Nadra Al-Husini
- Department of Biological Sciences, Wayne State University, Detroit, MI 48202
| | | | | | - Jared M. Schrader
- Department of Biological Sciences, Wayne State University, Detroit, MI 48202
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9
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von Dassow P, Mikhno M, Percopo I, Orellana VR, Aguilera V, Álvarez G, Araya M, Cornejo-Guzmán S, Llona T, Mardones JI, Norambuena L, Salas-Rojas V, Kooistra WHCF, Montresor M, Sarno D. Diversity and toxicity of the planktonic diatom genus Pseudo-nitzschia from coastal and offshore waters of the Southeast Pacific, including Pseudo-nitzschia dampieri sp. nov. HARMFUL ALGAE 2023; 130:102520. [PMID: 38061816 DOI: 10.1016/j.hal.2023.102520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 09/18/2023] [Accepted: 09/29/2023] [Indexed: 12/18/2023]
Abstract
To expand knowledge of Pseudo-nitzschia species in the Southeast Pacific, we isolated specimens from coastal waters of central Chile (36°S-30°S), the Gulf of Corcovado, and the oceanic Robinson Crusoe Island (700 km offshore) and grew them into monoclonal strains. A total of 123 Pseudo-nitzschia strains were identified to 11 species based on sequencing of the ITS region of the nuclear rDNA and on ultrastructural and morphometric analyses of the frustule in selected representatives of each clade: P. australis, P. bucculenta, P. cf. chiniana, P. cf. decipiens, P. fraudulenta, P. hasleana, P. multistriata, P. plurisecta, P. cf. sabit, the new species P. dampieri sp. nov., and one undescribed species. Partial 18S and 28S rDNA sequences, including the hypervariable V4 and D1-D3 regions used for barcoding, were gathered from representative strains of each species to facilitate future metabarcoding studies. Results showed different levels of genetic, and at times ultrastructural, diversity among the above-mentioned entities, suggesting morphological variants (P. bucculenta), rapidly radiating complexes with ill-defined species boundaries (P. cf. decipiens and P. cf. sabit), and the presence of new species (P. dampieri sp. nov., Pseudo-nitzschia sp. 1, and probably P. cf. chiniana). Domoic acid (DA) was detected in 18 out of 82 strains tested, including those of P. australis, P. plurisecta, and P. multistriata. Toxicity varied among species mostly corresponding to expectations from previous reports, with the prominent exception of P. fraudulenta; DA was not detected in any of its 10 strains tested. In conclusion, a high diversity of Pseudo-nitzschia exists in Chilean waters, particularly offshore.
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Affiliation(s)
- Peter von Dassow
- Departamento de Ecología, Facultad de Ciencias Biologicas, Pontificia Universidad Catolica de Chile, Avenida Libertador Bernardo O'Higgins 340, Santiago, 8331150, Chile; Instituto Milenio de Oceanografía, Universidad de Concepción, Barrio Universitario S/N, Concepción, 4070112, Chile; Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy.
| | - Marta Mikhno
- Departamento de Ecología, Facultad de Ciencias Biologicas, Pontificia Universidad Catolica de Chile, Avenida Libertador Bernardo O'Higgins 340, Santiago, 8331150, Chile; Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy
| | - Isabella Percopo
- Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy
| | - Valentina Rubio Orellana
- Departamento de Ecología, Facultad de Ciencias Biologicas, Pontificia Universidad Catolica de Chile, Avenida Libertador Bernardo O'Higgins 340, Santiago, 8331150, Chile; Instituto Milenio de Oceanografía, Universidad de Concepción, Barrio Universitario S/N, Concepción, 4070112, Chile
| | - Víctor Aguilera
- Instituto Milenio de Oceanografía, Universidad de Concepción, Barrio Universitario S/N, Concepción, 4070112, Chile; Laboratorio de Oceanografía Desértico Costera (LODEC), Centro de Estudios Avanzados en Zonas Áridas, Larrondo 1281, Coquimbo, 1781421, Chile
| | - Gonzalo Álvarez
- Facultad de Ciencias del Mar, Departamento de Acuicultura, Universidad Católica del Norte, Larrondo 1281, Coquimbo, 1781421, Chile; Centro de Investigación y Desarrollo Tecnológico en Algas (CIDTA), Universidad Católica del Norte, Larrondo 1281, Coquimbo, 1781421, Chile
| | - Michael Araya
- Centro de Investigación y Desarrollo Tecnológico en Algas (CIDTA), Universidad Católica del Norte, Larrondo 1281, Coquimbo, 1781421, Chile
| | - Sebastián Cornejo-Guzmán
- Departamento de Geofísica, Universidad de Concepción, Barrio Universitario S/N, Concepción, 4070112 Chile
| | - Tomás Llona
- Instituto Milenio de Oceanografía, Universidad de Concepción, Barrio Universitario S/N, Concepción, 4070112, Chile
| | - Jorge I Mardones
- Centro de Estudio de Algas Nocivas (CREAN), Instituto de Fomento Pesquero, Padre Harter 574, Puerto Montt, 5501679, Chile; Centro de Investigación en Recursos Naturales y Sustentabilidad (CIRENYS), Universidad Bernardo O´Higgins, Santiago 8370993, Chile
| | - Luis Norambuena
- Centro de Estudio de Algas Nocivas (CREAN), Instituto de Fomento Pesquero, Padre Harter 574, Puerto Montt, 5501679, Chile
| | - Victoria Salas-Rojas
- Departamento de Ecología, Facultad de Ciencias Biologicas, Pontificia Universidad Catolica de Chile, Avenida Libertador Bernardo O'Higgins 340, Santiago, 8331150, Chile; Instituto Milenio de Oceanografía, Universidad de Concepción, Barrio Universitario S/N, Concepción, 4070112, Chile
| | | | - Marina Montresor
- Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy
| | - Diana Sarno
- Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy
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10
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Nasaev SS, Mukanov AR, Kuznetsov II, Veselovsky AV. AliNA - a deep learning program for RNA secondary structure prediction. Mol Inform 2023; 42:e202300113. [PMID: 37710142 DOI: 10.1002/minf.202300113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/16/2023]
Abstract
Nowadays there are numerous discovered natural RNA variations participating in different cellular processes and artificial RNA, e. g., aptamers, riboswitches. One of the required tasks in the investigation of their functions and mechanism of influence on cells and interaction with targets is the prediction of RNA secondary structures. The classic thermodynamic-based prediction algorithms do not consider the specificity of biological folding and deep learning methods that were designed to resolve this issue suffer from homology-based methods problems. Herein, we present a method for RNA secondary structure prediction based on deep learning - AliNA (ALIgned Nucleic Acids). Our method successfully predicts secondary structures for non-homologous to train-data RNA families thanks to usage of the data augmentation techniques. Augmentation extends existing datasets with easily-accessible simulated data. The proposed method shows a high quality of prediction across different benchmarks including pseudoknots. The method is available on GitHub for free (https://github.com/Arty40m/AliNA).
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Affiliation(s)
- Shamsudin S Nasaev
- Institute of Biomedical Chemistry, 10, Pogodinskaya str., 119121, Moscow, Russia
| | - Artem R Mukanov
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, 18, Kremlyovskaya str., 420008, Kazan, Russia
| | - Ivan I Kuznetsov
- Moscow University of Finance and Law, 10 block 1, Serpuhovsky val str., 115191, Moscow, Russia
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11
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Tieng FYF, Abdullah-Zawawi MR, Md Shahri NAA, Mohamed-Hussein ZA, Lee LH, Mutalib NSA. A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools. Brief Bioinform 2023; 25:bbad421. [PMID: 38040490 PMCID: PMC10753535 DOI: 10.1093/bib/bbad421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/26/2023] [Indexed: 12/03/2023] Open
Abstract
RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.
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Affiliation(s)
- Francis Yew Fu Tieng
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
| | | | - Nur Alyaa Afifah Md Shahri
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of Systems Biology (INBIOSIS), UKM, Selangor 43600, Malaysia
- Department of Applied Physics, Faculty of Science and Technology, UKM, Selangor 43600, Malaysia
| | - Learn-Han Lee
- Sunway Microbiomics Centre, School of Medical and Life Sciences, Sunway University, Sunway City 47500, Malaysia
- Novel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University of Malaysia, Selangor 47500, Malaysia
| | - Nurul-Syakima Ab Mutalib
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
- Novel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University of Malaysia, Selangor 47500, Malaysia
- Faculty of Health Sciences, UKM, Kuala Lumpur 50300, Malaysia
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12
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Wang Y, Zhang H, Xu Z, Zhang S, Guo R. TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19320-19340. [PMID: 38052602 DOI: 10.3934/mbe.2023854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance.
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Affiliation(s)
- Yunxiang Wang
- School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China
| | - Hong Zhang
- School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China
| | - Zhenchao Xu
- School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China
| | - Shouhua Zhang
- Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Rui Guo
- College of Life Sciences, Institute of Life Science and Green Development, Hebei University, Baoding, China
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13
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Yang E, Zhang H, Zang Z, Zhou Z, Wang S, Liu Z, Liu Y. GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction. Comput Biol Med 2023; 164:107246. [PMID: 37487383 DOI: 10.1016/j.compbiomed.2023.107246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/26/2023]
Abstract
RNA secondary structure is essential for predicting the tertiary structure and understanding RNA function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. Structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of RNA, such as stems, hairpin, and internal loops. Structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an RNA secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://github.com/EnbinYang/GCNfold.
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Affiliation(s)
- Enbin Yang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Hao Zhang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China
| | - Zinan Zang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhiyong Zhou
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Shuo Wang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhen Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Graduate School of Engineering, Nagasaki Institute of Applied Science, 536 Aba-machi, Nagasaki 851-0193, Japan
| | - Yuanning Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China.
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14
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Tang M, Hwang K, Kang SH. StemP: A Fast and Deterministic Stem-Graph Approach for RNA Secondary Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3278-3291. [PMID: 37028040 DOI: 10.1109/tcbb.2023.3253049] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We propose a new deterministic methodology to predict the secondary structure of RNA sequences. What information of stem is important for structure prediction, and is it enough ? The proposed simple deterministic algorithm uses minimum stem length, Stem-Loop score, and co-existence of stems, to give good structure predictions for short RNA and tRNA sequences. The main idea is to consider all possible stem with certain stem loop energy and strength to predict RNA secondary structure. We use graph notation, where stems are represented as vertexes, and co-existence between stems as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for structure prediction. Stem-Loop score adds structure information and speeds up the computation. The proposed method can predict secondary structure even with pseudo knots. One of the strengths of this approach is the simplicity and flexibility of the algorithm, and it gives a deterministic answer. Numerical experiments are done on various sequences from Protein Data Bank and the Gutell Lab using a laptop and results take only a few seconds.
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15
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Wu KE, Zou JY, Chang H. Machine learning modeling of RNA structures: methods, challenges and future perspectives. Brief Bioinform 2023; 24:bbad210. [PMID: 37280185 DOI: 10.1093/bib/bbad210] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 06/08/2023] Open
Abstract
The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.
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Affiliation(s)
- Kevin E Wu
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - James Y Zou
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Howard Chang
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
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16
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Sato K, Hamada M. Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery. Brief Bioinform 2023; 24:bbad186. [PMID: 37232359 PMCID: PMC10359090 DOI: 10.1093/bib/bbad186] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/27/2023] Open
Abstract
Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.
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Affiliation(s)
- Kengo Sato
- School of System Design and Technology, Tokyo Denki University, 5 Senju Asahi-cho, Adachi-ku, Tokyo 120-8551, Japan
| | - Michiaki Hamada
- Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) , National Institute of Advanced Industrial Science and Technology (AIST), 3-4-1, Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Graduate School of Medicine, Nippon Medical School, 1-1-5, Sendagi, Bunkyo-ku, Tokyo 113-8602, Japan
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17
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Abstract
RNAstructure is a user-friendly program for the prediction and analysis of RNA secondary structure. It is available as a web server, a program with a graphical user interface, or a set of command line tools. The programs are available for Microsoft Windows, macOS, or Linux. This article provides protocols for prediction of RNA secondary structure (using the web server, the graphical user interface, or the command line) and high-affinity oligonucleotide binding sites to a structured RNA target (using the graphical user interface). © 2023 Wiley Periodicals LLC. Basic Protocol 1: Predicting RNA secondary structure using the RNAstructure web server Alternate Protocol 1: Predicting secondary structure and base pair probabilities using the RNAstructure graphical user interface Alternate Protocol 2: Predicting secondary structure and base pair probabilities using the RNAstructure command line interface Basic Protocol 2: Predicting binding affinities of oligonucleotides complementary to an RNA target using OligoWalk.
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Affiliation(s)
- Sara E. Ali
- Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, New York 14642
| | - Abhinav Mittal
- Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, New York 14642
| | - David H. Mathews
- Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, New York 14642
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18
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Justyna M, Antczak M, Szachniuk M. Machine learning for RNA 2D structure prediction benchmarked on experimental data. Brief Bioinform 2023; 24:7140288. [PMID: 37096592 DOI: 10.1093/bib/bbad153] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/15/2023] [Accepted: 03/29/2023] [Indexed: 04/26/2023] Open
Abstract
Since the 1980s, dozens of computational methods have addressed the problem of predicting RNA secondary structure. Among them are those that follow standard optimization approaches and, more recently, machine learning (ML) algorithms. The former were repeatedly benchmarked on various datasets. The latter, on the other hand, have not yet undergone extensive analysis that could suggest to the user which algorithm best fits the problem to be solved. In this review, we compare 15 methods that predict the secondary structure of RNA, of which 6 are based on deep learning (DL), 3 on shallow learning (SL) and 6 control methods on non-ML approaches. We discuss the ML strategies implemented and perform three experiments in which we evaluate the prediction of (I) representatives of the RNA equivalence classes, (II) selected Rfam sequences and (III) RNAs from new Rfam families. We show that DL-based algorithms (such as SPOT-RNA and UFold) can outperform SL and traditional methods if the data distribution is similar in the training and testing set. However, when predicting 2D structures for new RNA families, the advantage of DL is no longer clear, and its performance is inferior or equal to that of SL and non-ML methods.
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Affiliation(s)
- Marek Justyna
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Maciej Antczak
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Marta Szachniuk
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
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19
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Li J, Chen SJ. RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling. Nucleic Acids Res 2023; 51:3341-3356. [PMID: 36864729 PMCID: PMC10123122 DOI: 10.1093/nar/gkad122] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 01/14/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023] Open
Abstract
RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.
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Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
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20
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Qiu X. Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction. PLoS Comput Biol 2023; 19:e1011047. [PMID: 37068100 PMCID: PMC10138783 DOI: 10.1371/journal.pcbi.1011047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 04/27/2023] [Accepted: 03/25/2023] [Indexed: 04/18/2023] Open
Abstract
Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.
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Affiliation(s)
- Xiangyun Qiu
- Department of Physics, George Washington University, Washington DC, United States of America
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21
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Chen CC, Chan YM. REDfold: accurate RNA secondary structure prediction using residual encoder-decoder network. BMC Bioinformatics 2023; 24:122. [PMID: 36977986 PMCID: PMC10044938 DOI: 10.1186/s12859-023-05238-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND As the RNA secondary structure is highly related to its stability and functions, the structure prediction is of great value to biological research. The traditional computational prediction for RNA secondary prediction is mainly based on the thermodynamic model with dynamic programming to find the optimal structure. However, the prediction performance based on the traditional approach is unsatisfactory for further research. Besides, the computational complexity of the structure prediction using dynamic programming is [Formula: see text]; it becomes [Formula: see text] for RNA structure with pseudoknots, which is computationally impractical for large-scale analysis. RESULTS In this paper, we propose REDfold, a novel deep learning-based method for RNA secondary prediction. REDfold utilizes an encoder-decoder network based on CNN to learn the short and long range dependencies among the RNA sequence, and the network is further integrated with symmetric skip connections to efficiently propagate activation information across layers. Moreover, the network output is post-processed with constrained optimization to yield favorable predictions even for RNAs with pseudoknots. Experimental results based on the ncRNA database demonstrate that REDfold achieves better performance in terms of efficiency and accuracy, outperforming the contemporary state-of-the-art methods.
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Affiliation(s)
- Chun-Chi Chen
- Department of Electrical Engineering, National Chiayi University, Chiayi, Taiwan.
| | - Yi-Ming Chan
- MindtronicAI Co., Ltd., 7F, No. 218, Sec. 6, Roosevelt Rd., 24105, Taipei, Taiwan
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22
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Zhao Q, Mao Q, Zhao Z, Yuan W, He Q, Sun Q, Yao Y, Fan X. RNA independent fragment partition method based on deep learning for RNA secondary structure prediction. Sci Rep 2023; 13:2861. [PMID: 36801945 PMCID: PMC9938198 DOI: 10.1038/s41598-023-30124-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/16/2023] [Indexed: 02/19/2023] Open
Abstract
The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar .
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Affiliation(s)
- Qi Zhao
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169 Liaoning China
| | - Qian Mao
- grid.411356.40000 0000 9339 3042College of Light Industry, Liaoning University, Shenyang, 110036 Liaoning China
| | - Zheng Zhao
- grid.440686.80000 0001 0543 8253College of Artificial Intelligence, Dalian Maritime University, Dalian, 116026 Liaoning China
| | - Wenxuan Yuan
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169 Liaoning China
| | - Qiang He
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169 Liaoning China
| | - Qixuan Sun
- grid.412252.20000 0004 0368 6968College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169 Liaoning China
| | - Yudong Yao
- grid.217309.e0000 0001 2180 0654Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030 USA
| | - Xiaoya Fan
- School of Software, Dalian University of Technology, Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, 116620, Liaoning, China.
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23
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Hollar A, Bursey H, Jabbari H. Pseudoknots in RNA Structure Prediction. Curr Protoc 2023; 3:e661. [PMID: 36779804 DOI: 10.1002/cpz1.661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases. © 2023 Wiley Periodicals LLC.
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Affiliation(s)
- Andrew Hollar
- Department of Computer Science, University of Victoria, Victoria, Canada
| | - Hunter Bursey
- Department of Computer Science, University of Victoria, Victoria, Canada
| | - Hosna Jabbari
- Department of Computer Science, University of Victoria, Victoria, Canada
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24
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Kilar AM, Fajkus P, Fajkus J. GERONIMO: A tool for systematic retrieval of structural RNAs in a broad evolutionary context. Gigascience 2022; 12:giad080. [PMID: 37848616 PMCID: PMC10580375 DOI: 10.1093/gigascience/giad080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/04/2023] [Accepted: 09/11/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND While web-based tools such as BLAST have made identifying conserved gene homologs appear easy, genes with variable sequences pose significant challenges. Functionally important noncoding RNAs (ncRNA) often show low sequence conservation due to genetic variations, including insertions and deletions. Rather than conserved sequences, these RNAs possess highly conserved structural features across a broad phylogenetic range. Such features can be identified using the covariance models approach, which combines sequence alignment with a secondary RNA structure consensus. However, running standard implementation of that approach (Infernal) requires advanced bioinformatics knowledge compared to user-friendly web services like BLAST. The issue is partially addressed by RNAcentral, which can be used to search for homologs across a broad range of ncRNA sequence collections from diverse organisms but not across the genome assemblies. RESULTS Here, we present GERONIMO, which conducts evolutionary searches across hundreds of genomes in a fully automated way. It provides results extended with taxonomy context, as summary tables and visualizations, to facilitate analysis for user convenience. Additionally, GERONIMO supplements homologous sequences with genomic regions to analyze promoter motifs or gene collinearity, enhancing the validation of results. CONCLUSION GERONIMO, built using Snakemake, has undergone extensive testing on hundreds of genomes, establishing itself as a valuable tool in the identification of ncRNA homologs across diverse taxonomic groups. Consequently, GERONIMO facilitates the investigation of the evolutionary patterns of functionally significant ncRNA players, whose understanding has previously been limited to individual organisms and close relatives.
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Affiliation(s)
- Agata M Kilar
- Mendel Centre for Plant Genomics and Proteomics, CEITEC Masaryk University, Brno CZ-62500, Czech Republic
- Laboratory of Functional Genomics and Proteomics, NCBR, Faculty of Science, Masaryk University, Brno CZ-61137, Czech Republic
| | - Petr Fajkus
- Mendel Centre for Plant Genomics and Proteomics, CEITEC Masaryk University, Brno CZ-62500, Czech Republic
- Department of Cell Biology and Radiobiology, Institute of Biophysics of the Czech Academy of Sciences, Brno CZ-61265, Czech Republic
| | - Jiří Fajkus
- Mendel Centre for Plant Genomics and Proteomics, CEITEC Masaryk University, Brno CZ-62500, Czech Republic
- Laboratory of Functional Genomics and Proteomics, NCBR, Faculty of Science, Masaryk University, Brno CZ-61137, Czech Republic
- Department of Cell Biology and Radiobiology, Institute of Biophysics of the Czech Academy of Sciences, Brno CZ-61265, Czech Republic
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25
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Devi MP, Dasgupta M, Mohanty S, Sharma SK, Hegde V, Roy SS, Renadevan R, Kumar KB, Patel HK, Sahoo MR. DNA Barcoding and ITS2 Secondary Structure Predictions in Taro ( Colocasia esculenta L. Schott) from the North Eastern Hill Region of India. Genes (Basel) 2022; 13:genes13122294. [PMID: 36553561 PMCID: PMC9778394 DOI: 10.3390/genes13122294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 10/28/2022] [Accepted: 11/01/2022] [Indexed: 12/12/2022] Open
Abstract
Taro (Colocasia esculenta L. Schott, Araceae), an ancient root and tuber crop, is highly polygenic, polyphyletic, and polygeographic in nature, which leads to its rapid genetic erosion. To prevent the perceived loss of taro diversity, species discrimination and genetic conservation of promising taro genotypes need special attention. Reports on genetic discrimination of taro at its center of origin are still untapped. We performed DNA barcoding of twenty promising genotypes of taro indigenous to the northeastern hill region of India, deploying two chloroplast-plastid genes, matK and rbcL, and the ribosomal nuclear gene ITS2. The secondary structure of ITS2 was determined and molecular phylogeny was performed to assess genetic discrimination among the taro genotypes. The matK and rbcL genes were highly efficient (>90%) in amplification and sequencing. However, the ITS2 barcode region achieved significant discrimination among the tested taro genotypes. All the taro genotypes displayed most similar sequences at the conserved matK and rbcL loci. However, distinct sequence lengths were observed in the ITS2 barcode region, revealing accurate discriminations among the genotypes. Multiple barcode markers are unrelated to one another and change independently, providing different estimations of heritable traits and genetic lineages; thus, they are advantageous over a single locus in genetic discrimination studies. A dynamic programming algorithm that used base-pairing interactions within a single nucleic acid polymer or between two polymers transformed the secondary structures into the symbol code data to predict seven different minimum free energy secondary structures. Our analysis strengthens the potential of the ITS2 gene as a potent DNA barcode candidate in the prediction of a valuable secondary structure that would help in genetic discrimination between the genotypes while augmenting future breeding strategies in taro.
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Affiliation(s)
- Mayengbam Premi Devi
- Indian Council of Agricultural Research (ICAR) Research Complex for North Eastern Hill Region, Imphal 795004, India
- College of Agriculture, Central Agricultural University (CAU-Imphal), Kyrdemkulai 793105, India
| | - Madhumita Dasgupta
- Indian Council of Agricultural Research (ICAR) Research Complex for North Eastern Hill Region, Imphal 795004, India
| | - Sansuta Mohanty
- Central Horticultural Experiment Station, ICAR–Indian Institute of Horticultural Research, Bhubaneswar 751019, India
| | - Susheel Kumar Sharma
- Indian Council of Agricultural Research (ICAR) Research Complex for North Eastern Hill Region, Imphal 795004, India
- ICAR—Indian Agricultural Research Institute, Pusa Campus, New Delhi 110012, India
| | - Vivek Hegde
- ICAR—Central Tuber Crops Research Institute, Thiruvananthapuram 695017, India
- ICAR—Indian Institute of Horticultural Research, Bengaluru 560089, India
| | - Subhra Saikat Roy
- Indian Council of Agricultural Research (ICAR) Research Complex for North Eastern Hill Region, Imphal 795004, India
| | - Rennya Renadevan
- Centre for Cellular and Molecular Biology, Hyderabad 570007, India
| | | | - Hitendra Kumar Patel
- Centre for Cellular and Molecular Biology, Hyderabad 570007, India
- Correspondence: (H.K.P.); (M.R.S.); Tel.: +91-674-247-1867 (M.R.S.); Fax: +91-674-247-1712 (M.R.S.)
| | - Manas Ranjan Sahoo
- Indian Council of Agricultural Research (ICAR) Research Complex for North Eastern Hill Region, Imphal 795004, India
- Central Horticultural Experiment Station, ICAR–Indian Institute of Horticultural Research, Bhubaneswar 751019, India
- Correspondence: (H.K.P.); (M.R.S.); Tel.: +91-674-247-1867 (M.R.S.); Fax: +91-674-247-1712 (M.R.S.)
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26
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rMSA: a sequence search and alignment algorithm to improve RNA structure modeling. J Mol Biol 2022. [DOI: 10.1016/j.jmb.2022.167904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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27
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Fukunaga T, Hamada M. LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods. BIOINFORMATICS ADVANCES 2022; 2:vbac078. [PMID: 36699418 PMCID: PMC9710674 DOI: 10.1093/bioadv/vbac078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/13/2022] [Accepted: 10/21/2022] [Indexed: 11/05/2022]
Abstract
Motivation RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time. Results In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30 000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results. Availability and implementation The source codes of LinAliFold and CentroidLinAliFold are freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | - Michiaki Hamada
- Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 1698555, Japan,Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Tokyo 1698555, Japan
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28
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Zhang J, Fei Y, Sun L, Zhang QC. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat Methods 2022; 19:1193-1207. [PMID: 36203019 DOI: 10.1038/s41592-022-01623-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/09/2022]
Abstract
Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.
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Affiliation(s)
- Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China.,Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Yuhan Fei
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China.,Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Lei Sun
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China. .,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China. .,Tsinghua-Peking Center for Life Sciences, Beijing, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China. .,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China. .,Tsinghua-Peking Center for Life Sciences, Beijing, China.
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29
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Fei Y, Zhang H, Wang Y, Liu Z, Liu Y. LTPConstraint: a transfer learning based end-to-end method for RNA secondary structure prediction. BMC Bioinformatics 2022; 23:354. [PMID: 35999499 PMCID: PMC9396797 DOI: 10.1186/s12859-022-04847-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/18/2022] [Indexed: 11/26/2022] Open
Abstract
Background RNA secondary structure is very important for deciphering cell’s activity and disease occurrence. The first method which was used by the academics to predict this structure is biological experiment, But this method is too expensive, causing the promotion to be affected. Then, computing methods emerged, which has good efficiency and low cost. However, the accuracy of computing methods are not satisfactory. Many machine learning methods have also been applied to this area, but the accuracy has not improved significantly. Deep learning has matured and achieves great success in many areas such as computer vision and natural language processing. It uses neural network which is a kind of structure that has good functionality and versatility, but its effect is highly correlated with the quantity and quality of the data. At present, there is no model with high accuracy, low data dependence and high convenience in predicting RNA secondary structure. Results This paper designs a neural network called LTPConstraint to predict RNA secondary structure. The network is based on many network structure such as Bidirectional LSTM, Transformer and generator. It also uses transfer learning to train modelso that the data dependence can be reduced. Conclusions LTPConstraint has achieved high accuracy in RNA secondary structure prediction. Compared with the previous methods, the accuracy improves obviously both in predicting the structure with pseudoknot and the structure without pseudoknot. At the same time, LTPConstraint is easy to operate and can achieve result very quickly.
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Affiliation(s)
- Yinchao Fei
- College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China
| | - Hao Zhang
- College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China
| | - Yili Wang
- College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China
| | - Zhen Liu
- Graduate School of Engineering, Nagasaki Institute of Applied Science, Nagasaki, Japan
| | - Yuanning Liu
- College of Computer Science and Technology, Jilin University, Changchun, China. .,Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China.
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30
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Szikszai M, Wise M, Datta A, Ward M, Mathews DH. Deep learning models for RNA secondary structure prediction (probably) do not generalize across families. Bioinformatics 2022; 38:3892-3899. [PMID: 35748706 PMCID: PMC9364374 DOI: 10.1093/bioinformatics/btac415] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem. RESULTS We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family. AVAILABILITY AND IMPLEMENTATION Source code and data are available at https://github.com/marcellszi/dl-rna. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marcell Szikszai
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Michael Wise
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
- The Marshall Centre for Infectious Diseases Research and Training, The University of Western Australia, Perth, WA 6009, Australia
| | - Amitava Datta
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Max Ward
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - David H Mathews
- Department of Biochemistry & Biophysics, Center for RNA Biology, and Department of Biostatistics & Computational Biology, University of Rochester, Rochester, NY 14642, USA
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31
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Yang TH, Lin YC, Hsia M, Liao ZY. SSRTool: a web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability. Comput Struct Biotechnol J 2022; 20:2473-2483. [PMID: 35664227 PMCID: PMC9136272 DOI: 10.1016/j.csbj.2022.05.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 01/02/2023] Open
Abstract
RNA secondary structures can carry out essential cellular functions alone or interact with one another to form the hierarchical tertiary structures. Experimental structure identification approa ches can show the in vitro structures of RNA molecules. However, they usually have limits in the resolution and are costly. In silico structure prediction tools are thus primarily relied on for pre-experiment analysis. Various structure prediction models have been developed over the decades. Since these tools are usually used before knowing the actual RNA structures, evaluating and ranking the pile of secondary structure predictions of a given sequence is essential in computational analysis. In this research, we implemented a web service called SSRTool (RNA Secondary Structure prediction Ranking Tool) to assist in the ranking and evaluation of the generated predicted structures of a given sequence. Based on the computed species-specific interpretability significance in four common RNA structure–function aspects, SSRTool provides three functions along with visualization interfaces: (1) Rank user-generated predictions. (2) Provide an automated streamline of structure prediction and ranking for a given sequence. (3) Infer the functional aspects of a given structure. We demonstrated the applicability of SSRTool via real case studies and reported the similar trends between computed species-specific rankings and the corresponding prediction F1 values. The SSRTool web service is available online at https://cobisHSS0.im.nuk.edu.tw/SSRTool/, http://cosbi3.ee.ncku.edu.tw/SSRTool/, or the redirecting site https://github.com/cobisLab/SSRTool/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
- Corresponding author.
| | - Yu-Cian Lin
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Min Hsia
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Zhan-Yi Liao
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
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32
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Mao K, Wang J, Xiao Y. Length-Dependent Deep Learning Model for RNA Secondary Structure Prediction. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27031030. [PMID: 35164295 PMCID: PMC8838716 DOI: 10.3390/molecules27031030] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/17/2022] [Accepted: 01/29/2022] [Indexed: 11/16/2022]
Abstract
Deep learning methods for RNA secondary structure prediction have shown higher performance than traditional methods, but there is still much room to improve. It is known that the lengths of RNAs are very different, as are their secondary structures. However, the current deep learning methods all use length-independent models, so it is difficult for these models to learn very different secondary structures. Here, we propose a length-dependent model that is obtained by further training the length-independent model for different length ranges of RNAs through transfer learning. 2dRNA, a coupled deep learning neural network for RNA secondary structure prediction, is used to do this. Benchmarking shows that the length-dependent model performs better than the usual length-independent model.
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33
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Saman Booy M, Ilin A, Orponen P. RNA secondary structure prediction with convolutional neural networks. BMC Bioinformatics 2022; 23:58. [PMID: 35109787 PMCID: PMC8812003 DOI: 10.1186/s12859-021-04540-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 12/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete. RESULTS In this paper, we propose a simple, yet effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accuracy improvements over existing methods on two standard datasets, RNAStrAlign and ArchiveII, for 10 RNA families, where our experiments show excellent performance of the model across a wide range of sequence lengths. Since our matrix representation and post-processing approaches do not require the structures to be pseudoknot-free, we get similar good performance also for pseudoknotted structures. CONCLUSION We show how to use an artificial neural network design to predict the structure for a given RNA sequence with high accuracy only by learning from samples whose native structures have been experimentally characterized, independent of any energy model.
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Affiliation(s)
- Mehdi Saman Booy
- Department of Computer Science, Aalto University, Espoo, Finland.
| | - Alexander Ilin
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Pekka Orponen
- Department of Computer Science, Aalto University, Espoo, Finland
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34
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Tagashira M, Asai K. ConsAlifold: considering RNA structural alignments improves prediction accuracy of RNA consensus secondary structures. Bioinformatics 2022; 38:710-719. [PMID: 34694364 DOI: 10.1093/bioinformatics/btab738] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 08/24/2021] [Accepted: 10/20/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION By detecting homology among RNAs, the probabilistic consideration of RNA structural alignments has improved the prediction accuracy of significant RNA prediction problems. Predicting an RNA consensus secondary structure from an RNA sequence alignment is a fundamental research objective because in the detection of conserved base-pairings among RNA homologs, predicting an RNA consensus secondary structure is more convenient than predicting an RNA structural alignment. RESULTS We developed and implemented ConsAlifold, a dynamic programming-based method that predicts the consensus secondary structure of an RNA sequence alignment. ConsAlifold considers RNA structural alignments. ConsAlifold achieves moderate running time and the best prediction accuracy of RNA consensus secondary structures among available prediction methods. AVAILABILITY AND IMPLEMENTATION ConsAlifold, data and Python scripts for generating both figures and tables are freely available at https://github.com/heartsh/consalifold. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Masaki Tagashira
- Department of Computational Biology and Medical Sciences, University of Tokyo, Chiba 277-8561, Japan.,Artificial Intelligence Research Center, AIST, Tokyo 135-0064, Japan
| | - Kiyoshi Asai
- Department of Computational Biology and Medical Sciences, University of Tokyo, Chiba 277-8561, Japan.,Artificial Intelligence Research Center, AIST, Tokyo 135-0064, Japan
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35
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Winkler J, Urgese G, Ficarra E, Reinert K. LaRA 2: parallel and vectorized program for sequence-structure alignment of RNA sequences. BMC Bioinformatics 2022; 23:18. [PMID: 34991448 PMCID: PMC8734264 DOI: 10.1186/s12859-021-04532-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/13/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson-Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software. RESULTS We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version. CONCLUSIONS With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases.
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Affiliation(s)
- Jörg Winkler
- Department of Mathematics and Computer Science, Free University Berlin, Takustraße 9, 14195 Berlin, Germany
- Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin, Germany
| | - Gianvito Urgese
- Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Elisa Ficarra
- Department of Control and Computer Science, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Knut Reinert
- Department of Mathematics and Computer Science, Free University Berlin, Takustraße 9, 14195 Berlin, Germany
- Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin, Germany
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36
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Zhao J, Kennedy SD, Turner DH. Nuclear Magnetic Resonance Spectra and AMBER OL3 and ROC-RNA Simulations of UCUCGU Reveal Force Field Strengths and Weaknesses for Single-Stranded RNA. J Chem Theory Comput 2022; 18:1241-1254. [PMID: 34990548 DOI: 10.1021/acs.jctc.1c00643] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Single-stranded regions of RNA are important for folding of sequences into 3D structures and for design of therapeutics targeting RNA. Prediction of ensembles of 3D structures for single-stranded regions often involves classical mechanical approximations of interactions defined by quantum mechanical calculations on small model systems. Nuclear magnetic resonance (NMR) spectra and molecular dynamics (MD) simulations of short single strands provide tests for how well the approximations model many of the interactions. Here, the NMR spectra for UCUCGU at 2, 15, and 30 °C are compared to simulations with the AMBER force fields, OL3 and ROC-RNA. This is the first such comparison to an oligoribonucleotide containing an internal guanosine nucleotide (G). G is particularly interesting because of its many H-bonding groups, large dipole moment, and proclivity for both syn and anti conformations. Results reveal formation of a G amino to phosphate non-bridging oxygen H-bond. The results also demonstrate dramatic differences in details of the predicted structures. The variations emphasize the dependence of predictions on individual parameters and their balance with the rest of the force field. The NMR data can serve as a benchmark for future force fields.
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37
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Yang TH. An Aggregation Method to Identify the RNA Meta-Stable Secondary Structure and its Functionally Interpretable Structure Ensemble. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:75-86. [PMID: 34014829 DOI: 10.1109/tcbb.2021.3082396] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
RNA can provide vital cellular functions through its secondary or tertiary structure. Due to the low-throughput nature of experimental approaches, studies on RNA structures mainly resort to computational methods. However, current existing tools fail to consider RNA structure ensembles and do not provide ways to decipher functional hypotheses for the new predictions. In this research, a novel method was proposed to identify the functionally interpretable structure ensemble of a given RNA sequence and provide the meta-stable structure, or the most frequently observed functional RNA cellular conformation, based on the ensemble. In the prediction of meta-stable structures, the proposed method outperformed existing tools on a yeast test set. The inferred functional aspects were then manually checked and demonstrated a micro-averaging F1 value of 0.92. Further, a biological example of the yeast ASH1-E1 element was discussed to articulate that these functional aspects can also suggest testable hypotheses. Then the proposed method was verified to be well applicable to other species through a human test set. Finally, the proposed method was demonstrated to show resistance to sequence length-dependent performance deterioration.
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38
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Li S, Zhang H, Zhang L, Liu K, Liu B, Mathews DH, Huang L. LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2. Proc Natl Acad Sci U S A 2021; 118:e2116269118. [PMID: 34887342 PMCID: PMC8719904 DOI: 10.1073/pnas.2116269118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2021] [Indexed: 12/26/2022] Open
Abstract
The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.
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Affiliation(s)
- Sizhen Li
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331
| | - He Zhang
- Baidu Research, Sunnyvale, CA 94089
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331
| | - Liang Zhang
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331
- Baidu Research, Sunnyvale, CA 94089
| | - Kaibo Liu
- Baidu Research, Sunnyvale, CA 94089
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331
| | | | - David H Mathews
- Department of Biochemistry & Biophysics, University of Rochester Medical Center, Rochester, NY 14642;
- Center for RNA Biology, University of Rochester Medical Center, Rochester, NY 14642
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY 14642
| | - Liang Huang
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331;
- Baidu Research, Sunnyvale, CA 94089
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Fu L, Cao Y, Wu J, Peng Q, Nie Q, Xie X. UFold: fast and accurate RNA secondary structure prediction with deep learning. Nucleic Acids Res 2021; 50:e14. [PMID: 34792173 PMCID: PMC8860580 DOI: 10.1093/nar/gkab1074] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/15/2021] [Accepted: 10/19/2021] [Indexed: 11/13/2022] Open
Abstract
For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.
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Affiliation(s)
- Laiyi Fu
- Systems Engineering Institute, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.,Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Yingxin Cao
- Department of Computer Science, University of California, Irvine, CA 92697, USA.,Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA.,NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Jie Wu
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
| | - Qinke Peng
- Systems Engineering Institute, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, CA 92697, USA.,Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA.,NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Xiaohui Xie
- Department of Computer Science, University of California, Irvine, CA 92697, USA
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40
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Li S, Zhang H, Zhang L, Liu K, Liu B, Mathews DH, Huang L. LinearTurboFold: Linear-Time Global Prediction of Conserved Structures for RNA Homologs with Applications to SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.11.23.393488. [PMID: 34816262 PMCID: PMC8609897 DOI: 10.1101/2020.11.23.393488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length, and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt ) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurbo-Fold's purely in silico prediction not only is close to experimentally-guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' UTRs (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies novel conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, siRNAs, CRISPR-Cas13 guide RNAs and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies, and will be a useful tool in fighting the current and future pandemics. SIGNIFICANCE STATEMENT Conserved RNA structures are critical for designing diagnostic and therapeutic tools for many diseases including COVID-19. However, existing algorithms are much too slow to model the global structures of full-length RNA viral genomes. We present LinearTurboFold, a linear-time algorithm that is orders of magnitude faster, making it the first method to simultaneously fold and align whole genomes of SARS-CoV-2 variants, the longest known RNA virus (∼30 kilobases). Our work enables unprecedented global structural analysis and captures long-range interactions that are out of reach for existing algorithms but crucial for RNA functions. LinearTurboFold is a general technique for full-length genome studies and can help fight the current and future pandemics.
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Affiliation(s)
- Sizhen Li
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR
| | - He Zhang
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR
- Baidu Research, Sunnyvale, CA
| | - Liang Zhang
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR
- Baidu Research, Sunnyvale, CA
| | - Kaibo Liu
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR
- Baidu Research, Sunnyvale, CA
| | | | - David H. Mathews
- Department of Biochemistry & Biophysics, Center for RNA Biology, and Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY
| | - Liang Huang
- School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR
- Baidu Research, Sunnyvale, CA
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Woźniak T, Sajek M, Jaruzelska J, Sajek MP. RNAlign2D: a rapid method for combined RNA structure and sequence-based alignment using a pseudo-amino acid substitution matrix. BMC Bioinformatics 2021; 22:504. [PMID: 34656080 PMCID: PMC8520625 DOI: 10.1186/s12859-021-04426-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 10/05/2021] [Indexed: 11/15/2022] Open
Abstract
Background The functions of RNA molecules are mainly determined by their secondary structures. These functions can also be predicted using bioinformatic tools that enable the alignment of multiple RNAs to determine functional domains and/or classify RNA molecules into RNA families. However, the existing multiple RNA alignment tools, which use structural information, are slow in aligning long molecules and/or a large number of molecules. Therefore, a more rapid tool for multiple RNA alignment may improve the classification of known RNAs and help to reveal the functions of newly discovered RNAs. Results Here, we introduce an extremely fast Python-based tool called RNAlign2D. It converts RNA sequences to pseudo-amino acid sequences, which incorporate structural information, and uses a customizable scoring matrix to align these RNA molecules via the multiple protein sequence alignment tool MUSCLE. Conclusions RNAlign2D produces accurate RNA alignments in a very short time. The pseudo-amino acid substitution matrix approach utilized in RNAlign2D is applicable for virtually all protein aligners. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04426-8.
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Affiliation(s)
- Tomasz Woźniak
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszyńska 32, 60-479, Poznań, Poland
| | - Małgorzata Sajek
- Department of Human Molecular Genetics, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznańskiego 6, 61-614, Poznań, Poland
| | - Jadwiga Jaruzelska
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszyńska 32, 60-479, Poznań, Poland
| | - Marcin Piotr Sajek
- Institute of Human Genetics, Polish Academy of Sciences, Strzeszyńska 32, 60-479, Poznań, Poland. .,RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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42
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Sanbonmatsu K. Getting to the bottom of lncRNA mechanism: structure-function relationships. Mamm Genome 2021; 33:343-353. [PMID: 34642784 PMCID: PMC8509902 DOI: 10.1007/s00335-021-09924-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/28/2021] [Indexed: 12/14/2022]
Abstract
While long non-coding RNAs are known to play key roles in disease and development, relatively few structural studies have been performed for this important class of RNAs. Here, we review functional studies of long non-coding RNAs and expose the need for high-resolution 3-D structural studies, discussing the roles of long non-coding RNAs in the cell and how structure–function relationships might be used to elucidate further understanding. We then describe structural studies of other classes of RNAs using chemical probing, nuclear magnetic resonance, small-angle X-ray scattering, X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Next, we review early structural studies of long non-coding RNAs to date and describe the way forward for the structural biology of long non-coding RNAs in terms of cryo-EM.
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43
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Hanumanthappa AK, Singh J, Paliwal K, Singh J, Zhou Y. Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network. Bioinformatics 2021; 36:5169-5176. [PMID: 33106872 DOI: 10.1093/bioinformatics/btaa652] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 06/30/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition. RESULTS Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs. AVAILABILITY AND IMPLEMENTATION Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anil Kumar Hanumanthappa
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Jaspreet Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
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44
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Zhao Q, Zhao Z, Fan X, Yuan Z, Mao Q, Yao Y. Review of machine learning methods for RNA secondary structure prediction. PLoS Comput Biol 2021; 17:e1009291. [PMID: 34437528 PMCID: PMC8389396 DOI: 10.1371/journal.pcbi.1009291] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.
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Affiliation(s)
- Qi Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Zheng Zhao
- School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China
| | - Xiaoya Fan
- School of Software, Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, Liaoning, China
| | - Zhengwei Yuan
- Key Laboratory of Health Ministry for Congenital Malformation, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Qian Mao
- College of Light Industry, Liaoning University, Shenyang, Liaoning, China
- Key Laboratory of Agroproducts Processing Technology, Changchun University, Changchun, Jilin, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, New Jersey, United States of America
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45
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Learning the Fastest RNA Folding Path Based on Reinforcement Learning and Monte Carlo Tree Search. Molecules 2021; 26:molecules26154420. [PMID: 34361572 PMCID: PMC8347524 DOI: 10.3390/molecules26154420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/17/2021] [Accepted: 07/20/2021] [Indexed: 11/17/2022] Open
Abstract
RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem. The folding problem includes two aspects: structure prediction and folding mechanism. Although the former has been widely studied, the latter is still not well understood. Here we present a deep reinforcement learning algorithms 2dRNA-Fold to study the fastest folding paths of RNA secondary structure. 2dRNA-Fold uses a neural network combined with Monte Carlo tree search to select residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. We apply 2dRNA-Fold to several short RNA molecules and one longer RNA 1Y26 and find that their fastest folding paths show some interesting features. 2dRNA-Fold is further trained using a set of RNA molecules from the dataset bpRNA and is used to predict RNA secondary structure. Since in 2dRNA-Fold the scoring to determine next step is based on possible base pairings, the learned or predicted fastest folding path may not agree with the actual folding paths determined by free energy according to physical laws.
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Singh J, Paliwal K, Zhang T, Singh J, Litfin T, Zhou Y. Improved RNA Secondary Structure and Tertiary Base-pairing Prediction Using Evolutionary Profile, Mutational Coupling and Two-dimensional Transfer Learning. Bioinformatics 2021; 37:2589-2600. [PMID: 33704363 DOI: 10.1093/bioinformatics/btab165] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/05/2021] [Accepted: 03/08/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling. RESULTS The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, noncanonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences. AVAILABILITY Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.
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Affiliation(s)
- Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Tongchuan Zhang
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Jaspreet Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Thomas Litfin
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
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47
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Sato K, Akiyama M, Sakakibara Y. RNA secondary structure prediction using deep learning with thermodynamic integration. Nat Commun 2021; 12:941. [PMID: 33574226 PMCID: PMC7878809 DOI: 10.1038/s41467-021-21194-4] [Citation(s) in RCA: 128] [Impact Index Per Article: 42.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/15/2021] [Indexed: 12/23/2022] Open
Abstract
Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure. Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms.
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Affiliation(s)
- Kengo Sato
- Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan.
| | - Manato Akiyama
- Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan
| | - Yasubumi Sakakibara
- Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan
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48
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Wang Y, Liu Y, Wang S, Liu Z, Gao Y, Zhang H, Dong L. ATTfold: RNA Secondary Structure Prediction With Pseudoknots Based on Attention Mechanism. Front Genet 2020; 11:612086. [PMID: 33384721 PMCID: PMC7770172 DOI: 10.3389/fgene.2020.612086] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 11/17/2020] [Indexed: 11/15/2022] Open
Abstract
Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.
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Affiliation(s)
- Yili Wang
- College of Software, Jilin University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Yuanning Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Shuo Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Zhen Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
- Graduate School of Engineering, Nagasaki Institute of Applied Science, Nagasaki, Japan
| | - Yubing Gao
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Hao Zhang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Liyan Dong
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
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49
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Huynh N, Depner N, Larson R, King-Jones K. A versatile toolkit for CRISPR-Cas13-based RNA manipulation in Drosophila. Genome Biol 2020; 21:279. [PMID: 33203452 PMCID: PMC7670108 DOI: 10.1186/s13059-020-02193-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 11/04/2020] [Indexed: 02/08/2023] Open
Abstract
Advances in CRISPR technology have immensely improved our ability to manipulate nucleic acids, and the recent discovery of the RNA-targeting endonuclease Cas13 adds even further functionality. Here, we show that Cas13 works efficiently in Drosophila, both ex vivo and in vivo. We test 44 different Cas13 variants to identify enzymes with the best overall performance and show that Cas13 could target endogenous Drosophila transcripts in vivo with high efficiency and specificity. We also develop Cas13 applications to edit mRNAs and target mitochondrial transcripts. Our vector collection represents a versatile tool collection to manipulate gene expression at the post-transcriptional level.
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Affiliation(s)
- Nhan Huynh
- Department of Biological Sciences, University of Alberta, G-504 Biological Sciences Bldg., Edmonton, Alberta, T6G 2E9, Canada
| | - Noah Depner
- Department of Biological Sciences, University of Alberta, G-504 Biological Sciences Bldg., Edmonton, Alberta, T6G 2E9, Canada
| | - Raegan Larson
- Department of Biological Sciences, University of Alberta, G-504 Biological Sciences Bldg., Edmonton, Alberta, T6G 2E9, Canada
| | - Kirst King-Jones
- Department of Biological Sciences, University of Alberta, G-504 Biological Sciences Bldg., Edmonton, Alberta, T6G 2E9, Canada.
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Bossanyi MA, Carpentier V, Glouzon JPS, Ouangraoua A, Anselmetti Y. aliFreeFoldMulti: alignment-free method to predict secondary structures of multiple RNA homologs. NAR Genom Bioinform 2020; 2:lqaa086. [PMID: 33575631 PMCID: PMC7671329 DOI: 10.1093/nargab/lqaa086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 10/19/2020] [Indexed: 11/18/2022] Open
Abstract
Predicting RNA structure is crucial for understanding RNA’s mechanism of action. Comparative approaches for the prediction of RNA structures can be classified into four main strategies. The three first—align-and-fold, align-then-fold and fold-then-align—exploit multiple sequence alignments to improve the accuracy of conserved RNA-structure prediction. Align-and-fold methods perform generally better, but are also typically slower than the other alignment-based methods. The fourth strategy—alignment-free—consists in predicting the conserved RNA structure without relying on sequence alignment. This strategy has the advantage of being the faster, while predicting accurate structures through the use of latent representations of the candidate structures for each sequence. This paper presents aliFreeFoldMulti, an extension of the aliFreeFold algorithm. This algorithm predicts a representative secondary structure of multiple RNA homologs by using a vector representation of their suboptimal structures. aliFreeFoldMulti improves on aliFreeFold by additionally computing the conserved structure for each sequence. aliFreeFoldMulti is assessed by comparing its prediction performance and time efficiency with a set of leading RNA-structure prediction methods. aliFreeFoldMulti has the lowest computing times and the highest maximum accuracy scores. It achieves comparable average structure prediction accuracy as other methods, except TurboFoldII which is the best in terms of average accuracy but with the highest computing times. We present aliFreeFoldMulti as an illustration of the potential of alignment-free approaches to provide fast and accurate RNA-structure prediction methods.
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Affiliation(s)
- Marc-André Bossanyi
- CoBIUS lab, Department of Computer Science, University of Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC J1K 2R1, Canada
| | - Valentin Carpentier
- CoBIUS lab, Department of Computer Science, University of Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC J1K 2R1, Canada
| | - Jean-Pierre S Glouzon
- CoBIUS lab, Department of Computer Science, University of Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC J1K 2R1, Canada
| | - Aïda Ouangraoua
- CoBIUS lab, Department of Computer Science, University of Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC J1K 2R1, Canada
| | - Yoann Anselmetti
- CoBIUS lab, Department of Computer Science, University of Sherbrooke, 2500 Boulevard de l’Université, Sherbrooke, QC J1K 2R1, Canada
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