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Siemers M, Lippegaus A, Papenfort K. ChimericFragments: computation, analysis and visualization of global RNA networks. NAR Genom Bioinform 2024; 6:lqae035. [PMID: 38633425 PMCID: PMC11023125 DOI: 10.1093/nargab/lqae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/08/2024] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
RNA-RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA-RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seq or CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA-target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.
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Affiliation(s)
- Malte Siemers
- Friedrich Schiller University, Institute of Microbiology, 07745 Jena, Germany
- Microverse Cluster, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Anne Lippegaus
- Friedrich Schiller University, Institute of Microbiology, 07745 Jena, Germany
| | - Kai Papenfort
- Friedrich Schiller University, Institute of Microbiology, 07745 Jena, Germany
- Microverse Cluster, Friedrich Schiller University Jena, 07743 Jena, Germany
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2
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von Löhneysen S, Mörl M, Stadler PF. Limits of experimental evidence in RNA secondary structure prediction. FRONTIERS IN BIOINFORMATICS 2024; 4:1346779. [PMID: 38456157 PMCID: PMC10918467 DOI: 10.3389/fbinf.2024.1346779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/09/2024] [Indexed: 03/09/2024] Open
Affiliation(s)
- Sarah von Löhneysen
- Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Mario Mörl
- Institute for Biochemistry, Leipzig University, Leipzig, Germany
| | - Peter F. Stadler
- Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
- Competence Center for Scalable Data Analytics and Artificial Intelligence, School of Embedded and Compositive Artificial Intelligence (SECAI), Leipzig University, Leipzig, Germany
- Department of Theoretical Chemistry, University of Vienna, Wien, Austria
- Facultad de Ciencias, Universidad National de Colombia, Bogotá, Colombia
- Center for Non-Coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
- Santa Fe Institute, Santa Fe, NM, United States
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3
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Müller T, Mautner S, Videm P, Eggenhofer F, Raden M, Backofen R. CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites. Gigascience 2024; 13:giae022. [PMID: 38837942 PMCID: PMC11152173 DOI: 10.1093/gigascience/giae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/04/2024] [Accepted: 04/22/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate. RESULTS We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs. CONCLUSIONS CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.
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Affiliation(s)
- Teresa Müller
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Stefan Mautner
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Pavankumar Videm
- 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
| | - Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
- Signalling Research Centre CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
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Tieng FYF, Abdullah-Zawawi MR, Md Shahri NAA, Mohamed-Hussein ZA, Lee LH, Mutalib NSA. A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools. Brief Bioinform 2023; 25:bbad421. [PMID: 38040490 PMCID: PMC10753535 DOI: 10.1093/bib/bbad421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/26/2023] [Indexed: 12/03/2023] Open
Abstract
RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.
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Affiliation(s)
- Francis Yew Fu Tieng
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
| | | | - Nur Alyaa Afifah Md Shahri
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of Systems Biology (INBIOSIS), UKM, Selangor 43600, Malaysia
- Department of Applied Physics, Faculty of Science and Technology, UKM, Selangor 43600, Malaysia
| | - Learn-Han Lee
- Sunway Microbiomics Centre, School of Medical and Life Sciences, Sunway University, Sunway City 47500, Malaysia
- Novel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University of Malaysia, Selangor 47500, Malaysia
| | - Nurul-Syakima Ab Mutalib
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
- Novel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University of Malaysia, Selangor 47500, Malaysia
- Faculty of Health Sciences, UKM, Kuala Lumpur 50300, Malaysia
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Schäfer RA, Rabsch D, Scholz GE, Stadler PF, Hess WR, Backofen R, Fallmann J, Voß B. RNA interaction format: a general data format for RNA interactions. Bioinformatics 2023; 39:btad665. [PMID: 37944046 PMCID: PMC10640394 DOI: 10.1093/bioinformatics/btad665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 09/19/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
SUMMARY RNA molecules play crucial roles in various biological processes. They mediate their function mainly by interacting with other RNAs or proteins. At present, information about these interactions is distributed over different resources, often providing the data in simple tab-delimited formats that differ between the databases. There is no standardized data format that can capture the nature of all these different interactions in detail. AVAILABILITY AND IMPLEMENTATION Here, we propose the RNA interaction format (RIF) for the detailed representation of RNA-RNA and RNA-Protein interactions and provide reference implementations in C/C++, Python, and JavaScript. RIF is released under licence GNU General Public License version 3 (GNU GPLv3) and is available on https://github.com/RNABioInfo/rna-interaction-format.
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Affiliation(s)
- Richard A Schäfer
- RNA-Biology and Bioinformatics, Institute of Biomedical Genetics, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Dominik Rabsch
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Guillaume E Scholz
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany
| | - Wolfgang R Hess
- Genetics and Experimental Bioinformatics, Institute of Biology III, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Jörg Fallmann
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany
| | - Björn Voß
- RNA-Biology and Bioinformatics, Institute of Biomedical Genetics, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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Velema WA, Lu Z. Chemical RNA Cross-Linking: Mechanisms, Computational Analysis, and Biological Applications. JACS AU 2023; 3:316-332. [PMID: 36873678 PMCID: PMC9975857 DOI: 10.1021/jacsau.2c00625] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/23/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
In recent years, RNA has emerged as a multifaceted biomolecule that is involved in virtually every function of the cell and is critical for human health. This has led to a substantial increase in research efforts to uncover the many chemical and biological aspects of RNA and target RNA for therapeutic purposes. In particular, analysis of RNA structures and interactions in cells has been critical for understanding their diverse functions and druggability. In the last 5 years, several chemical methods have been developed to achieve this goal, using chemical cross-linking combined with high-throughput sequencing and computational analysis. Applications of these methods resulted in important new insights into RNA functions in a variety of biological contexts. Given the rapid development of new chemical technologies, a thorough perspective on the past and future of this field is provided. In particular, the various RNA cross-linkers and their mechanisms, the computational analysis and challenges, and illustrative examples from recent literature are discussed.
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Affiliation(s)
- Willem A. Velema
- Institute
for Molecules and Materials, Radboud University, Nijmegen 6500 HC, The Netherlands
| | - Zhipeng Lu
- Department
of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, California 90033, United States
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Krohmaly KI, Freishtat RJ, Hahn AL. Bioinformatic and experimental methods to identify and validate bacterial RNA-human RNA interactions. J Investig Med 2023; 71:23-31. [PMID: 36162901 DOI: 10.1136/jim-2022-002509] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/31/2022] [Indexed: 01/21/2023]
Abstract
Ample evidence supports the importance of the microbiota on human health and disease. Recent studies suggest that extracellular vesicles are an important means of bacterial-host communication, in part via the transport of small RNAs (sRNAs). Bacterial sRNAs have been shown to co-precipitate with human and mouse RNA-induced silencing complex, hinting that some may regulate gene expression as eukaryotic microRNAs do. Bioinformatic tools, including those that can incorporate an sRNA's secondary structure, can be used to predict interactions between bacterial sRNAs and human messenger RNAs (mRNAs). Validation of these potential interactions using reproducible experimental methods is essential to move the field forward. This review will cover the evidence of interspecies communication via sRNAs, bioinformatic tools currently available to identify potential bacterial sRNA-host (specifically, human) mRNA interactions, and experimental methods to identify and validate those interactions.
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Affiliation(s)
- Kylie I Krohmaly
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, District of Columbia, USA.,Institute for Biomedical Sciences, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Robert J Freishtat
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, District of Columbia, USA.,Division of Emergency Medicine, Children's National Hospital, Washington, District of Columbia, USA.,Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Andrea L Hahn
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, District of Columbia, USA.,Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA.,Division of Infectious Diseases, Children's National Hospital, Washington, District of Columbia, USA
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Mills WT, Eadara S, Jaffe AE, Meffert MK. SCRAP: a bioinformatic pipeline for the analysis of small chimeric RNA-seq data. RNA (NEW YORK, N.Y.) 2022; 29:rna.079240.122. [PMID: 36316086 PMCID: PMC9808574 DOI: 10.1261/rna.079240.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs (sncRNAs) that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets which results in inhibition of translation and typically destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA-target interactions by crosslinking and ligation of sncRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high throughput sequencing of the chimeric sncRNA:target RNAs. Despite the strength of these direct profiling approaches, standardized pipelines for effectively analyzing the resulting chimeric sncRNA:target RNA sequencing data are not readily available. Here we present SCRAP, a robust Small Chimeric RNA Analysis Pipeline for the bioinformatic processing of chimeric sncRNA:target RNA sequencing data. SCRAP consists of two parts, each of which are specifically optimized for the distinctive characteristics of chimeric small RNA sequencing reads: first, read processing and alignment and second, peak calling and annotation. We apply SCRAP to benchmark chimeric sncRNA:target RNA sequencing datasets generated by distinct molecular approaches, and compare SCRAP to existing chimeric RNA analysis pipelines. SCRAP has minimal hardware requirements, is cross-platform, and contains extensive annotation to broaden accessibility for processing small chimeric RNA sequencing data and enable insights about the targets of small non-coding RNAs in regulating diverse biological systems.
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