1
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Lisowski P, Lickfett S, Rybak-Wolf A, Menacho C, Le S, Pentimalli TM, Notopoulou S, Dykstra W, Oehler D, López-Calcerrada S, Mlody B, Otto M, Wu H, Richter Y, Roth P, Anand R, Kulka LAM, Meierhofer D, Glazar P, Legnini I, Telugu NS, Hahn T, Neuendorf N, Miller DC, Böddrich A, Polzin A, Mayatepek E, Diecke S, Olzscha H, Kirstein J, Ugalde C, Petrakis S, Cambridge S, Rajewsky N, Kühn R, Wanker EE, Priller J, Metzger JJ, Prigione A. Mutant huntingtin impairs neurodevelopment in human brain organoids through CHCHD2-mediated neurometabolic failure. Nat Commun 2024; 15:7027. [PMID: 39174523 PMCID: PMC11341898 DOI: 10.1038/s41467-024-51216-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 08/01/2024] [Indexed: 08/24/2024] Open
Abstract
Expansion of the glutamine tract (poly-Q) in the protein huntingtin (HTT) causes the neurodegenerative disorder Huntington's disease (HD). Emerging evidence suggests that mutant HTT (mHTT) disrupts brain development. To gain mechanistic insights into the neurodevelopmental impact of human mHTT, we engineered male induced pluripotent stem cells to introduce a biallelic or monoallelic mutant 70Q expansion or to remove the poly-Q tract of HTT. The introduction of a 70Q mutation caused aberrant development of cerebral organoids with loss of neural progenitor organization. The early neurodevelopmental signature of mHTT highlighted the dysregulation of the protein coiled-coil-helix-coiled-coil-helix domain containing 2 (CHCHD2), a transcription factor involved in mitochondrial integrated stress response. CHCHD2 repression was associated with abnormal mitochondrial morpho-dynamics that was reverted upon overexpression of CHCHD2. Removing the poly-Q tract from HTT normalized CHCHD2 levels and corrected key mitochondrial defects. Hence, mHTT-mediated disruption of human neurodevelopment is paralleled by aberrant neurometabolic programming mediated by dysregulation of CHCHD2, which could then serve as an early interventional target for HD.
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Affiliation(s)
- Pawel Lisowski
- Quantitative Stem Cell Biology, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité - Universitätsmedizin, Berlin, Germany
- Department of Molecular Biology, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, Jastrzebiec n/Warsaw, Poland
| | - Selene Lickfett
- Faculty of Mathematics and Natural Sciences, Heinrich Heine University, Düsseldorf, Germany
- Department of General Pediatrics, Neonatology and Pediatric Cardiology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
- Institute of Anatomy II, Heinrich-Heine-University, Düsseldorf, Germany
| | - Agnieszka Rybak-Wolf
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Organoid Platform, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Carmen Menacho
- Faculty of Mathematics and Natural Sciences, Heinrich Heine University, Düsseldorf, Germany
- Department of General Pediatrics, Neonatology and Pediatric Cardiology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Stephanie Le
- Faculty of Mathematics and Natural Sciences, Heinrich Heine University, Düsseldorf, Germany
- Department of General Pediatrics, Neonatology and Pediatric Cardiology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Tancredi Massimo Pentimalli
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Laboratory for Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
- Charité - Universitätsmedizin, Berlin, Germany
| | - Sofia Notopoulou
- Institute of Applied Biosciences (INAB), Centre For Research and Technology Hellas (CERTH), Thessaloniki, Greece
| | - Werner Dykstra
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | - Daniel Oehler
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital Düsseldorf, Cardiovascular Research Institute Düsseldorf (CARID), Düsseldorf, Germany
| | | | - Barbara Mlody
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Centogene, Rostock, Germany
| | - Maximilian Otto
- Quantitative Stem Cell Biology, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Haijia Wu
- Institute of Molecular Medicine, Medical School, Hamburg, Germany
| | | | - Philipp Roth
- Quantitative Stem Cell Biology, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Ruchika Anand
- Institute of Biochemistry and Molecular Biology I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Linda A M Kulka
- Institute of Physiological Chemistry, Martin-Luther-University, Halle-Wittenberg, Germany
| | - David Meierhofer
- Quantitative RNA Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Petar Glazar
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Laboratory for Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
- Quantitative RNA Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Ivano Legnini
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Laboratory for Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
- Human Technopole, Milan, Italy
| | - Narasimha Swamy Telugu
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Tobias Hahn
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Nancy Neuendorf
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Duncan C Miller
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Annett Böddrich
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Amin Polzin
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital Düsseldorf, Cardiovascular Research Institute Düsseldorf (CARID), Düsseldorf, Germany
| | - Ertan Mayatepek
- Department of General Pediatrics, Neonatology and Pediatric Cardiology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Sebastian Diecke
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Berlin, Germany
| | - Heidi Olzscha
- Institute of Molecular Medicine, Medical School, Hamburg, Germany
- Institute of Physiological Chemistry, Martin-Luther-University, Halle-Wittenberg, Germany
| | - Janine Kirstein
- Cell Biology, University of Bremen, Bremen, Germany
- Leibniz Institute on Aging - Fritz-Lipmann Institute, Jena, Germany
| | - Cristina Ugalde
- Instituto de Investigación Hospital 12 de Octubre (i + 12), Madrid, Spain
- Centro de Investigaciones Biológicas Margarita Salas (CIB-CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Spyros Petrakis
- Institute of Applied Biosciences (INAB), Centre For Research and Technology Hellas (CERTH), Thessaloniki, Greece
| | - Sidney Cambridge
- Institute of Anatomy II, Heinrich-Heine-University, Düsseldorf, Germany
- Dr. Senckenberg Anatomy, Anatomy II, Goethe-University, Frankfurt, Germany
| | - Nikolaus Rajewsky
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Laboratory for Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Berlin, Germany
- NeuroCure Cluster of Excellence, Berlin, Germany
- National Center for Tumor Diseases (NCT), German Cancer Consortium (DKTK), Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Ralf Kühn
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Erich E Wanker
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Josef Priller
- Department of Psychiatry and Psychotherapy, Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité - Universitätsmedizin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy; School of Medicine and Health, Technical University of Munich and German Center for Mental Health (DZPG), Munich, Germany
- University of Edinburgh and UK Dementia Research Institute, Edinburgh, UK
| | - Jakob J Metzger
- Quantitative Stem Cell Biology, Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany.
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
| | - Alessandro Prigione
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
- Department of General Pediatrics, Neonatology and Pediatric Cardiology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
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2
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Zhu XT, Sanz-Jimenez P, Ning XT, Tahir Ul Qamar M, Chen LL. Direct RNA sequencing in plants: Practical applications and future perspectives. PLANT COMMUNICATIONS 2024:101064. [PMID: 39155503 DOI: 10.1016/j.xplc.2024.101064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/17/2024] [Accepted: 08/14/2024] [Indexed: 08/20/2024]
Abstract
The transcriptome serves as a bridge that links genomic variation to phenotypic diversity. A vast number of studies using next-generation RNA sequencing (RNA-seq) over the last 2 decades have emphasized the essential roles of the plant transcriptome in response to developmental and environmental conditions, providing numerous insights into the dynamic changes, evolutionary traces, and elaborate regulation of the plant transcriptome. With substantial improvement in accuracy and throughput, direct RNA sequencing (DRS) has emerged as a new and powerful sequencing platform for precise detection of native and full-length transcripts, overcoming many limitations such as read length and PCR bias that are inherent to short-read RNA-seq. Here, we review recent advances in dissecting the complexity and diversity of plant transcriptomes using DRS as the main technological approach, covering many aspects of RNA metabolism, including novel isoforms, poly(A) tails, and RNA modification, and we propose a comprehensive workflow for processing of plant DRS data. Many challenges to the application of DRS in plants, such as the need for machine learning tools tailored to plant transcriptomes, remain to be overcome, and together we outline future biological questions that can be addressed by DRS, such as allele-specific RNA modification. This technology provides convenient support on which the connection of distinct RNA features is tightly built, sustainably refining our understanding of the biological functions of the plant transcriptome.
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Affiliation(s)
- Xi-Tong Zhu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China.
| | - Pablo Sanz-Jimenez
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiao-Tong Ning
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China
| | - Muhammad Tahir Ul Qamar
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Ling-Ling Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China.
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3
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Gong B, Li D, Łabaj PP, Pan B, Novoradovskaya N, Thierry-Mieg D, Thierry-Mieg J, Chen G, Bergstrom Lucas A, LoCoco JS, Richmond TA, Tseng E, Kusko R, Happe S, Mercer TR, Pabón-Peña C, Salmans M, Tilgner HU, Xiao W, Johann DJ, Jones W, Tong W, Mason CE, Kreil DP, Xu J. Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing. Sci Data 2024; 11:892. [PMID: 39152166 PMCID: PMC11329654 DOI: 10.1038/s41597-024-03741-y] [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] [Received: 03/05/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.
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Affiliation(s)
- Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Paweł P Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Bioinformatics Research, Institute of Molecular Biotechnology, Boku University Vienna, Vienna, Austria
| | - Bohu Pan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | | | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Guangchun Chen
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd., Dallas, TX, 75390, USA
| | - Anne Bergstrom Lucas
- Agilent Technologies, Inc., 5301 Stevens Creek Blvd., Santa Clara, CA, 95051, USA
| | | | - Todd A Richmond
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., 4300 Hacienda Dr., Pleasanton, CA, 94588, USA
| | | | - Rebecca Kusko
- Cellino Bio, 750 Main Street, Cambridge, MA, 02143, USA
| | - Scott Happe
- Agilent Technologies, Inc., 1834 State Hwy 71 West, Cedar Creek, TX, 78612, USA
| | - Timothy R Mercer
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD, Australia
| | - Carlos Pabón-Peña
- Agilent Technologies, Inc., 5301 Stevens Creek Blvd., Santa Clara, CA, 95051, USA
| | | | - Hagen U Tilgner
- Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Wenzhong Xiao
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Donald J Johann
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, 4301W Markham St., Little Rock, AR, 72205, USA
| | - Wendell Jones
- Q squared Solutions Genomics, 2400 Elis Road, Durham, NC, 27703, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
| | - David P Kreil
- Bioinformatics Research, Institute of Molecular Biotechnology, Boku University Vienna, Vienna, Austria.
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA.
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4
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Su Y, Yu Z, Jin S, Ai Z, Yuan R, Chen X, Xue Z, Guo Y, Chen D, Liang H, Liu Z, Liu W. Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data. Nat Commun 2024; 15:3972. [PMID: 38730241 PMCID: PMC11087464 DOI: 10.1038/s41467-024-48117-3] [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: 10/18/2022] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
The advancement of Long-Read Sequencing (LRS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing events and isoform expressions. Recently, numerous computational tools for isoform detection using long-read sequencing data have been developed. Nevertheless, there remains a deficiency in comparative studies that systemically evaluate the performance of these tools, which are implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmark analysis of thirteen methods implemented in nine tools capable of identifying isoform structures from long-read RNA-seq data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, RNA sequins (sequencing spike-ins) data, as well as experimental data. Our findings demonstrate IsoQuant as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance. These results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.
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Affiliation(s)
- Yaqi Su
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
| | - Zhejian Yu
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Siqian Jin
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Zhipeng Ai
- Division of Human Reproduction and Developmental Genetics, Women's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Ruihong Yuan
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Xinyi Chen
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Ziwei Xue
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Yixin Guo
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Di Chen
- Center for Reproductive Medicine of the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre for Regeneration and Cell Therapy of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Hongqing Liang
- Division of Human Reproduction and Developmental Genetics, Women's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Zuozhu Liu
- Zhejiang University-Angel Align Inc. R&D Center for Intelligent Healthcare, Zhejiang University-University of Illinois at Urbana-Champaign Institute (ZJU-UIUC Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Wanlu Liu
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China.
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314100, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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5
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Runemark A, Moore EC, Larson EL. Hybridization and gene expression: Beyond differentially expressed genes. Mol Ecol 2024:e17303. [PMID: 38411307 DOI: 10.1111/mec.17303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/06/2024] [Accepted: 02/15/2024] [Indexed: 02/28/2024]
Abstract
Gene expression has a key role in reproductive isolation, and studies of hybrid gene expression have identified mechanisms causing hybrid sterility. Here, we review the evidence for altered gene expression following hybridization and outline the mechanisms shown to contribute to altered gene expression in hybrids. Transgressive gene expression, transcending that of both parental species, is pervasive in early generation sterile hybrids, but also frequently observed in viable, fertile hybrids. We highlight studies showing that hybridization can result in transgressive gene expression, also in established hybrid lineages or species. Such extreme patterns of gene expression in stabilized hybrid taxa suggest that altered hybrid gene expression may result in hybridization-derived evolutionary novelty. We also conclude that while patterns of misexpression in hybrids are well documented, the understanding of the mechanisms causing misexpression is lagging. We argue that jointly assessing differences in cell composition and cell-specific changes in gene expression in hybrids, in addition to assessing changes in chromatin and methylation, will significantly advance our understanding of the basis of altered gene expression. Moreover, uncovering to what extent evolution of gene expression results in altered expression for individual genes, or entire networks of genes, will advance our understanding of how selection moulds gene expression. Finally, we argue that jointly studying the dual roles of altered hybrid gene expression, serving both as a mechanism for reproductive isolation and as a substrate for hybrid ecological adaptation, will lead to significant advances in our understanding of the evolution of gene expression.
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Affiliation(s)
- Anna Runemark
- Department of Biology, Lund University, Lund, Sweden
| | - Emily C Moore
- Department of Biological Sciences, University of Denver, Denver, Colorado, USA
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Erica L Larson
- Department of Biological Sciences, University of Denver, Denver, Colorado, USA
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6
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Mestre-Tomás J, Liu T, Pardo-Palacios F, Conesa A. SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark. Genome Biol 2023; 24:286. [PMID: 38082294 PMCID: PMC10712166 DOI: 10.1186/s13059-023-03127-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Long-read RNA sequencing has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile tool that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field.
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Affiliation(s)
- Jorge Mestre-Tomás
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Camino de Vera, Valencia, 46022, Spain
| | - Tianyuan Liu
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain
| | - Francisco Pardo-Palacios
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain.
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7
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Wang R, Helbig I, Edmondson AC, Lin L, Xing Y. Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis. Brief Bioinform 2023; 24:bbad284. [PMID: 37580177 PMCID: PMC10516351 DOI: 10.1093/bib/bbad284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/10/2023] [Accepted: 07/20/2023] [Indexed: 08/16/2023] Open
Abstract
Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.
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Affiliation(s)
- Robert Wang
- Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ingo Helbig
- The Epilepsy NeuroGenetics Initiative, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew C Edmondson
- Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Lan Lin
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yi Xing
- Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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8
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Engelhard CA, Khani S, Derdak S, Bilban M, Kornfeld JW. Nanopore sequencing unveils the complexity of the cold-activated murine brown adipose tissue transcriptome. iScience 2023; 26:107190. [PMID: 37564700 PMCID: PMC10410515 DOI: 10.1016/j.isci.2023.107190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/28/2023] [Accepted: 06/16/2023] [Indexed: 08/12/2023] Open
Abstract
Alternative transcription increases transcriptome complexity by expression of multiple transcripts per gene. Annotation and quantification of transcripts using short-read sequencing is non-trivial. Long-read sequencing aims at overcoming these problems by sequencing full-length transcripts. Activation of brown adipose tissue (BAT) thermogenesis involves major transcriptomic remodeling and positively affects metabolism via increased energy expenditure. We benchmark Oxford Nanopore Technology (ONT) long-read sequencing protocols to Illumina short-read sequencing assessing alignment characteristics, gene and transcript detection and quantification, differential gene and transcript expression, transcriptome reannotation, and differential transcript usage (DTU). We find ONT sequencing is superior to Illumina for transcriptome reassembly, reducing the risk of false-positive events by unambiguously mapping reads to transcripts. We identified novel isoforms of genes undergoing DTU in cold-activated BAT including Cars2, Adtrp, Acsl5, Scp2, Aldoa, and Pde4d, validated by real-time PCR. The reannotated murine BAT transcriptome established here provides a framework for future investigations into the regulation of BAT.
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Affiliation(s)
- Christoph Andreas Engelhard
- Department for Biochemistry and Molecular Biology (BMB), University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Sajjad Khani
- Max Planck Institute for Metabolism Research, Gleueler Strasse 50, 50931 Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Ageing-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Sophia Derdak
- Core Facilities, Medical University of Vienna, Lazarettgasse 14, 1090 Vienna, Austria
| | - Martin Bilban
- Department of Laboratory Medicine & Core Facilities, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Jan-Wilhelm Kornfeld
- Department for Biochemistry and Molecular Biology (BMB), University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
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9
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Calvo-Roitberg E, Daniels RF, Pai AA. Challenges in identifying mRNA transcript starts and ends from long-read sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550536. [PMID: 37546743 PMCID: PMC10402045 DOI: 10.1101/2023.07.26.550536] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Long-read sequencing (LRS) technologies have the potential to revolutionize scientific discoveries in RNA biology, especially by enabling the comprehensive identification and quantification of full length mRNA isoforms. However, inherently high error rates make the analysis of long-read sequencing data challenging. While these error rates have been characterized for sequence and splice site identification, it is still unclear how accurately LRS reads represent transcript start and end sites. Here, we systematically assess the variability and accuracy of mRNA terminal ends identified by LRS reads across multiple sequencing platforms. We find substantial inconsistencies in both the start and end coordinates of LRS reads spanning a gene, such that LRS reads often fail to accurately recapitulate annotated or empirically derived terminal ends of mRNA molecules. To address this challenge, we introduce an approach to condition reads based on empirically derived terminal ends and identified a subset of reads that are more likely to represent full-length transcripts. Our approach can improve transcriptome analyses by enhancing the fidelity of transcript terminal end identification, but may result in lower power to quantify genes or discover novel isoforms. Thus, it is necessary to be cautious when selecting sequencing approaches and/or interpreting data from long-read RNA sequencing.
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Affiliation(s)
| | - Rachel F Daniels
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA
| | - Athma A Pai
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA
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10
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Wu S, Schmitz U. Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination. Comput Struct Biotechnol J 2023; 21:2373-2380. [PMID: 37066125 PMCID: PMC10091034 DOI: 10.1016/j.csbj.2023.03.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level). In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.
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Affiliation(s)
- Siyuan Wu
- Department of Molecular & Cell Biology, James Cook University, Townsville 4811, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns 4870, Queensland, Australia
- School of Mathematics, Monash University, Melbourne 3800, Victoria, Australia
| | - Ulf Schmitz
- Department of Molecular & Cell Biology, James Cook University, Townsville 4811, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns 4870, Queensland, Australia
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11
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Manuel JM, Guilloy N, Khatir I, Roucou X, Laurent B. Re-evaluating the impact of alternative RNA splicing on proteomic diversity. Front Genet 2023; 14:1089053. [PMID: 36845399 PMCID: PMC9947481 DOI: 10.3389/fgene.2023.1089053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Alternative splicing (AS) constitutes a mechanism by which protein-coding genes and long non-coding RNA (lncRNA) genes produce more than a single mature transcript. From plants to humans, AS is a powerful process that increases transcriptome complexity. Importantly, splice variants produced from AS can potentially encode for distinct protein isoforms which can lose or gain specific domains and, hence, differ in their functional properties. Advances in proteomics have shown that the proteome is indeed diverse due to the presence of numerous protein isoforms. For the past decades, with the help of advanced high-throughput technologies, numerous alternatively spliced transcripts have been identified. However, the low detection rate of protein isoforms in proteomic studies raised debatable questions on whether AS contributes to proteomic diversity and on how many AS events are really functional. We propose here to assess and discuss the impact of AS on proteomic complexity in the light of the technological progress, updated genome annotation, and current scientific knowledge.
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Affiliation(s)
- Jeru Manoj Manuel
- Research Center on Aging, Centre Intégré Universitaire de Santé et Services Sociaux de l’Estrie-Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada,Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Noé Guilloy
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Inès Khatir
- Research Center on Aging, Centre Intégré Universitaire de Santé et Services Sociaux de l’Estrie-Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada,Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Xavier Roucou
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada,Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, QC, Canada,Quebec Network for Research on Protein Function Structure and Engineering, PROTEO, Québec, QC, Canada
| | - Benoit Laurent
- Research Center on Aging, Centre Intégré Universitaire de Santé et Services Sociaux de l’Estrie-Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada,Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada,*Correspondence: Benoit Laurent,
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12
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Leshkowitz D, Kedmi M, Fried Y, Pilzer D, Keren-Shaul H, Ainbinder E, Dassa B. Exploring differential exon usage via short- and long-read RNA sequencing strategies. Open Biol 2022; 12:220206. [PMID: 36168804 PMCID: PMC9516339 DOI: 10.1098/rsob.220206] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Alternative splicing produces various mRNAs, and thereby various protein products, from one gene, impacting a wide range of cellular activities. However, accurate reconstruction and quantification of full-length transcripts using short-reads is limited, due to their length. Long-reads sequencing technologies may provide a solution by sequencing full-length transcripts. We explored the use of both Illumina short-reads and two long Oxford Nanopore Technology (cDNA and Direct RNA) RNA-Seq reads for detecting global differential splicing during mouse embryonic stem cell differentiation, applying several bioinformatics strategies: gene-based, isoform-based and exon-based. We detected the strongest similarity among the sequencing platforms at the gene level compared to exon-based and isoform-based. Furthermore, the exon-based strategy discovered many differential exon usage (DEU) events, mostly in a platform-dependent manner and in non-differentially expressed genes. Thus, the platforms complemented each other in the ability to detect DEUs (i.e. long-reads exhibited an advantage in detecting DEUs at the UTRs, and short-reads detected more DEUs). Exons within 20 genes, detected in one or more platforms, were here validated by PCR, including key differentiation genes, such as Mdb3 and Aplp1. We provide an important analysis resource for discovering transcriptome changes during stem cell differentiation and insights for analysing such data.
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Affiliation(s)
- Dena Leshkowitz
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Merav Kedmi
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yael Fried
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
| | - David Pilzer
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Hadas Keren-Shaul
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Elena Ainbinder
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Bareket Dassa
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
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13
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Wright DJ, Hall NAL, Irish N, Man AL, Glynn W, Mould A, Angeles ADL, Angiolini E, Swarbreck D, Gharbi K, Tunbridge EM, Haerty W. Correction to: Long read sequencing reveals novel isoforms and insights into splicing regulation during cell state changes. BMC Genomics 2022; 23:79. [PMID: 35078420 PMCID: PMC8790846 DOI: 10.1186/s12864-022-08318-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- David J Wright
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK
| | - Nicola A L Hall
- Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxfordshire, OX3 3JX, UK
- Oxford Health, NHS Foundation Trust, Oxford, Oxfordshire, OX3 7JX, UK
| | - Naomi Irish
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK
| | - Angela L Man
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK
| | - Will Glynn
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK
| | - Arne Mould
- Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxfordshire, OX3 3JX, UK
- Oxford Health, NHS Foundation Trust, Oxford, Oxfordshire, OX3 7JX, UK
| | - Alejandro De Los Angeles
- Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxfordshire, OX3 3JX, UK
- Oxford Health, NHS Foundation Trust, Oxford, Oxfordshire, OX3 7JX, UK
| | - Emily Angiolini
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK
| | - David Swarbreck
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK
| | - Karim Gharbi
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK
| | - Elizabeth M Tunbridge
- Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxfordshire, OX3 3JX, UK
- Oxford Health, NHS Foundation Trust, Oxford, Oxfordshire, OX3 7JX, UK
| | - Wilfried Haerty
- Earlham Institute, Norwich Research Park, Norfolk, NR4 7UZ, UK.
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