1
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Backofen R, Gorodkin J, Hofacker IL, Stadler PF. Comparative RNA Genomics. Methods Mol Biol 2024; 2802:347-393. [PMID: 38819565 DOI: 10.1007/978-1-0716-3838-5_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.
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Affiliation(s)
- Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
| | - Jan Gorodkin
- Center for Non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Ivo L Hofacker
- Institute for Theoretical Chemistry, University of Vienna, Wien, Austria
- Bioinformatics and Computational Biology research group, University of Vienna, Vienna, Austria
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany.
- Universidad National de Colombia, Bogotá, Colombia.
- Institute for Theoretical Chemistry, University of Vienna, Wien, Austria.
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.
- Santa Fe Institute, Santa Fe, NM, USA.
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2
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Eggenhofer F, Höner Zu Siederdissen C. Evolutionary Structure Conservation and Covariance Scores. Methods Mol Biol 2024; 2726:255-284. [PMID: 38780735 DOI: 10.1007/978-1-0716-3519-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Effective homology search for non-coding RNAs is frequently not possible via sequence similarity alone. Current methods leverage evolutionary information like structure conservation or covariance scores to identify homologs in organisms that are phylogenetically more distant. In this chapter, we introduce the theoretical background of evolutionary structure conservation and covariance score, and we show hands-on how current methods in the field are applied on example datasets.
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Affiliation(s)
- Florian Eggenhofer
- Bioinformatics Group, Department of Computer Science University of Freiburg, Freiburg, Germany
| | - Christian Höner Zu Siederdissen
- Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
- Bioinformatics/High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany.
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3
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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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4
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Grzejda D, Mach J, Schweizer JA, Hummel B, Rezansoff AM, Eggenhofer F, Panhale A, Lalioti ME, Cabezas Wallscheid N, Backofen R, Felsenberg J, Hilgers V. The long noncoding RNA mimi scaffolds neuronal granules to maintain nervous system maturity. SCIENCE ADVANCES 2022; 8:eabo5578. [PMID: 36170367 PMCID: PMC9519039 DOI: 10.1126/sciadv.abo5578] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 08/15/2022] [Indexed: 05/29/2023]
Abstract
RNA binding proteins and messenger RNAs (mRNAs) assemble into ribonucleoprotein granules that regulate mRNA trafficking, local translation, and turnover. The dysregulation of RNA-protein condensation disturbs synaptic plasticity and neuron survival and has been widely associated with human neurological disease. Neuronal granules are thought to condense around particular proteins that dictate the identity and composition of each granule type. Here, we show in Drosophila that a previously uncharacterized long noncoding RNA, mimi, is required to scaffold large neuronal granules in the adult nervous system. Neuronal ELAV-like proteins directly bind mimi and mediate granule assembly, while Staufen maintains condensate integrity. mimi granules contain mRNAs and proteins involved in synaptic processes; granule loss in mimi mutant flies impairs nervous system maturity and neuropeptide-mediated signaling and causes phenotypes of neurodegeneration. Our work reports an architectural RNA for a neuronal granule and provides a handle to interrogate functions of a condensate independently of those of its constituent proteins.
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Affiliation(s)
- Dominika Grzejda
- Max-Planck-Institute of Immunobiology and Epigenetics, Freiburg 79108, Germany
- Faculty of Biology, Albert Ludwig University of Freiburg, Freiburg 79104, Germany
- International Max Planck Research School for Molecular and Cellular Biology (IMPRS- MCB), Freiburg 79108, Germany
| | - Jana Mach
- Max-Planck-Institute of Immunobiology and Epigenetics, Freiburg 79108, Germany
| | - Johanna Aurelia Schweizer
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel 4058, Switzerland
- University of Basel, Basel 4001, Switzerland
| | - Barbara Hummel
- Max-Planck-Institute of Immunobiology and Epigenetics, Freiburg 79108, Germany
| | | | - Florian Eggenhofer
- Department of Computer Science, Albert Ludwig University of Freiburg, Freiburg 79110, Germany
| | - Amol Panhale
- Max-Planck-Institute of Immunobiology and Epigenetics, Freiburg 79108, Germany
| | - Maria-Eleni Lalioti
- Max-Planck-Institute of Immunobiology and Epigenetics, Freiburg 79108, Germany
| | | | - Rolf Backofen
- Department of Computer Science, Albert Ludwig University of Freiburg, Freiburg 79110, Germany
- BIOSS and CIBSS Centres for Biological Signalling Studies, University of Freiburg, Freiburg 79104, Germany
| | - Johannes Felsenberg
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel 4058, Switzerland
| | - Valérie Hilgers
- Max-Planck-Institute of Immunobiology and Epigenetics, Freiburg 79108, Germany
- CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg 79104, Germany
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5
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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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6
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Evolution and Phylogeny of MicroRNAs - Protocols, Pitfalls, and Problems. Methods Mol Biol 2021. [PMID: 34432281 DOI: 10.1007/978-1-0716-1170-8_11] [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: 09/17/2023]
Abstract
MicroRNAs are important regulators in many eukaryotic lineages. Typical miRNAs have a length of about 22nt and are processed from precursors that form a characteristic hairpin structure. Once they appear in a genome, miRNAs are among the best-conserved elements in both animal and plant genomes. Functionally, they play an important role in particular in development. In contrast to protein-coding genes, miRNAs frequently emerge de novo. The genomes of animals and plants harbor hundreds of mutually unrelated families of homologous miRNAs that tend to be persistent throughout evolution. The evolution of their genomic miRNA complement closely correlates with important morphological innovation. In addition, miRNAs have been used as valuable characters in phylogenetic studies. An accurate and comprehensive annotation of miRNAs is required as a basis to understand their impact on phenotypic evolution. Since experimental data on miRNA expression are limited to relatively few species and are subject to unavoidable ascertainment biases, it is inevitable to complement miRNA sequencing by homology based annotation methods. This chapter reviews the state of the art workflows for homology based miRNA annotation, with an emphasis on their limitations and open problems.
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7
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Kalvari I, Nawrocki EP, Ontiveros-Palacios N, Argasinska J, Lamkiewicz K, Marz M, Griffiths-Jones S, Toffano-Nioche C, Gautheret D, Weinberg Z, Rivas E, Eddy SR, Finn RD, Bateman A, Petrov AI. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res 2021; 49:D192-D200. [PMID: 33211869 PMCID: PMC7779021 DOI: 10.1093/nar/gkaa1047] [Citation(s) in RCA: 438] [Impact Index Per Article: 146.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 12/15/2022] Open
Abstract
Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.
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Affiliation(s)
- Ioanna Kalvari
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Eric P Nawrocki
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Nancy Ontiveros-Palacios
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Joanna Argasinska
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Kevin Lamkiewicz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany.,European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany.,European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany
| | - Sam Griffiths-Jones
- Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Claire Toffano-Nioche
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Daniel Gautheret
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Zasha Weinberg
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Centre for Bioinformatics, Leipzig University, 04107 Leipzig, Germany
| | - Elena Rivas
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Sean R Eddy
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.,Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA.,John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Anton I Petrov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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8
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Yazbeck AM, Stadler PF, Tout K, Fallmann J. Automatic curation of large comparative animal MicroRNA datasets. Bioinformatics 2020; 35:4553-4559. [PMID: 30993337 DOI: 10.1093/bioinformatics/btz271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 03/11/2019] [Accepted: 04/10/2019] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION MicroRNAs form an important class of RNA regulators that has been studied extensively. The miRBase and Rfam database provide rich, frequently updated information on both pre-miRNAs and their mature forms. These data sources, however, rely on individual data submission and thus are neither complete nor consistent in their coverage across different miRNA families. Quantitative studies of miRNA evolution therefore are difficult or impossible on this basis. RESULTS We present here a workflow and a corresponding implementation, MIRfix, that automatically curates miRNA datasets by improving alignments of their precursors, the consistency of the annotation of mature miR and miR* sequence, and the phylogenetic coverage. MIRfix produces alignments that are comparable across families and sets the stage for improved homology search as well as quantitative analyses. AVAILABILITY AND IMPLEMENTATION MIRfix can be downloaded from https://github.com/Bierinformatik/MIRfix. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ali M Yazbeck
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, D-04107 Leipzig, Germany.,Doctoral School of Science and Technology, Center for Biotechnology Research, Lebanese University, Hadath Campus, Beirut, Lebanon.,Helmholtz Centre for Environmental Research - UFZ, Young Investigators Group Bioinformatics and Transcriptomics, D-04318 Leipzig, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, D-04107 Leipzig, Germany.,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, and Leipzig Research Center for Civilization Diseases, University Leipzig, D-04107 Leipzig, Germany.,Max Planck Institute for Mathematics in the Sciences, D-04103 Leipzig, Germany.,Institute for Theoretical Chemistry, University of Vienna, A-1090 Wien, Austria.,Facultad de Ciencias, Universidad National de Colombia, Sede Bogotá, Colombia.,Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Kifah Tout
- Doctoral School of Science and Technology, Center for Biotechnology Research, Lebanese University, Hadath Campus, Beirut, Lebanon
| | - Jörg Fallmann
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, D-04107 Leipzig, Germany
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9
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Eggenhofer F, Hofacker IL, Backofen R, Höner Zu Siederdissen C. CMV: visualization for RNA and protein family models and their comparisons. Bioinformatics 2019; 34:2676-2678. [PMID: 29554223 PMCID: PMC6061798 DOI: 10.1093/bioinformatics/bty158] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 03/13/2018] [Indexed: 11/14/2022] Open
Abstract
Summary A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest. Availability and implementation Source code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Florian Eggenhofer
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.,Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Ivo L Hofacker
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.,Bioinformatics and Computational Biology Research Group, University of Vienna, Vienna, Austria
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.,Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany
| | - Christian Höner Zu Siederdissen
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.,Bioinformatics Group, Department of Computer Science, University of Leipzig, D-04107 Leipzig, Germany.,Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany
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10
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Fallmann J, Videm P, Bagnacani A, Batut B, Doyle MA, Klingstrom T, Eggenhofer F, Stadler PF, Backofen R, Grüning B. The RNA workbench 2.0: next generation RNA data analysis. Nucleic Acids Res 2019; 47:W511-W515. [PMID: 31073612 PMCID: PMC6602469 DOI: 10.1093/nar/gkz353] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 04/11/2019] [Accepted: 04/29/2019] [Indexed: 12/30/2022] Open
Abstract
RNA has become one of the major research topics in molecular biology. As a central player in key processes regulating gene expression, RNA is in the focus of many efforts to decipher the pathways that govern the transition of genetic information to a fully functional cell. As more and more researchers join this endeavour, there is a rapidly growing demand for comprehensive collections of tools that cover the diverse layers of RNA-related research. However, increasing amounts of data, from diverse types of experiments, addressing different aspects of biological questions need to be consolidated and integrated into a single framework. Only then is it possible to connect findings from e.g. RNA-Seq experiments and methods for e.g. target predictions. To address these needs, we present the RNA Workbench 2.0 , an updated online resource for RNA related analysis. With the RNA Workbench we created a comprehensive set of analysis tools and workflows that enables researchers to analyze their data without the need for sophisticated command-line skills. This update takes the established framework to the next level, providing not only a containerized infrastructure for analysis, but also a ready-to-use platform for hands-on training, analysis, data exploration, and visualization. The new framework is available at https://rna.usegalaxy.eu , and login is free and open to all users. The containerized version can be found at https://github.com/bgruening/galaxy-rna-workbench.
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Affiliation(s)
- Jörg Fallmann
- Bioinformatics Group, Department of Computer Science; Leipzig University, Härtelstraße 16-18, D-04107 Leipzig
| | - Pavankumar Videm
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
| | - Andrea Bagnacani
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany
| | - Bérénice Batut
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
| | - Maria A Doyle
- Research Computing Facility, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia
| | - Tomas Klingstrom
- SLU-Global Bioinformatics Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences
| | - Florian Eggenhofer
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science; Leipzig University, Härtelstraße 16-18, D-04107 Leipzig
- Interdisciplinary Center of Bioinformatics; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; Competence Center for Scalable Data Services and Solutions; and Leipzig Research Center for Civilization Diseases, Leipzig University, Härtelstraße 16-18, D-04107 Leipzig
- Max-Planck-Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig Inst. f. Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria; Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Colombia Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
- Signalling Research Centres BIOSS and CIBSS, Albert-Ludwigs-University Freiburg, Schänzlestr. 18, Freiburg 79104, Germany
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, Habsburgerstr. 49, 79104 Freiburg, Germany
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11
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Lagares A, Valverde C. Guidelines for Inferring and Characterizing a Family of Bacterial trans-Acting Small Noncoding RNAs. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2019; 1737:31-45. [PMID: 29484585 DOI: 10.1007/978-1-4939-7634-8_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
So far, every sequenced bacterial transcriptome encompasses hundreds of small regulatory noncoding RNAs (sRNAs). From those sRNAs that have been already characterized, we learned that their regulatory functions could span over almost every bacterial process, mostly acting at the posttranscriptional control of gene expression (Wagner and Romby, Adv Genet 90:133-208, 2015). Canonical molecular mechanisms of sRNA action have been described to rely on both sequence and/or structural traits of the RNA molecule. As for protein-coding genes, the conservation of sRNAs among species suggests conserved and adjusted functions across evolution. Knowing the phylogenetic distribution of an sRNA gene and how its functional traits have evolved may help to get a broad picture of its biological role in each single species. Here, we present a simple computational workflow to identify close and distant sRNA homologs present in sequenced bacterial genomes, which allows defining novel sRNA families. This strategy is based on the use of Covariance Models (CM) and assumes the conservation of sequence and structure of functional sRNA genes throughout evolution. Moreover, by carefully inspecting the conservation of the close genomic context of every member of the RNA family and how the patterns of microsynteny follow the path of species evolution, it is possible to define subgroups of sRNA orthologs, which in turn enables the definition of RNA subfamilies.
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Affiliation(s)
- Antonio Lagares
- Laboratorio de Bioquímica, Microbiología e Interacciones Biológicas en el Suelo, Universidad Nacional de Quilmes-CONICET, Roque Sáenz Peña 352, Bernal, B1876BXD, Argentina.
| | - Claudio Valverde
- Laboratorio de Bioquímica, Microbiología e Interacciones Biológicas en el Suelo, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes-CONICET, Bernal, Argentina
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12
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Abstract
Computational methods can often facilitate the functional characterization of individual sRNAs and furthermore allow high-throughput analysis on large numbers of sRNA candidates. This chapter outlines a potential workflow for computational sRNA analyses and describes in detail methods for homolog detection, target prediction, and functional characterization based on enrichment analysis. The cyanobacterial sRNA IsaR1 is used as a specific example. All methods are available as webservers and easily accessible for nonexpert users.
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13
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Abstract
Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in "wet" laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many in silico methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.
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14
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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: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [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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15
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Lott SC, Schäfer RA, Mann M, Backofen R, Hess WR, Voß B, Georg J. GLASSgo - Automated and Reliable Detection of sRNA Homologs From a Single Input Sequence. Front Genet 2018; 9:124. [PMID: 29719549 PMCID: PMC5913331 DOI: 10.3389/fgene.2018.00124] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 03/26/2018] [Indexed: 11/24/2022] Open
Abstract
Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.
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Affiliation(s)
- Steffen C Lott
- Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Richard A Schäfer
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
| | - Martin Mann
- Bioinformatics Group, Faculty of Computer Science, University of Freiburg, Freiburg, Germany.,Forest Growth and Dendroecology, Institute of Forest Sciences, University of Freiburg, Freiburg, Germany
| | - Rolf Backofen
- Bioinformatics Group, Faculty of Computer Science, University of Freiburg, Freiburg, Germany.,ZBSA Center for Biological Systems Analysis, University of Freiburg, Freiburg, Germany.,BIOSS Centre for Biological Signalling Studies, Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
| | - Wolfgang R Hess
- Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Freiburg, Germany.,Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg, Germany
| | - Björn Voß
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
| | - Jens Georg
- Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Freiburg, Germany
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16
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Identification and functional characterization of bacterial small non-coding RNAs and their target: A review. GENE REPORTS 2018. [DOI: 10.1016/j.genrep.2018.01.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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17
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Poblete S, Bottaro S, Bussi G. A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs. Nucleic Acids Res 2018; 46:1674-1683. [PMID: 29272539 PMCID: PMC5829650 DOI: 10.1093/nar/gkx1269] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 12/05/2017] [Accepted: 12/07/2017] [Indexed: 01/30/2023] Open
Abstract
We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the $\mathcal {E}$SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation.
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Affiliation(s)
- Simón Poblete
- Scuola Internazionale Superiore di Studi Avanzati, 265, Via Bonomea I-34136 Trieste, Italy
| | - Sandro Bottaro
- Scuola Internazionale Superiore di Studi Avanzati, 265, Via Bonomea I-34136 Trieste, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, 265, Via Bonomea I-34136 Trieste, Italy
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18
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Arias-Carrasco R, Vásquez-Morán Y, Nakaya HI, Maracaja-Coutinho V. StructRNAfinder: an automated pipeline and web server for RNA families prediction. BMC Bioinformatics 2018; 19:55. [PMID: 29454313 PMCID: PMC5816368 DOI: 10.1186/s12859-018-2052-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 02/02/2018] [Indexed: 01/11/2023] Open
Abstract
Background The function of many noncoding RNAs (ncRNAs) depend upon their secondary structures. Over the last decades, several methodologies have been developed to predict such structures or to use them to functionally annotate RNAs into RNA families. However, to fully perform this analysis, researchers should utilize multiple tools, which require the constant parsing and processing of several intermediate files. This makes the large-scale prediction and annotation of RNAs a daunting task even to researchers with good computational or bioinformatics skills. Results We present an automated pipeline named StructRNAfinder that predicts and annotates RNA families in transcript or genome sequences. This single tool not only displays the sequence/structural consensus alignments for each RNA family, according to Rfam database but also provides a taxonomic overview for each assigned functional RNA. Moreover, we implemented a user-friendly web service that allows researchers to upload their own nucleotide sequences in order to perform the whole analysis. Finally, we provided a stand-alone version of StructRNAfinder to be used in large-scale projects. The tool was developed under GNU General Public License (GPLv3) and is freely available at http://structrnafinder.integrativebioinformatics.me. Conclusions The main advantage of StructRNAfinder relies on the large-scale processing and integrating the data obtained by each tool and database employed along the workflow, of which several files are generated and displayed in user-friendly reports, useful for downstream analyses and data exploration. Electronic supplementary material The online version of this article (10.1186/s12859-018-2052-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Raúl Arias-Carrasco
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, 8580745, Santiago, Chile.,Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, 8580745, Santiago, Chile
| | - Yessenia Vásquez-Morán
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, 8580745, Santiago, Chile
| | - Helder I Nakaya
- Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, 05508-900, Brazil.
| | - Vinicius Maracaja-Coutinho
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, 8580745, Santiago, Chile. .,Instituto Vandique, João Pessoa, 58000-000, Brazil. .,Beagle Bioinformatics, 8320000, Santiago, Chile. .,Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, 8380492, Santiago, Chile.
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19
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Lim CS, Brown CM. Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs. Front Microbiol 2018; 8:2582. [PMID: 29354101 PMCID: PMC5758548 DOI: 10.3389/fmicb.2017.02582] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 12/11/2017] [Indexed: 12/14/2022] Open
Abstract
Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.
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Affiliation(s)
- Chun Shen Lim
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Chris M Brown
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
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20
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Abstract
Over the last two decades it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible noncoding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of noncoding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.
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Affiliation(s)
- Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.,Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark
| | - Jan Gorodkin
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark
| | - Ivo L Hofacker
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark.,Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria.,Bioinformatics and Computational Biology Research Group, University of Vienna, Währingerstraße 17, A-1090 Vienna, Austria
| | - Peter F Stadler
- Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark. .,Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. .,Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany. .,Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany. .,Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, D-04103 Leipzig, Germany. .,Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.
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21
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Lott SC, Wolfien M, Riege K, Bagnacani A, Wolkenhauer O, Hoffmann S, Hess WR. Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome experiments. J Biotechnol 2017; 261:85-96. [DOI: 10.1016/j.jbiotec.2017.06.1203] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 06/22/2017] [Accepted: 06/26/2017] [Indexed: 12/14/2022]
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22
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Grüning BA, Fallmann J, Yusuf D, Will S, Erxleben A, Eggenhofer F, Houwaart T, Batut B, Videm P, Bagnacani A, Wolfien M, Lott SC, Hoogstrate Y, Hess WR, Wolkenhauer O, Hoffmann S, Akalin A, Ohler U, Stadler PF, Backofen R. The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy. Nucleic Acids Res 2017; 45:W560-W566. [PMID: 28582575 PMCID: PMC5570170 DOI: 10.1093/nar/gkx409] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 04/13/2017] [Accepted: 05/31/2017] [Indexed: 01/23/2023] Open
Abstract
RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. AVAILABILITY The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench.
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Affiliation(s)
- Björn A. Grüning
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, Habsburgerstr. 49, D-79104 Freiburg, Germany
| | - Jörg Fallmann
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
| | - Dilmurat Yusuf
- Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, D-13125, Berlin, Germany
| | - Sebastian Will
- Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria
| | - Anika Erxleben
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
| | - Florian Eggenhofer
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
| | - Torsten Houwaart
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
| | - Bérénice Batut
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
| | - Pavankumar Videm
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
| | - Andrea Bagnacani
- Department of Systems Biology and Bioinformatics, University of Rostock, Ulmenstr. 69, D-18051 Rostock, Germany
| | - Markus Wolfien
- Department of Systems Biology and Bioinformatics, University of Rostock, Ulmenstr. 69, D-18051 Rostock, Germany
| | - Steffen C. Lott
- Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, D-79104 Freiburg, Germany
| | - Youri Hoogstrate
- Department of Urology, Erasmus University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Wolfgang R. Hess
- Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, D-79104 Freiburg, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Ulmenstr. 69, D-18051 Rostock, Germany
| | - Steve Hoffmann
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
| | - Altuna Akalin
- Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, D-13125, Berlin, Germany
| | - Uwe Ohler
- Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, D-13125, Berlin, Germany
- Departments of Biology and Computer Science, Humboldt University, Unter den Linden 6, D-10099 Berlin
| | - Peter F. Stadler
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
- Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria
- Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, D-04103 Leipzig, Germany
- Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, Habsburgerstr. 49, D-79104 Freiburg, Germany
- BIOSS Centre for Biological Signaling Studies, University of Freiburg, Schänzlestr. 18, D-79104 Freiburg, Germany
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23
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Phan A, Mailey K, Saeki J, Gu X, Schroeder SJ. Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops. RNA (NEW YORK, N.Y.) 2017; 23:770-781. [PMID: 28213527 PMCID: PMC5393185 DOI: 10.1261/rna.059865.116] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 02/13/2017] [Indexed: 05/24/2023]
Abstract
Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased toward small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with >6 nucleotides (nt) that occur frequently in viral RNA. This article presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Uracil and protonated cytosine base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with >6 nt are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2 × 2 have been measured. These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites.
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Affiliation(s)
- Andy Phan
- Department of Chemistry and Biochemistry
| | | | | | - Xiaobo Gu
- Department of Chemistry and Biochemistry
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Susan J Schroeder
- Department of Chemistry and Biochemistry
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, USA
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24
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Petrov AI, Kay SJE, Kalvari I, Howe KL, Gray KA, Bruford EA, Kersey PJ, Cochrane G, Finn RD, Bateman A, Kozomara A, Griffiths-Jones S, Frankish A, Zwieb CW, Lau BY, Williams KP, Chan PP, Lowe TM, Cannone JJ, Gutell R, Machnicka MA, Bujnicki JM, Yoshihama M, Kenmochi N, Chai B, Cole JR, Szymanski M, Karlowski WM, Wood V, Huala E, Berardini TZ, Zhao Y, Chen R, Zhu W, Paraskevopoulou MD, Vlachos IS, Hatzigeorgiou AG, Ma L, Zhang Z, Puetz J, Stadler PF, McDonald D, Basu S, Fey P, Engel SR, Cherry JM, Volders PJ, Mestdagh P, Wower J, Clark MB, Quek XC, Dinger ME. RNAcentral: a comprehensive database of non-coding RNA sequences. Nucleic Acids Res 2017; 45:D128-D134. [PMID: 27794554 PMCID: PMC5210518 DOI: 10.1093/nar/gkw1008] [Citation(s) in RCA: 138] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 10/13/2016] [Accepted: 10/18/2016] [Indexed: 12/12/2022] Open
Abstract
RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. The website has been subject to continuous improvements focusing on text and sequence similarity searches as well as genome browsing functionality. All RNAcentral data is provided for free and is available for browsing, bulk downloads, and programmatic access at http://rnacentral.org/.
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25
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Poyntner C, Blasi B, Arcalis E, Mirastschijski U, Sterflinger K, Tafer H. The Transcriptome of Exophiala dermatitidis during Ex-vivo Skin Model Infection. Front Cell Infect Microbiol 2016; 6:136. [PMID: 27822460 PMCID: PMC5075926 DOI: 10.3389/fcimb.2016.00136] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 10/06/2016] [Indexed: 12/12/2022] Open
Abstract
The black yeast Exophiala dermatitidis is a widespread polyextremophile and human pathogen, that is found in extreme natural habitats and man-made environments such as dishwashers. It can cause various diseases ranging from phaeohyphomycosis and systemic infections, with fatality rates reaching 40%. While the number of cases in immunocompromised patients are increasing, knowledge of the infections, virulence factors and host response is still scarce. In this study, for the first time, an artificial infection of an ex-vivo skin model with Exophiala dermatitidis was monitored microscopically and transcriptomically. Results show that Exophiala dermatitidis is able to actively grow and penetrate the skin. The analysis of the genomic and RNA-sequencing data delivers a rich and complex transcriptome where circular RNAs, fusion transcripts, long non-coding RNAs and antisense transcripts are found. Changes in transcription strongly affect pathways related to nutrients acquisition, energy metabolism, cell wall, morphological switch, and known virulence factors. The L-Tyrosine melanin pathway is specifically upregulated during infection. Moreover the production of secondary metabolites, especially alkaloids, is increased. Our study is the first that gives an insight into the complexity of the transcriptome of Exophiala dermatitidis during artificial skin infections and reveals new virulence factors.
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Affiliation(s)
- Caroline Poyntner
- Department of Biotechnology, VIBT EQ Extremophile Center, University of Natural Resources and Life Sciences Vienna, Austria
| | - Barbara Blasi
- Department of Biotechnology, VIBT EQ Extremophile Center, University of Natural Resources and Life Sciences Vienna, Austria
| | - Elsa Arcalis
- Department for Applied Genetics and Cell Biology, Molecular Plant Physiology and Crop Biotechnology, University of Natural Resources and Life Sciences Vienna, Austria
| | - Ursula Mirastschijski
- Klinikum Bremen-Mitte, Department of Plastic, Reconstructive and Aesthetic Surgery, Faculty of Biology and Chemistry, Center for Biomolecular Interactions Bremen, University Bremen Bremen, Germany
| | - Katja Sterflinger
- Department of Biotechnology, VIBT EQ Extremophile Center, University of Natural Resources and Life Sciences Vienna, Austria
| | - Hakim Tafer
- Department of Biotechnology, VIBT EQ Extremophile Center, University of Natural Resources and Life Sciences Vienna, Austria
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