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Raden M, Miladi M. How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA. Methods Mol Biol 2024; 2726:209-234. [PMID: 38780733 DOI: 10.1007/978-1-0716-3519-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.
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Affiliation(s)
- Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
| | - Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
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2
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Wallach T, Raden M, Hinkelmann L, Brehm M, Rabsch D, Weidling H, Krüger C, Kettenmann H, Backofen R, Lehnardt S. Distinct SARS-CoV-2 RNA fragments activate Toll-like receptors 7 and 8 and induce cytokine release from human macrophages and microglia. Front Immunol 2023; 13:1066456. [PMID: 36713399 PMCID: PMC9880480 DOI: 10.3389/fimmu.2022.1066456] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/23/2022] [Indexed: 01/14/2023] Open
Abstract
Introduction The pandemic coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is marked by thromboembolic events and an inflammatory response throughout the body, including the brain. Methods Employing the machine learning approach BrainDead we systematically screened for SARS-CoV-2 genome-derived single-stranded (ss) RNA fragments with high potential to activate the viral RNA-sensing innate immune receptors Toll-like receptor (TLR)7 and/or TLR8. Analyzing HEK TLR7/8 reporter cells we tested such RNA fragments with respect to their potential to induce activation of human TLR7 and TLR8 and to activate human macrophages, as well as iPSC-derived human microglia, the resident immune cells in the brain. Results We experimentally validated several sequence-specific RNA fragment candidates out of the SARS-CoV-2 RNA fragments predicted in silico as activators of human TLR7 and TLR8. Moreover, these SARS-CoV-2 ssRNAs induced cytokine release from human macrophages and iPSC-derived human microglia in a sequence- and species-specific fashion. Discussion Our findings determine TLR7 and TLR8 as key sensors of SARS-CoV-2-derived ssRNAs and may deepen our understanding of the mechanisms how this virus triggers, but also modulates an inflammatory response through innate immune signaling.
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Affiliation(s)
- Thomas Wallach
- Institute of Cell Biology and Neurobiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Martin Raden
- Bioinformatics, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Lukas Hinkelmann
- Institute of Cell Biology and Neurobiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Mariam Brehm
- Institute of Cell Biology and Neurobiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Dominik Rabsch
- Bioinformatics, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Hannah Weidling
- Institute of Cell Biology and Neurobiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany,Cellular Neuroscience, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Christina Krüger
- Institute of Cell Biology and Neurobiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Helmut Kettenmann
- Cellular Neuroscience, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Rolf Backofen
- Bioinformatics, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany,Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany,*Correspondence: Seija Lehnardt, ; Rolf Backofen,
| | - Seija Lehnardt
- Institute of Cell Biology and Neurobiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany,Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany,*Correspondence: Seija Lehnardt, ; Rolf Backofen,
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3
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Wallach T, Mossmann ZJ, Szczepek M, Wetzel M, Machado R, Raden M, Miladi M, Kleinau G, Krüger C, Dembny P, Adler D, Zhai Y, Kumbol V, Dzaye O, Schüler J, Futschik M, Backofen R, Scheerer P, Lehnardt S. MicroRNA-100-5p and microRNA-298-5p released from apoptotic cortical neurons are endogenous Toll-like receptor 7/8 ligands that contribute to neurodegeneration. Mol Neurodegener 2021; 16:80. [PMID: 34838071 PMCID: PMC8626928 DOI: 10.1186/s13024-021-00498-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 11/03/2021] [Indexed: 12/30/2022] Open
Abstract
Background MicroRNA (miRNA) expression in the brain is altered in neurodegenerative diseases. Recent studies demonstrated that selected miRNAs conventionally regulating gene expression at the post-transcriptional level can act extracellularly as signaling molecules. The identity of miRNA species serving as membrane receptor ligands involved in neuronal apoptosis in the central nervous system (CNS), as well as the miRNAs’ sequence and structure required for this mode of action remained largely unresolved. Methods Using a microarray-based screening approach we analyzed apoptotic cortical neurons of C56BL/6 mice and their supernatant with respect to alterations in miRNA expression/presence. HEK-Blue Toll-like receptor (TLR) 7/8 reporter cells, primary microglia and macrophages derived from human and mouse were employed to test the potential of the identified miRNAs released from apoptotic neurons to serve as signaling molecules for the RNA-sensing receptors. Biophysical and bioinformatical approaches, as well as immunoassays and sequential microscopy were used to analyze the interaction between candidate miRNA and TLR. Immunocytochemical and -histochemical analyses of murine CNS cultures and adult mice intrathecally injected with miRNAs, respectively, were performed to evaluate the impact of miRNA-induced TLR activation on neuronal survival and microglial activation. Results We identified a specific pattern of miRNAs released from apoptotic cortical neurons that activate TLR7 and/or TLR8, depending on sequence and species. Exposure of microglia and macrophages to certain miRNA classes released from apoptotic neurons resulted in the sequence-specific production of distinct cytokines/chemokines and increased phagocytic activity. Out of those miRNAs miR-100-5p and miR-298-5p, which have consistently been linked to neurodegenerative diseases, entered microglia, located to their endosomes, and directly bound to human TLR8. The miRNA-TLR interaction required novel sequence features, but no specific structure formation of mature miRNA. As a consequence of miR-100-5p- and miR-298-5p-induced TLR activation, cortical neurons underwent cell-autonomous apoptosis. Presence of miR-100-5p and miR-298-5p in cerebrospinal fluid led to neurodegeneration and microglial accumulation in the murine cerebral cortex through TLR7 signaling. Conclusion Our data demonstrate that specific miRNAs are released from apoptotic cortical neurons, serve as endogenous TLR7/8 ligands, and thereby trigger further neuronal apoptosis in the CNS. Our findings underline the recently discovered role of miRNAs as extracellular signaling molecules, particularly in the context of neurodegeneration. Supplementary Information The online version contains supplementary material available at 10.1186/s13024-021-00498-5.
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Affiliation(s)
- Thomas Wallach
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany.
| | - Zoé J Mossmann
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Michal Szczepek
- Institute for Medical Physics and Biophysics, Group Protein X-ray Crystallography & Signal Transduction, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Max Wetzel
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Rui Machado
- Department of Biomedical Sciences and Medicine, University of Algarve, 8005-139, Faro, Portugal
| | - Martin Raden
- Bioinformatics, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Milad Miladi
- Bioinformatics, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Gunnar Kleinau
- Institute for Medical Physics and Biophysics, Group Protein X-ray Crystallography & Signal Transduction, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Christina Krüger
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Paul Dembny
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Drew Adler
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Yuanyuan Zhai
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Victor Kumbol
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Omar Dzaye
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Jutta Schüler
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany
| | - Matthias Futschik
- Department of Biomedical Sciences and Medicine, University of Algarve, 8005-139, Faro, Portugal.,School of Biomedical Sciences, Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK.,MRC London Institute of Medical Sciences (LMS), Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Rolf Backofen
- Bioinformatics, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Patrick Scheerer
- Institute for Medical Physics and Biophysics, Group Protein X-ray Crystallography & Signal Transduction, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany.,German Centre for Cardiovascular Research, partner site Berlin, Berlin, Germany
| | - Seija Lehnardt
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany. .,Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117, Berlin, Germany.
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Alkhnbashi OS, Mitrofanov A, Bonidia R, Raden M, Tran V, Eggenhofer F, Shah S, Öztürk E, Padilha V, Sanches D, de Carvalho A, Backofen R. CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems. Nucleic Acids Res 2021; 49:W125-W130. [PMID: 34133710 PMCID: PMC8265192 DOI: 10.1093/nar/gkab456] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/28/2021] [Accepted: 05/17/2021] [Indexed: 11/17/2022] Open
Abstract
CRISPR–Cas systems are adaptive immune systems in prokaryotes, providing resistance against invading viruses and plasmids. The identification of CRISPR loci is currently a non-standardized, ambiguous process, requiring the manual combination of multiple tools, where existing tools detect only parts of the CRISPR-systems, and lack quality control, annotation and assessment capabilities of the detected CRISPR loci. Our CRISPRloci server provides the first resource for the prediction and assessment of all possible CRISPR loci. The server integrates a series of advanced Machine Learning tools within a seamless web interface featuring: (i) prediction of all CRISPR arrays in the correct orientation; (ii) definition of CRISPR leaders for each locus; and (iii) annotation of cas genes and their unambiguous classification. As a result, CRISPRloci is able to accurately determine the CRISPR array and associated information, such as: the Cas subtypes; cassette boundaries; accuracy of the repeat structure, orientation and leader sequence; virus-host interactions; self-targeting; as well as the annotation of cas genes, all of which have been missing from existing tools. This annotation is presented in an interactive interface, making it easy for scientists to gain an overview of the CRISPR system in their organism of interest. Predictions are also rendered in GFF format, enabling in-depth genome browser inspection. In summary, CRISPRloci constitutes a full suite for CRISPR–Cas system characterization that offers annotation quality previously available only after manual inspection.
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Affiliation(s)
- Omer S Alkhnbashi
- To whom correspondence should be addressed. Tel: +49 761 2037460; Fax: +49 761 2037462;
| | | | | | - Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Van Dinh Tran
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Florian Eggenhofer
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Shiraz A Shah
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Denmark
| | - Ekrem Öztürk
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Victor A Padilha
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil
| | - Danilo S Sanches
- Universidade Tecnológica Federal do Paraná, Campus Cornélio Procópio, 86300000 Cornélio Procópio, PR, Brazil
| | | | - Rolf Backofen
- Correspondence may also be addressed to Rolf Backofen.
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5
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Raden M, Wallach T, Miladi M, Zhai Y, Krüger C, Mossmann ZJ, Dembny P, Backofen R, Lehnardt S. Structure-aware machine learning identifies microRNAs operating as Toll-like receptor 7/8 ligands. RNA Biol 2021; 18:268-277. [PMID: 34241565 PMCID: PMC8677043 DOI: 10.1080/15476286.2021.1940697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MicroRNAs (miRNAs) can serve as activation signals for membrane receptors, a recently discovered function that is independent of the miRNAs’ conventional role in post-transcriptional gene regulation. Here, we introduce a machine learning approach, BrainDead, to identify oligonucleotides that act as ligands for single-stranded RNA-detecting Toll-like receptors (TLR)7/8, thereby triggering an immune response. BrainDead was trained on activation data obtained from in vitro experiments on murine microglia, incorporating sequence and intra-molecular structure, as well as inter-molecular homo-dimerization potential of candidate RNAs. The method was applied to analyse all known human miRNAs regarding their potential to induce TLR7/8 signalling and microglia activation. We validated the predicted functional activity of subsets of high- and low-scoring miRNAs experimentally, of which a selection has been linked to Alzheimer’s disease. High agreement between predictions and experiments confirms the robustness and power of BrainDead. The results provide new insight into the mechanisms of how miRNAs act as TLR ligands. Eventually, BrainDead implements a generic machine learning methodology for learning and predicting the functions of short RNAs in any context.
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Affiliation(s)
- Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Thomas Wallach
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Yuanyuan Zhai
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Christina Krüger
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Zoé J Mossmann
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Paul Dembny
- Institute of Cell Biology and Neurobiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.,Signalling Research Centre CIBSS, University of Freiburg, Freiburg, Germany
| | - Seija Lehnardt
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
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6
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Parise MTD, Parise D, Aburjaile FF, Pinto Gomide AC, Kato RB, Raden M, Backofen R, Azevedo VADC, Baumbach J. An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria. Front Microbiol 2021; 12:656435. [PMID: 34220744 PMCID: PMC8247434 DOI: 10.3389/fmicb.2021.656435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/19/2021] [Indexed: 12/02/2022] Open
Abstract
Small RNAs (sRNAs) are one of the key players in the post-transcriptional regulation of bacterial gene expression. These molecules, together with transcription factors, form regulatory networks and greatly influence the bacterial regulatory landscape. Little is known concerning sRNAs and their influence on the regulatory machinery in the genus Corynebacterium, despite its medical, veterinary and biotechnological importance. Here, we expand corynebacterial regulatory knowledge by integrating sRNAs and their regulatory interactions into the transcriptional regulatory networks of six corynebacterial species, covering four human and animal pathogens, and integrate this data into the CoryneRegNet database. To this end, we predicted sRNAs to regulate 754 genes, including 206 transcription factors, in corynebacterial gene regulatory networks. Amongst them, the sRNA Cd-NCTC13129-sRNA-2 is predicted to directly regulate ydfH, which indirectly regulates 66 genes, including the global regulator glxR in C. diphtheriae. All of the sRNA-enriched regulatory networks of the genus Corynebacterium have been made publicly available in the newest release of CoryneRegNet(www.exbio.wzw.tum.de/coryneregnet/) to aid in providing valuable insights and to guide future experiments.
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Affiliation(s)
- Mariana Teixeira Dornelles Parise
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.,Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Doglas Parise
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.,Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Rodrigo Bentes Kato
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Martin Raden
- Bioinformatics, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Rolf Backofen
- Bioinformatics, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | | | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.,Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.,Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
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7
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Miladi M, Raden M, Will S, Backofen R. Fast and accurate structure probability estimation for simultaneous alignment and folding of RNAs with Markov chains. Algorithms Mol Biol 2020; 15:19. [PMID: 33292340 PMCID: PMC7666477 DOI: 10.1186/s13015-020-00179-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 10/16/2020] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Simultaneous alignment and folding (SA&F) of RNAs is the indispensable gold standard for inferring the structure of non-coding RNAs and their general analysis. The original algorithm, proposed by Sankoff, solves the theoretical problem exactly with a complexity of [Formula: see text] in the full energy model. Over the last two decades, several variants and improvements of the Sankoff algorithm have been proposed to reduce its extreme complexity by proposing simplified energy models or imposing restrictions on the predicted alignments. RESULTS Here, we introduce a novel variant of Sankoff's algorithm that reconciles the simplifications of PMcomp, namely moving from the full energy model to a simpler base pair-based model, with the accuracy of the loop-based full energy model. Instead of estimating pseudo-energies from unconditional base pair probabilities, our model calculates energies from conditional base pair probabilities that allow to accurately capture structure probabilities, which obey a conditional dependency. This model gives rise to the fast and highly accurate novel algorithm Pankov (Probabilistic Sankoff-like simultaneous alignment and folding of RNAs inspired by Markov chains). CONCLUSIONS Pankov benefits from the speed-up of excluding unreliable base-pairing without compromising the loop-based free energy model of the Sankoff's algorithm. We show that Pankov outperforms its predecessors LocARNA and SPARSE in folding quality and is faster than LocARNA.
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Affiliation(s)
- Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Sebastian Will
- Theoretical Biochemistry Group (TBI), Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, Vienna, Austria
- Bioinformatics group (AMIBIO), Laboratoire d’Informatique de l’École Polytechnique (LIX), Institut Polytechnique de Paris (IPP), Batiment Turing, 1 rue d’Estienne d’Orve, Palaiseau, France
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schänzlestr. 18, Freiburg, Germany
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Miladi M, Raden M, Diederichs S, Backofen R. MutaRNA: analysis and visualization of mutation-induced changes in RNA structure. Nucleic Acids Res 2020; 48:W287-W291. [PMID: 32392303 PMCID: PMC7319544 DOI: 10.1093/nar/gkaa331] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/14/2020] [Accepted: 04/22/2020] [Indexed: 12/25/2022] Open
Abstract
RNA molecules fold into complex structures as a result of intramolecular interactions between their nucleotides. The function of many non-coding RNAs and some cis-regulatory elements of messenger RNAs highly depends on their fold. Single-nucleotide variants (SNVs) and other types of mutations can disrupt the native function of an RNA element by altering its base pairing pattern. Identifying the effect of a mutation on an RNA’s structure is, therefore, a crucial step in evaluating the impact of mutations on the post-transcriptional regulation and function of RNAs within the cell. Even though a single nucleotide variation can have striking impacts on the structure formation, interpreting and comparing the impact usually needs expertise and meticulous efforts. Here, we present MutaRNA, a web server for visualization and interpretation of mutation-induced changes on the RNA structure in an intuitive and integrative fashion. To this end, probabilities of base pairing and position-wise unpaired probabilities of wildtype and mutated RNA sequences are computed and compared. Differential heatmap-like dot plot representations in combination with circular plots and arc diagrams help to identify local structure abberations, which are otherwise hidden in standard outputs. Eventually, MutaRNA provides a comprehensive and comparative overview of the mutation-induced changes in base pairing potentials and accessibility. The MutaRNA web server is freely available at http://rna.informatik.uni-freiburg.de/MutaRNA.
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Affiliation(s)
- Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Sven Diederichs
- Division of Cancer Research, Department of Thoracic Surgery, Faculty of Medicine, German Cancer Consortium (DKTK), University of Freiburg, 79085 Freiburg, Germany.,Division of RNA Biology and Cancer, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
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Entzian G, Raden M. pourRNA-a time- and memory-efficient approach for the guided exploration of RNA energy landscapes. Bioinformatics 2020; 36:462-469. [PMID: 31350881 DOI: 10.1093/bioinformatics/btz583] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 06/25/2019] [Accepted: 07/22/2019] [Indexed: 01/03/2023] Open
Abstract
MOTIVATION The folding dynamics of ribonucleic acids (RNAs) are typically studied via coarse-grained models of the underlying energy landscape to face the exponential growths of the RNA secondary structure space. Still, studies of exact folding kinetics based on gradient basin abstractions are currently limited to short sequence lengths due to vast memory requirements. In order to compute exact transition rates between gradient basins, state-of-the-art approaches apply global flooding schemes that require to memorize the whole structure space at once. pourRNA tackles this problem via local flooding techniques where memorization is limited to the structure ensembles of individual gradient basins. RESULTS Compared to the only available tool for exact gradient basin-based macro-state transition rates (namely barriers), pourRNA computes the same exact transition rates up to 10 times faster and requires two orders of magnitude less memory for sequences that are still computationally accessible for exhaustive enumeration. Parallelized computation as well as additional heuristics further speed up computations while still producing high-quality transition model approximations. The introduced heuristics enable a guided trade-off between model quality and required computational resources. We introduce and evaluate a macroscopic direct path heuristics to efficiently compute refolding energy barrier estimations for the co-transcriptionally trapped RNA sv11 of length 115 nt. Finally, we also show how pourRNA can be used to identify folding funnels and their respective energetically lowest minima. AVAILABILITY AND IMPLEMENTATION pourRNA is freely available at https://github.com/ViennaRNA/pourRNA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gregor Entzian
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
| | - Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg 79110, Germany
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Miladi M, Montaseri S, Backofen R, Raden M. Integration of accessibility data from structure probing into RNA-RNA interaction prediction. Bioinformatics 2020; 35:2862-2864. [PMID: 30590479 PMCID: PMC6691327 DOI: 10.1093/bioinformatics/bty1029] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 11/20/2018] [Accepted: 12/18/2018] [Indexed: 12/12/2022] Open
Abstract
SUMMARY Experimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided e.g. by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA-RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here, we show how such experimental data can be incorporated seamlessly into accessibility-based RNA-RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA-RNA interaction prediction. We evaluate our approach by investigating interactions of a spliceosomal U1 snRNA transcript with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity. AVAILABILITY AND IMPLEMENTATION https://github.com/BackofenLab/IntaRNA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Milad Miladi
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany
| | - Soheila Montaseri
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany
| | - Rolf Backofen
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany.,Center for Biological Signaling Studies (BIOSS), University of Freiburg, Freiburg D-79104, Germany
| | - Martin Raden
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany
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11
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Abstract
Summary SHAPE experiments are used to probe the structure of RNA molecules. We present ShaKer to predict SHAPE data for RNA using a graph-kernel-based machine learning approach that is trained on experimental SHAPE information. While other available methods require a manually curated reference structure, ShaKer predicts reactivity data based on sequence input only and by sampling the ensemble of possible structures. Thus, ShaKer is well placed to enable experiment-driven, transcriptome-wide SHAPE data prediction to enable the study of RNA structuredness and to improve RNA structure and RNA–RNA interaction prediction. For performance evaluation, we use accuracy and accessibility comparing to experimental SHAPE data and competing methods. We can show that Shaker outperforms its competitors and is able to predict high quality SHAPE annotations even when no reference structure is provided. Availability and implementation ShaKer is freely available at https://github.com/BackofenLab/ShaKer.
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Affiliation(s)
- Stefan Mautner
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Soheila Montaseri
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Fabrizio Costa
- Department Computer Science, University of Exeter, Exeter, UK
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
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Raden M, Gutmann F, Uhl M, Backofen R. CopomuS-Ranking Compensatory Mutations to Guide RNA-RNA Interaction Verification Experiments. Int J Mol Sci 2020; 21:ijms21113852. [PMID: 32481751 PMCID: PMC7311995 DOI: 10.3390/ijms21113852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/18/2020] [Accepted: 05/25/2020] [Indexed: 11/16/2022] Open
Abstract
In silico RNA-RNA interaction prediction is widely applied to identify putative interaction partners and to assess interaction details in base pair resolution. To verify specific interactions, in vitro evidence can be obtained via compensatory mutation experiments. Unfortunately, the selection of compensatory mutations is non-trivial and typically based on subjective ad hoc decisions. To support the decision process, we introduce our COmPensatOry MUtation Selector CopomuS. CopomuS evaluates the effects of mutations on RNA-RNA interaction formation using a set of objective criteria, and outputs a reliable ranking of compensatory mutation candidates. For RNA-RNA interaction assessment, the state-of-the-art IntaRNA prediction tool is applied. We investigate characteristics of successfully verified RNA-RNA interactions from the literature, which guided the design of CopomuS. Finally, we evaluate its performance based on experimentally validated compensatory mutations of prokaryotic sRNAs and their target mRNAs. CopomuS predictions highly agree with known results, making it a valuable tool to support the design of verification experiments for RNA-RNA interactions. It is part of the IntaRNA package and available as stand-alone webserver for ad hoc application.
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Affiliation(s)
- Martin Raden
- Bioinformatics, Department of Computer Science, University Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany; (F.G.); (M.U.); (R.B.)
- Correspondence:
| | - Fabio Gutmann
- Bioinformatics, Department of Computer Science, University Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany; (F.G.); (M.U.); (R.B.)
| | - Michael Uhl
- Bioinformatics, Department of Computer Science, University Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany; (F.G.); (M.U.); (R.B.)
| | - Rolf Backofen
- Bioinformatics, Department of Computer Science, University Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany; (F.G.); (M.U.); (R.B.)
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
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Raden M, Müller T, Mautner S, Gelhausen R, Backofen R. The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment. BMC Bioinformatics 2020; 21:15. [PMID: 31931703 PMCID: PMC6956497 DOI: 10.1186/s12859-019-3143-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 10/09/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Seed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results. RESULTS Here, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations. CONCLUSIONS This provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches.We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction.
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Affiliation(s)
- Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, Freiburg, 79110, Germany.
| | - Teresa Müller
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, Freiburg, 79110, Germany
| | - Stefan Mautner
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, Freiburg, 79110, Germany
| | - Rick Gelhausen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, Freiburg, 79110, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, Freiburg, 79110, Germany.,Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, Freiburg, 79104, Germany
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14
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Gelhausen R, Will S, Hofacker IL, Backofen R, Raden M. IntaRNAhelix-composing RNA–RNA interactions from stable inter-molecular helices boosts bacterial sRNA target prediction. J Bioinform Comput Biol 2019; 17:1940009. [DOI: 10.1142/s0219720019400092] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Efficient computational tools for the identification of putative target RNAs regulated by prokaryotic sRNAs rely on thermodynamic models of RNA secondary structures. While they typically predict RNA–RNA interaction complexes accurately, they yield many highly-ranked false positives in target screens. One obvious source of this low specificity appears to be the disability of current secondary-structure-based models to reflect steric constraints, which nevertheless govern the kinetic formation of RNA–RNA interactions. For example, often — even thermodynamically favorable — extensions of short initial kissing hairpin interactions are kinetically prohibited, since this would require unwinding of intra-molecular helices as well as sterically impossible bending of the interaction helix. Another source is the consideration of instable and thus unlikely subinteractions that enable better scoring of longer interactions. In consequence, the efficient prediction methods that do not consider such effects show a high false positive rate. To increase the prediction accuracy we devise IntaRNAhelix, a dynamic programming algorithm that length-restricts the runs of consecutive inter-molecular base pairs (perfect canonical stackings), which we hypothesize to implicitly model the steric and kinetic effects. The novel method is implemented by extending the state-of-the-art tool IntaRNA. Our comprehensive bacterial sRNA target prediction benchmark demonstrates significant improvements of the prediction accuracy and enables more than 40-times faster computations. These results indicate — supporting our hypothesis — that stable helix composition increases the accuracy of interaction prediction models compared to the current state-of-the-art approach.
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Affiliation(s)
- Rick Gelhausen
- Bioinformatics Group, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Sebastian Will
- Institute for Theoretical Chemistry, University of Vienna, Waehringer Strasse 17, 1090 Wien, Austria
| | - Ivo L. Hofacker
- Institute for Theoretical Chemistry, University of Vienna, Waehringer Strasse 17, 1090 Wien, Austria
| | - Rolf Backofen
- Bioinformatics Group, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
| | - Martin Raden
- Bioinformatics Group, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
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Raden M, Mohamed MM, Ali SM, Backofen R. Interactive implementations of thermodynamics-based RNA structure and RNA-RNA interaction prediction approaches for example-driven teaching. PLoS Comput Biol 2018; 14:e1006341. [PMID: 30161123 PMCID: PMC6116925 DOI: 10.1371/journal.pcbi.1006341] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
The investigation of RNA-based regulation of cellular processes is becoming an increasingly important part of biological or medical research. For the analysis of this type of data, RNA-related prediction tools are integrated into many pipelines and workflows. In order to correctly apply and tune these programs, the user has to have a precise understanding of their limitations and concepts. Within this manuscript, we provide the mathematical foundations and extract the algorithmic ideas that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. To allow the reader to change and adapt the algorithms or to play with different inputs, we provide an open-source web interface to JavaScript implementations and visualizations of each algorithm. The conceptual, teaching-focused presentation enables a high-level survey of the approaches, while providing sufficient details for understanding important concepts. This is boosted by the simple generation and study of examples using the web interface available at http://rna.informatik.uni-freiburg.de/Teaching/. In combination, we provide a valuable resource for teaching, learning, and understanding the discussed prediction tools and thus enable a more informed analysis of RNA-related effects. RNA molecules are central players in many cellular processes. Thus, the analysis of RNA-based regulation has provided valuable insights and is often pivotal to biological and medical research. In order to correctly select appropriate algorithms and apply available RNA structure and RNA–RNA interaction prediction software, it is crucial to have a good understanding of their limitations and concepts. Such an overview is hard to achieve by end users, since most state-of-the-art tools are introduced on expert level and are not discussed in text books. Within this manuscript, we provide the mathematical means and extract the algorithmic concepts that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. The conceptual, teaching-focused presentation enables a detailed understanding of the approaches using a simplified model for didactic purposes. We support this process by providing clear examples using the web interface of our algorithm implementation. In summary, we have compiled material and web applications for teaching—and the self-study of—several state-of-the-art algorithms commonly used to investigate the role of RNA in regulatory processes.
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Affiliation(s)
- Martin Raden
- Chair of Forest Growth and Dendroecology, University of Freiburg, Freiburg, Germany
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
- * E-mail:
| | - Mostafa Mahmoud Mohamed
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Syed Mohsin Ali
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
- Center for Biological Signaling Studies, University of Freiburg, Freiburg, Germany
- Center for Biological Systems Analysis, University of Freiburg, Freiburg, Germany
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16
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Raden M, Ali SM, Alkhnbashi OS, Busch A, Costa F, Davis JA, Eggenhofer F, Gelhausen R, Georg J, Heyne S, Hiller M, Kundu K, Kleinkauf R, Lott SC, Mohamed MM, Mattheis A, Miladi M, Richter AS, Will S, Wolff J, Wright PR, Backofen R. Freiburg RNA tools: a central online resource for RNA-focused research and teaching. Nucleic Acids Res 2018; 46:W25-W29. [PMID: 29788132 PMCID: PMC6030932 DOI: 10.1093/nar/gky329] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/03/2018] [Accepted: 05/18/2018] [Indexed: 12/20/2022] Open
Abstract
The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.
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Affiliation(s)
- Martin Raden
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Syed M Ali
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Omer S Alkhnbashi
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Anke Busch
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany
| | - Fabrizio Costa
- Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK
| | - Jason A Davis
- Coreva Scientific, Kaiser-Joseph-Str 198-200, 79098 Freiburg, Germany
| | - Florian Eggenhofer
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Rick Gelhausen
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Jens Georg
- Genetics and Experimental Bioinformatics, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Steffen Heyne
- Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg, Germany
| | - Michael Hiller
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Kousik Kundu
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
- Department of Human Genetics, The Wellcome Trust Sanger Institute, Hinxton Cambridge CB10 1HH, UK
| | - Robert Kleinkauf
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Steffen C Lott
- Genetics and Experimental Bioinformatics, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Mostafa M Mohamed
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Alexander Mattheis
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Milad Miladi
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | | | - Sebastian Will
- Theoretical Biochemistry Group, University of Vienna, Währingerstraße 17, 1090 Vienna, Austria
| | - Joachim Wolff
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Patrick R Wright
- Department of Clinical Research, Clinical Trial Unit, University of Basel Hospital, Schanzenstrasse 55, 4031 Basel, Switzerland
| | - Rolf Backofen
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
- Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
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Makara D, Lederman G, Raden M, Silverman P, Grosman I, Costantino T, Fastaia M. Fractionated Stereotactic Radiosurgery (FSR) for Acoustic Neuroma (AN) — lack of side effects. Int J Radiat Oncol Biol Phys 2003. [DOI: 10.1016/s0360-3016(03)01206-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Fastaia M, Silverman P, Dimino E, Makara D, Costantino T, Lowry J, Lederman G, Raden M, Grosman I. Fractionated stereotactic radiosurgery (FSR) for meningiomas - effects of prior surgery. Int J Radiat Oncol Biol Phys 2003. [DOI: 10.1016/s0360-3016(03)01364-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Bockowski D, Wertheim S, Lederman G, Raden M, Odaimi M, Grosman I, Silverman P, Bockowski D, Pannullo S. Fractionated stereotactic radiosurgery and taxol (FSR/T) for recurrent glioblastoma (RGBM). Int J Radiat Oncol Biol Phys 2002. [DOI: 10.1016/s0360-3016(02)03486-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Lederman G, Wertheim S, Raden M, Silverman P, Bockowski D. Hearing preservation for acoustic neuromas after treatment with hypo-fractionated radiosurgery. Int J Radiat Oncol Biol Phys 2002. [DOI: 10.1016/s0360-3016(02)03307-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lederman G, Lowry J, Wertheim S, Fine M, Raden M, Silverman P, Volpicella F, Bockowski D, Lombardi E. Hearing preservation after hypofractionated radiosurgery for acoustic neuromas. Int J Radiat Oncol Biol Phys 2001. [DOI: 10.1016/s0360-3016(01)01904-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Lederman G, Lowry J, Wertheim S, Fine M, Raden M, Silverman P, Volpicella F, Qian G, Pannullo S, Arbit E. Fractionated stereotactic radiosurgery (FSR) for acoustic neuroma (AN). Int J Radiat Oncol Biol Phys 2000. [DOI: 10.1016/s0360-3016(00)80306-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Lederman G, Lowry J, Wertheim S, Fine M, Raden M, Silverman P, Lombardi E, Qian G, Pannullo S, Arbit E. 1030 Fractionated Stereotactic Radiosurgery (FSR) for Acoustic Neuroma (AN). Int J Radiat Oncol Biol Phys 1999. [DOI: 10.1016/s0360-3016(99)90256-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abstract
This study was designed to determine whether the prevalences of the DSM-III alcohol abuse/dependence symptoms in 87 early and 73 late onset male alcoholics differ from one another. The authors administered a 19-item alcohol abuse/dependence symptom checklist with items based on the DSM-III criteria. Nine of the 19 symptoms were reported significantly more often in the early than in the late onset alcoholics. Antisocial behaviors were reported to have been particularly frequent in the early onset group.
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Affiliation(s)
- C G Watson
- Research Service, Veterans Affairs Medical Center, St. Cloud, Minnesota 56303, USA
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25
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Abstract
The purpose of this project was to identify the outcomes associated with frequent, moderate, occasional, and nonparticipation in Alcoholics Anonymous by male alcohol dependents during the first month after treatment. Informants reported nonparticipants consumed far more alcohol during a 48 week followup than moderate or occasional participants. Moderate and occasional participants were rated as abstinent more often than nonparticipants. Nonparticipants were also reported jailed more often than participants. All other consumption and quality of life comparisons between the groups were nonsignificant. Occasional and moderate AA attendance appear to be associated with better outcomes than nonattendance, but frequent participation was not associated with additional improvement.
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Affiliation(s)
- C G Watson
- Department of Veterans Affairs Medical Center, St. Cloud, Minnesota 56303, USA
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Abstract
The research on the controversial Alcoholics Anonymous tenet that limited drinking rapidly leads alcoholics to inebriety is inconclusive. We conducted 48-week follow-ups on 51 posttreatment alcohol dependents who had reportedly engaged in limited drinking and 51 paired controls who apparently had not. According to the informants, the limited drinkers consumed 16 times as much alcohol and were 4 times as likely to regress to unacceptable drinking as controls. They were also more often rehospitalized and attended fewer Alcoholics Anonymous meetings than the controls. They were, however, usually (62%) categorized as abstinent or moderate drinkers when assessed during the follow-up period. The groups did not differ in risk of jailing, detoxification, or job loss, nor did limited drinkers ordinarily regress quickly to inebriety. The outcomes of our limited drinkers were inferior to those of controls but much less negative than those Wilson's Alcoholics Anonymous maintains.
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Affiliation(s)
- C G Watson
- Department of Veterans Affairs Medical Center, Research Service, St. Cloud, Minnesota 56303, USA
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Watson CG, Plemel D, Schaefer A, Raden M, Alfano AM, Anderson PE, Thomas D, Anderson D. The comparative concurrent validities of the Shipley Institute of Living Scale and the Henmon-Nelson Tests of Mental Ability. J Clin Psychol 1992; 48:233-9. [PMID: 1573026 DOI: 10.1002/1097-4679(199203)48:2<233::aid-jclp2270480215>3.0.co;2-r] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
This study compared the abilities of the Shipley Institute of Living Scale and the Henmon-Nelson Tests of Mental Ability to predict Wechsler Adult Intelligence Scale-Revised (WAIS-R) scores in psychiatric hospital patients. The Henmon-Nelson DIQs accounted for about 50% more WAIS-R Verbal and Full Scale IQ variance than did the Shipley IQs, apparently because of their higher correlations with the Information, Vocabulary, and, perhaps, Similarities subtests. Because Henmon-Nelson scores were more variable and generally higher than their WAIS-R counterparts, statistical adjustments were needed to optimize Wechsler IQ estimates. Therefore, regression formulae and a conversion table for the estimation of WAIS-R Full Scale IQs from Henmon-Nelson and Shipley intelligence scores also are presented.
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Affiliation(s)
- C G Watson
- Research Service, Veterans Administration Medical Center, St. Cloud, MN 56303
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