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Iosub IA, Wilkins OG, Ule J. Riboseq-flow: A streamlined, reliable pipeline for ribosome profiling data analysis and quality control. Wellcome Open Res 2024; 9:179. [PMID: 38846930 PMCID: PMC11153996 DOI: 10.12688/wellcomeopenres.21000.1] [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] [Accepted: 03/12/2024] [Indexed: 06/09/2024] Open
Abstract
Ribosome profiling is a powerful technique to study translation at a transcriptome-wide level. However, ensuring good data quality is paramount for accurate interpretation, as is ensuring that the analyses are reproducible. We introduce a new Nextflow DSL2 pipeline, riboseq-flow, designed for processing and comprehensive quality control of ribosome profiling experiments. Riboseq-flow is user-friendly, versatile and upholds high standards in reproducibility, scalability, portability, version control and continuous integration. It enables users to efficiently analyse multiple samples in parallel and helps them evaluate the quality and utility of their data based on the detailed metrics and visualisations that are automatically generated. Riboseq-flow is available at https://github.com/iraiosub/riboseq-flow.
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Affiliation(s)
- Ira A. Iosub
- The Francis Crick Institute, London, England, UK
- UK Dementia Research Institute at King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oscar G. Wilkins
- The Francis Crick Institute, London, England, UK
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Jernej Ule
- The Francis Crick Institute, London, England, UK
- UK Dementia Research Institute at King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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2
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Su D, Ding C, Qiu J, Yang G, Wang R, Liu Y, Tao J, Luo W, Weng G, Zhang T. Ribosome profiling: a powerful tool in oncological research. Biomark Res 2024; 12:11. [PMID: 38273337 PMCID: PMC10809610 DOI: 10.1186/s40364-024-00562-4] [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: 08/19/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
Neoplastic cells need to adapt their gene expression pattern to survive in an ever-changing or unfavorable tumor microenvironment. Protein synthesis (or mRNA translation), an essential part of gene expression, is dysregulated in cancer. The emergence of distinct translatomic technologies has revolutionized oncological studies to elucidate translational regulatory mechanisms. Ribosome profiling can provide adequate information on diverse aspects of translation by aiding in quantitatively analyzing the intensity of translating ribosome-protected fragments. Here, we review the primary currently used translatomics techniques and highlight their advantages and disadvantages as tools for translatomics studies. Subsequently, we clarified the areas in which ribosome profiling could be applied to better understand translational control. Finally, we summarized the latest advances in cancer studies using ribosome profiling to highlight the extensive application of this powerful and promising translatomic tool.
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Affiliation(s)
- Dan Su
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Chen Ding
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Jiangdong Qiu
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Gang Yang
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Ruobing Wang
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Yueze Liu
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Jinxin Tao
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Wenhao Luo
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College Hospital, Beijing, 100023, P.R. China
| | - Guihu Weng
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China
| | - Taiping Zhang
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P.R. China.
- Key Laboratory of Research in Pancreatic Tumor, Chinese Academy of Medical Sciences, Beijing, 100023, P.R. China.
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3
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Sonia J, Kanodia P, Lozier Z, Miller WA. Ribosome Profiling of Plants. Methods Mol Biol 2024; 2724:139-163. [PMID: 37987904 PMCID: PMC11158114 DOI: 10.1007/978-1-0716-3485-1_11] [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] [Indexed: 11/22/2023]
Abstract
Translation is a key step in control of gene expression, yet most analyses of global responses to a stimulus focus on transcription and the transcriptome. For RNA viruses in particular, which have no DNA-templated transcriptional control, control of viral and host translation is crucial. Here, we describe the method of ribosome profiling (ribo-seq) in plants, applied to virus infection. Ribo-seq is a deep sequencing technique that reveals the translatome by presenting a snapshot of the positions and relative amounts of translating ribosomes on all mRNAs in the cell. In contrast to RNA-seq, a crude cell extract is first digested with ribonuclease to degrade all mRNA not protected by a translating 80S ribosome. The resulting ribosome-protected fragments (RPFs) are deep sequenced. The number of reads mapping to a specific mRNA compared to the standard RNA-seq reads reveals the translational efficiency of that mRNA. Moreover, the precise positions of ribosome pause sites, previously unknown translatable open reading frames, and noncanonical translation events can be characterized quantitatively using ribo-seq. As this technique requires meticulous technique, here we present detailed step-by-step instructions for cell lysate preparation by flash freezing of samples, nuclease digestion of cell lysate, monosome collection by sucrose cushion ultracentrifugation, size-selective RNA extraction and rRNA depletion, library preparation for sequencing and finally quality control of sequenced data. These experimental methods apply to many plant systems, with minor nuclease digestion modifications depending on the plant tissue and species. This protocol should be valuable for studies of plant virus gene expression, and the global translational response to virus infection, or any other biotic or abiotic stress, by the host plant.
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Affiliation(s)
- Jahanara Sonia
- Plant Pathology, Entomology & Microbiology Department, Iowa State University, Ames, IA, USA
- Molecular, Cellular & Developmental Biology, Iowa State University, Ames, IA, USA
| | - Pulkit Kanodia
- Plant Pathology, Entomology & Microbiology Department, Iowa State University, Ames, IA, USA
- Interdepartmental Genetics & Genomics, Iowa State University, Ames, IA, USA
- , Santa Clara, CA, USA
| | - Zachary Lozier
- Plant Pathology, Entomology & Microbiology Department, Iowa State University, Ames, IA, USA
- Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA
| | - W Allen Miller
- Plant Pathology, Entomology & Microbiology Department, Iowa State University, Ames, IA, USA.
- Molecular, Cellular & Developmental Biology, Iowa State University, Ames, IA, USA.
- Interdepartmental Genetics & Genomics, Iowa State University, Ames, IA, USA.
- Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA.
- Biochemistry, Biophysics & Molecular Biology Department, Iowa State University, Ames, IA, USA.
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4
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Zhang B, Bassani-Sternberg M. Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery. J Immunother Cancer 2023; 11:e007073. [PMID: 37899131 PMCID: PMC10619091 DOI: 10.1136/jitc-2023-007073] [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] [Accepted: 07/21/2023] [Indexed: 10/31/2023] Open
Abstract
Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
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Affiliation(s)
- Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
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5
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Moritz RL, Deutsch EW, van Heesch S. What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome? Mol Cell Proteomics 2023; 22:100631. [PMID: 37572790 PMCID: PMC10506109 DOI: 10.1016/j.mcpro.2023.100631] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/21/2023] [Accepted: 08/08/2023] [Indexed: 08/14/2023] Open
Abstract
Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."
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Affiliation(s)
- John R Prensner
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, USA.
| | | | - Leron W Kok
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Agora Center Bugnon 25A, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology (ISB), Seattle, Washington, USA
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington, USA
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Navickas A, Asgharian H, Winkler J, Fish L, Garcia K, Markett D, Dodel M, Culbertson B, Miglani S, Joshi T, Yin K, Nguyen P, Zhang S, Stevers N, Hwang HW, Mardakheh F, Goga A, Goodarzi H. An mRNA processing pathway suppresses metastasis by governing translational control from the nucleus. Nat Cell Biol 2023; 25:892-903. [PMID: 37156909 PMCID: PMC10264242 DOI: 10.1038/s41556-023-01141-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/27/2023] [Indexed: 05/10/2023]
Abstract
Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis; however, its effects on cancer progression remain poorly understood. Here, to address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed dedicated regression-based methods to analyse ribosome profiling and alternative polyadenylation data, and identified heterogeneous nuclear ribonucleoprotein C (HNRNPC) as a translational controller of a specific mRNA regulon. We found that HNRNPC is downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3' untranslated region lengthening and, subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. In addition, the reduced expression of HNRNPC and its regulon is associated with the worse prognosis in breast cancer patient cohorts.
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Affiliation(s)
- Albertas Navickas
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Institut Curie, CNRS UMR3348, INSERM U1278, Orsay, France
| | - Hosseinali Asgharian
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Juliane Winkler
- Department of Cell and Tissue Biology, University of California, San Francisco, CA, USA
| | - Lisa Fish
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Kristle Garcia
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Daniel Markett
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Martin Dodel
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Bruce Culbertson
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Sohit Miglani
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Tanvi Joshi
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Keyi Yin
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Phi Nguyen
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Steven Zhang
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Nicholas Stevers
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Hun-Way Hwang
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Faraz Mardakheh
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Andrei Goga
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Department of Cell and Tissue Biology, University of California, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA.
- Department of Urology, University of California, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Deutsch EW, van Heesch S. What can Ribo-seq and proteomics tell us about the non-canonical proteome? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541049. [PMID: 37292611 PMCID: PMC10245706 DOI: 10.1101/2023.05.16.541049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In brief The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. Highlights Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.
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Affiliation(s)
- John R. Prensner
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | | | - Leron W. Kok
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
| | - Karl R. Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jonathan M. Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Eric W. Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Sebastiaan van Heesch
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
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8
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Xie M, Yang L, Chen G, Wang Y, Xie Z, Wang H. RiboChat: a chat-style web interface for analysis and annotation of ribosome profiling data. Brief Bioinform 2022; 23:6511203. [DOI: 10.1093/bib/bbab559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 11/29/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
Abstract
The increasing volume of ribosome profiling (Ribo-seq) data, computational complexity of its data processing and operational handicap of related analytical procedures present a daunting set of informatics challenges. These impose a substantial barrier to researchers particularly with no or limited bioinformatics expertise in analyzing and decoding translation information from Ribo-seq data, thus driving the need for a new research paradigm for data computation and information extraction. In this knowledge base, we herein present a novel interactive web platform, RiboChat (https://db.cngb.org/ribobench/chat.html), for direct analyzing and annotating Ribo-seq data in the form of a chat conversation. It consists of a user-friendly web interface and a backend cloud-computing service. When typing a data analysis question into the chat window, the object-text detection module will be run to recognize relevant keywords from the input text. Based on the features identified in the input, individual analytics modules are then scored to find the perfect-matching candidate. The corresponding analytics module will be further executed after checking the completion status of the uploading of datasets and configured parameters. Overall, RiboChat represents an important step forward in the emerging direction of next-generation data analytics and will enable the broad research community to conveniently decipher translation information embedded within Ribo-seq data.
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Vazquez-Laslop N, Sharma CM, Mankin A, Buskirk AR. Identifying Small Open Reading Frames in Prokaryotes with Ribosome Profiling. J Bacteriol 2022; 204:e0029421. [PMID: 34339296 PMCID: PMC8765392 DOI: 10.1128/jb.00294-21] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Small proteins encoded by open reading frames (ORFs) shorter than 50 codons (small ORFs [sORFs]) are often overlooked by annotation engines and are difficult to characterize using traditional biochemical techniques. Ribosome profiling has tremendous potential to empirically improve the annotations of prokaryotic genomes. Recent improvements in ribosome profiling methods for bacterial model organisms have revealed many new sORFs in well-characterized genomes. Antibiotics that trap ribosomes just after initiation have played a key role in these developments by allowing the unambiguous identification of the start codons (and, hence, the reading frame) for novel ORFs. Here, we describe these new methods and highlight critical controls and considerations for adapting ribosome profiling to different prokaryotic species.
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Affiliation(s)
- Nora Vazquez-Laslop
- Center for Biomolecular Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Cynthia M. Sharma
- Molecular Infection Biology II, Institute of Molecular Infection Biology, University of Würzburg, Würzburg, Germany
| | - Alexander Mankin
- Center for Biomolecular Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Allen R. Buskirk
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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10
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Oertlin C, Watt K, Ristau J, Larsson O. Anota2seq Analysis for Transcriptome-Wide Studies of mRNA Translation. Methods Mol Biol 2022; 2418:243-268. [PMID: 35119670 DOI: 10.1007/978-1-0716-1920-9_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
mRNA translation plays a critical role in determining proteome composition. In health, regulation of mRNA translation facilitates rapid gene expression responses to intra- and extracellular signals. Moreover, dysregulated mRNA translation is a common feature in disease states, including neurological disorders and cancer. Yet, most studies of gene expression focus on analysis of mRNA levels, leaving variations in translational efficiencies largely uncharacterized. Here, we outline procedures to identify mRNA-selective alterations in translational efficiencies on a transcriptome-wide scale using the anota2seq package. Anota2seq compares expression data originating from translated mRNA to data from matched total mRNA to identify changes in translated mRNA not paralleled by corresponding changes in total mRNA (interpreted as changes in translational efficiencies impacting protein levels), congruent changes in total and translated mRNA (interpreted as changes in transcription and/or mRNA stability), and changes in total mRNA not paralleled by corresponding alterations in translated mRNA (interpreted as translational buffering). To illustrate the functionality of the anota2seq analysis package, we demonstrate a detailed analysis using a polysome-profiling data set quantified by RNA sequencing, revealing that estrogen receptor α modulates gene expression via a type of translational buffering termed offsetting. Notably, this anota2seq analysis procedure is also applicable to ribosome-profiling (RiboSeq) data sets and can be adapted to a variety of other data types and experimental contexts. Finally, we provide guidance for extending anota2seq analysis to examine associations between untranslated regions and altered translational efficiencies as well as targeted cellular functions to gain insights into mechanisms and phenotypic consequences of altered mRNA translation. Thus, this step-by-step manual allows users to interrogate selective changes in mRNA translation on a transcriptome-wide scale using the Bioconductor package anota2seq.
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Affiliation(s)
- Christian Oertlin
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Kathleen Watt
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Johannes Ristau
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden.
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11
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Gobet C, Naef F. Ribo-DT: An automated pipeline for inferring codon dwell times from ribosome profiling data. Methods 2021; 203:10-16. [PMID: 34673173 DOI: 10.1016/j.ymeth.2021.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 11/16/2022] Open
Abstract
Protein synthesis is an energy consuming process characterised as a pivotal and highly regulated step in gene expression. The net protein output is dictated by a combination of translation initiation, elongation and termination rates that have remained difficult to measure. Recently, the development of ribosome profiling has enabled the inference of translation parameters through modelling, as this method informs on the ribosome position along the mRNA. Here, we present an automated, reproducible and portable computational pipeline to infer relative single-codon and codon-pair dwell times as well as gene flux from raw ribosome profiling sequencing data. As a case study, we applied our workflow to a publicly available yeast ribosome profiling dataset consisting of 57 independent gene knockouts related to RNA and tRNA modifications. We uncovered the effects of those modifications on translation elongation and codon selection during decoding. In particular, knocking out mod5 and trm7 increases codon-specific dwell times which indicates their potential tRNA targets, and highlights effects of nucleotide modifications on ribosome decoding rate.
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Affiliation(s)
- Cédric Gobet
- Institute of Bioengineering (IBI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Félix Naef
- Institute of Bioengineering (IBI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
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12
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Andreev DE, Smirnova VV, Shatsky IN. Modifications of Ribosome Profiling that Provide New Data on the Translation Regulation. BIOCHEMISTRY (MOSCOW) 2021; 86:1095-1106. [PMID: 34565313 DOI: 10.1134/s0006297921090054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Ribosome profiling (riboseq) has opened the possibilities for the genome-wide studies of translation in all living organisms. This method is based on deep sequencing of mRNA fragments protected by the ribosomes from hydrolysis by ribonucleases, the so-called ribosomal footprints (RFPs). Ribosomal profiling together with RNA sequencing allows not only to identify with a reasonable accuracy translated reading frames in the transcriptome, but also to track changes in gene expression in response to various stimuli. Notably, ribosomal profiling in its classical version has certain limitations. The size of the selected mRNA fragments is 25-35 nts, while RFPs of other sizes are usually omitted from analysis. Also, ribosomal profiling "averages" the data from all ribosomes and does not allow to study specific ribosomal complexes associated with particular translation factors. However, recently developed modifications of ribosomal profiling provide answers to a number of questions. Thus, it has become possible to analyze not only elongating, but also scanning and reinitiating ribosomes, to study events associated with the collision of ribosomes during mRNA translation, to discover new ways of cotranslational assembly of multisubunit protein complexes during translation, and to selectively isolate ribosomal complexes associated with certain protein factors. New data obtained using these modified approaches provide a better understanding of the mechanisms of translation regulation and the functional roles of translational apparatus components.
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Affiliation(s)
- Dmitry E Andreev
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119992, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997, Russia
| | - Viktoriya V Smirnova
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119992, Russia
| | - Ivan N Shatsky
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119992, Russia.
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13
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Zaheed O, Kiniry SJ, Baranov PV, Dean K. Exploring Evidence of Non-coding RNA Translation With Trips-Viz and GWIPS-Viz Browsers. Front Cell Dev Biol 2021; 9:703374. [PMID: 34490252 PMCID: PMC8416628 DOI: 10.3389/fcell.2021.703374] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/12/2021] [Indexed: 11/21/2022] Open
Abstract
Detection of translation in so-called non-coding RNA provides an opportunity for identification of novel bioactive peptides and microproteins. The main methods used for these purposes are ribosome profiling and mass spectrometry. A number of publicly available datasets already exist for a substantial number of different cell types grown under various conditions, and public data mining is an attractive strategy for identification of translation in non-coding RNAs. Since the analysis of publicly available data requires intensive data processing, several data resources have been created recently for exploring processed publicly available data, such as OpenProt, GWIPS-viz, and Trips-Viz. In this work we provide a detailed demonstration of how to use the latter two tools for exploring experimental evidence for translation of RNAs hitherto classified as non-coding. For this purpose, we use a set of transcripts with substantially different patterns of ribosome footprint distributions. We discuss how certain features of these patterns can be used as evidence for or against genuine translation. During our analysis we concluded that the MTLN mRNA, previously misannotated as lncRNA LINC00116, likely encodes only a short proteoform expressed from shorter RNA transcript variants.
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Affiliation(s)
- Oza Zaheed
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, RAS, Moscow, Russia
| | - Kellie Dean
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
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14
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Kiniry SJ, Judge CE, Michel AM, Baranov PV. Trips-Viz: an environment for the analysis of public and user-generated ribosome profiling data. Nucleic Acids Res 2021; 49:W662-W670. [PMID: 33950201 PMCID: PMC8262740 DOI: 10.1093/nar/gkab323] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/11/2021] [Accepted: 04/20/2021] [Indexed: 02/07/2023] Open
Abstract
Trips-Viz (https://trips.ucc.ie/) is an interactive platform for the analysis and visualization of ribosome profiling (Ribo-Seq) and shotgun RNA sequencing (RNA-seq) data. This includes publicly available and user generated data, hence Trips-Viz can be classified as a database and as a server. As a database it provides access to many processed Ribo-Seq and RNA-seq data aligned to reference transcriptomes which has been expanded considerably since its inception. Here, we focus on the server functionality of Trips-viz which also has been greatly improved. Trips-viz now enables visualisation of proteomics data from a large number of processed mass spectrometry datasets. It can be used to support translation inferred from Ribo-Seq data. Users are now able to upload a custom reference transcriptome as well as data types other than Ribo-Seq/RNA-Seq. Incorporating custom data has been streamlined with RiboGalaxy (https://ribogalaxy.ucc.ie/) integration. The other new functionality is the rapid detection of translated open reading frames (ORFs) through a simple easy to use interface. The analysis of differential expression has been also improved via integration of DESeq2 and Anota2seq in addition to a number of other improvements of existing Trips-viz features.
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Affiliation(s)
- Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Ciara E Judge
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Audrey M Michel
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
- Ribomaps Ltd, Western Gateway Bld, Western Rd, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, RAS, Moscow, Russia
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15
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Wu WS, Tsao YH, Shiue SC, Chen TY, Tseng YY, Tseng JT. A tool for analyzing and visualizing ribo-seq data at the isoform level. BMC Bioinformatics 2021; 22:271. [PMID: 34058988 PMCID: PMC8323483 DOI: 10.1186/s12859-021-04192-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 04/18/2021] [Accepted: 05/12/2021] [Indexed: 11/18/2022] Open
Abstract
Background Translational regulation is one important aspect of gene expression regulation. Dysregulation of translation results in abnormal cell physiology and leads to diseases. Ribosome profiling (RP), also called ribo-seq, is a powerful experimental technique to study translational regulation. It can capture a snapshot of translation by deep sequencing of ribosome-protected mRNA fragments. Many ribosome profiling data processing tools have been developed. However, almost all tools analyze ribosome profiling data at the gene level. Since different isoforms of a gene may produce different proteins with distinct biological functions, it is advantageous to analyze ribosome profiling data at the isoform level. To meet this need, previously we developed a pipeline to analyze 610 public human ribosome profiling data at the isoform level and constructed HRPDviewer database. Results To allow other researchers to use our pipeline as well, here we implement our pipeline as an easy-to-use software tool called RPiso. Compared to Ribomap (a widely used tool which provides isoform-level ribosome profiling analyses), our RPiso (1) estimates isoform abundance more accurately, (2) supports analyses on more species, and (3) provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. Conclusions In this study, we developed RPiso software tool (http://cosbi7.ee.ncku.edu.tw/RPiso/) to provide isoform-level ribosome profiling analyses. RPiso is very easy to install and execute. RPiso also provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. We believe that RPiso is a useful tool for researchers to analyze and visualize their own ribosome profiling data at the isoform level. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04192-7.
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Affiliation(s)
- Wei-Sheng Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan, 701, Taiwan.
| | - Yi-Hong Tsao
- Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, 701, Taiwan
| | - Sheng-Cian Shiue
- Department of Electrical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
| | - Ting-Yu Chen
- Department of Electrical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
| | - Yan-Yuan Tseng
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, 48201, USA
| | - Joseph T Tseng
- Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, 701, Taiwan.
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16
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RiboDoc: A Docker-based package for ribosome profiling analysis. Comput Struct Biotechnol J 2021; 19:2851-2860. [PMID: 34093996 PMCID: PMC8141510 DOI: 10.1016/j.csbj.2021.05.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/03/2021] [Accepted: 05/05/2021] [Indexed: 11/29/2022] Open
Abstract
Ribosome profiling (RiboSeq) has emerged as a powerful technique for studying the genome-wide regulation of translation in various cells. Several steps in the biological protocol have been improved, but the bioinformatics part of RiboSeq suffers from a lack of standardization, preventing the straightforward and complete reproduction of published results. Too many published studies provide insufficient detail about the bioinformatics pipeline used. The broad range of questions that can be asked with RiboSeq makes it difficult to use a single bioinformatics tool. Indeed, many scripts have been published for addressing diverse questions. Here (https://github.com/equipeGST/RiboDoc), we propose a unique tool (for use with multiple operating systems, OS) to standardize the general steps that must be performed systematically in RiboSeq analysis, together with the statistical analysis and quality control of the sample. The data generated can then be exploited with more specific tools. We hope that this tool will help to standardize bioinformatics analyses pipelines in the field of translation.
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17
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Active Ribosome Profiling with RiboLace: From Bench to Data Analysis. Methods Mol Biol 2021. [PMID: 33765277 DOI: 10.1007/978-1-0716-1150-0_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Ribosome profiling is based on the deep sequencing of RNA fragments protected by ribosomes from nuclease digestion. This technique has been extensively used to study translation, with the unique ability to provide information about ribosomes positioning along transcripts at single-nucleotide resolution. Classical ribosome profiling approaches do not distinguish between fragments protected by either actively translating or inactive ribosomes. Here we describe an original method, called active ribosome profiling or RiboLace, which is based on a unique puromycin-containing molecule capable of isolating active ribosomes by means of an antibody-free and tag-free pull-down approach. This method allows reliable estimates of the translational state of any biological system, in high concordance with protein levels. RiboLace can be applied both in vitro and in vivo and generates snapshots of active ribosome footprints at single-nucleotide resolution and genome-wide level. RiboLace data are suitable for the analysis of translated genes, codon-specific translation rates, and local changes in ribosome occupancy profiles.
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18
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Brunet MA, Lucier JF, Levesque M, Leblanc S, Jacques JF, Al-Saedi HRH, Guilloy N, Grenier F, Avino M, Fournier I, Salzet M, Ouangraoua A, Scott M, Boisvert FM, Roucou X. OpenProt 2021: deeper functional annotation of the coding potential of eukaryotic genomes. Nucleic Acids Res 2021; 49:D380-D388. [PMID: 33179748 PMCID: PMC7779043 DOI: 10.1093/nar/gkaa1036] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 12/12/2022] Open
Abstract
OpenProt (www.openprot.org) is the first proteogenomic resource supporting a polycistronic annotation model for eukaryotic genomes. It provides a deeper annotation of open reading frames (ORFs) while mining experimental data for supporting evidence using cutting-edge algorithms. This update presents the major improvements since the initial release of OpenProt. All species support recent NCBI RefSeq and Ensembl annotations, with changes in annotations being reported in OpenProt. Using the 131 ribosome profiling datasets re-analysed by OpenProt to date, non-AUG initiation starts are reported alongside a confidence score of the initiating codon. From the 177 mass spectrometry datasets re-analysed by OpenProt to date, the unicity of the detected peptides is controlled at each implementation. Furthermore, to guide the users, detectability statistics and protein relationships (isoforms) are now reported for each protein. Finally, to foster access to deeper ORF annotation independently of one's bioinformatics skills or computational resources, OpenProt now offers a data analysis platform. Users can submit their dataset for analysis and receive the results from the analysis by OpenProt. All data on OpenProt are freely available and downloadable for each species, the release-based format ensuring a continuous access to the data. Thus, OpenProt enables a more comprehensive annotation of eukaryotic genomes and fosters functional proteomic discoveries.
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Affiliation(s)
- Marie A Brunet
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Université Laval, Quebec City, QC G1V0A6, Canada
| | - Jean-François Lucier
- Center for Computational Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
- Biology Department, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Maxime Levesque
- Center for Computational Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
- Biology Department, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Sébastien Leblanc
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Université Laval, Quebec City, QC G1V0A6, Canada
| | - Jean-Francois Jacques
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Université Laval, Quebec City, QC G1V0A6, Canada
| | - Hassan R H Al-Saedi
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
| | - Noé Guilloy
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Université Laval, Quebec City, QC G1V0A6, Canada
| | - Frederic Grenier
- Center for Computational Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
- Biology Department, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Mariano Avino
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
| | - Isabelle Fournier
- INSERM U1192, Laboratoire Protéomique, Réponse Inflammatoire & Spectrométrie de Masse (PRISM), Université de Lille, F-59000 Lille, France
| | - Michel Salzet
- INSERM U1192, Laboratoire Protéomique, Réponse Inflammatoire & Spectrométrie de Masse (PRISM), Université de Lille, F-59000 Lille, France
| | - Aïda Ouangraoua
- Informatics Department, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Michelle S Scott
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
| | - François-Michel Boisvert
- Department of Immunology and Cellular Biology, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
| | - Xavier Roucou
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, 3201 Jean Mignault, Sherbrooke, QC J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Université Laval, Quebec City, QC G1V0A6, Canada
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19
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Hajj GNM, Nunes PBC, Roffe M. Genome-wide translation patterns in gliomas: An integrative view. Cell Signal 2020; 79:109883. [PMID: 33321181 DOI: 10.1016/j.cellsig.2020.109883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/01/2020] [Accepted: 12/11/2020] [Indexed: 02/06/2023]
Abstract
Gliomas are the most frequent tumors of the central nervous system (CNS) and include the highly malignant glioblastoma (GBM). Characteristically, gliomas have translational control deregulation related to overactivation of signaling pathways such as PI3K/AKT/mTORC1 and Ras/ERK1/2. Thus, mRNA translation appears to play a dominant role in glioma gene expression patterns. The, analysis of genome-wide translated transcripts, together known as the translatome, may reveal important information for understanding gene expression patterns in gliomas. This review provides a brief overview of translational control mechanisms altered in gliomas with a focus on the current knowledge related to the translatomes of glioma cells and murine glioma models. We present an integrative meta-analysis of selected glioma translatome data with the aim of identifying recurrent patterns of gene expression preferentially regulated at the level of translation and obtaining clues regarding the pathological significance of these alterations. Re-analysis of several translatome datasets was performed to compare the translatomes of glioma models with those of their non-tumor counterparts and to document glioma cell responses to radiotherapy and MNK modulation. The role of recurrently altered genes in the context of translational control and tumorigenesis are discussed.
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Affiliation(s)
- Glaucia Noeli Maroso Hajj
- International Research Institute, A.C.Camargo Cancer Center, Rua Taguá, 440, São Paulo ZIP Code: 01508-010, Brazil; National Institute of Oncogenomics and Innovation, Brazil.
| | - Paula Borzino Cordeiro Nunes
- International Research Institute, A.C.Camargo Cancer Center, Rua Taguá, 440, São Paulo ZIP Code: 01508-010, Brazil
| | - Martin Roffe
- International Research Institute, A.C.Camargo Cancer Center, Rua Taguá, 440, São Paulo ZIP Code: 01508-010, Brazil; National Institute of Oncogenomics and Innovation, Brazil.
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20
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Li K, Hope CM, Wang XA, Wang JP. RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data. Nucleic Acids Res 2020; 48:12016-12029. [PMID: 33211868 PMCID: PMC7708064 DOI: 10.1093/nar/gkaa1049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 10/14/2020] [Accepted: 10/20/2020] [Indexed: 12/18/2022] Open
Abstract
Ribosome profiling, also known as Ribo-seq, has become a popular approach to investigate regulatory mechanisms of translation in a wide variety of biological contexts. Ribo-seq not only provides a measurement of translation efficiency based on the relative abundance of ribosomes bound to transcripts, but also has the capacity to reveal dynamic and local regulation at different stages of translation based on positional information of footprints across individual transcripts. While many computational tools exist for the analysis of Ribo-seq data, no method is currently available for rigorous testing of the pattern differences in ribosome footprints. In this work, we develop a novel approach together with an R package, RiboDiPA, for Differential Pattern Analysis of Ribo-seq data. RiboDiPA allows for quick identification of genes with statistically significant differences in ribosome occupancy patterns for model organisms ranging from yeast to mammals. We show that differential pattern analysis reveals information that is distinct and complimentary to existing methods that focus on translational efficiency analysis. Using both simulated Ribo-seq footprint data and three benchmark data sets, we illustrate that RiboDiPA can uncover meaningful pattern differences across multiple biological conditions on a global scale, and pinpoint characteristic ribosome occupancy patterns at single codon resolution.
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Affiliation(s)
- Keren Li
- Department of Statistics, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
| | - C Matthew Hope
- NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,Department of Molecular Biosciences, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
| | - Xiaozhong A Wang
- NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,Department of Molecular Biosciences, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
| | - Ji-Ping Wang
- Department of Statistics, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
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21
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Nersisyan L, Ropat M, Pelechano V. Improved computational analysis of ribosome dynamics from 5'P degradome data using fivepseq. NAR Genom Bioinform 2020; 2:lqaa099. [PMID: 33575643 PMCID: PMC7685019 DOI: 10.1093/nargab/lqaa099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/20/2020] [Accepted: 11/03/2020] [Indexed: 02/07/2023] Open
Abstract
In eukaryotes, 5′–3′ co-translation degradation machinery follows the last translating ribosome providing an in vivo footprint of its position. Thus, 5′ monophosphorylated (5′P) degradome sequencing, in addition to informing about RNA decay, also provides information regarding ribosome dynamics. Multiple experimental methods have been developed to investigate the mRNA degradome; however, computational tools for their reproducible analysis are lacking. Here, we present fivepseq: an easy-to-use application for analysis and interactive visualization of 5′P degradome data. This tool performs both metagene- and gene-specific analysis, and enables easy investigation of codon-specific ribosome pauses. To demonstrate its ability to provide new biological information, we investigate gene-specific ribosome pauses in Saccharomyces cerevisiae after eIF5A depletion. In addition to identifying pauses at expected codon motifs, we identify multiple genes with strain-specific degradation frameshifts. To show its wide applicability, we investigate 5′P degradome from Arabidopsis thaliana and discover both motif-specific ribosome protection associated with particular developmental stages and generally increased ribosome protection at termination level associated with age. Our work shows how the use of improved analysis tools for the study of 5′P degradome can significantly increase the biological information that can be derived from such datasets and facilitate its reproducible analysis.
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Affiliation(s)
- Lilit Nersisyan
- SciLifeLab, Department of Microbiology, Tumor and Cell Biology. Karolinska Institutet, Solna 171 65, Sweden
| | - Maria Ropat
- SciLifeLab, Department of Microbiology, Tumor and Cell Biology. Karolinska Institutet, Solna 171 65, Sweden
| | - Vicent Pelechano
- SciLifeLab, Department of Microbiology, Tumor and Cell Biology. Karolinska Institutet, Solna 171 65, Sweden
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22
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Kage U, Powell JJ, Gardiner DM, Kazan K. Ribosome profiling in plants: what is not lost in translation? JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:5323-5332. [PMID: 32459844 DOI: 10.1093/jxb/eraa227] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 05/05/2020] [Indexed: 05/03/2023]
Abstract
Translation is a highly dynamic cellular process whereby genetic information residing in an mRNA molecule is converted into a protein that in turn executes specific functions. However, pre-synthesized mRNA levels do not always correlate with corresponding protein levels, suggesting that translational control plays an essential role in gene regulation. A better understanding of how gene expression is regulated during translation will enable the discovery of new genes and mechanisms that control important traits in plants. Therefore, in recent years, several methods have been developed to analyse the translatome; that is, all mRNAs being actively translated at a given time, tissue, and/or developmental stage. Ribosome profiling or ribo-seq is one such technology revolutionizing our ability to analyse the translatome and in turn understand translational control of gene expression. Ribo-seq involves isolating mRNA-ribosome complexes, treating them with a RNase, and then identifying ribosome-protected mRNA regions by deep sequencing. Here, we briefly review recent ribosome profiling studies that revealed new insights into plant biology. Manipulation of novel genes identified using ribosome profiling could prove useful for increasing yield through improved biotic and abiotic stress tolerance.
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Affiliation(s)
- Udaykumar Kage
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, St Lucia, QLD, Australia
| | - Jonathan J Powell
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, St Lucia, QLD, Australia
| | - Donald M Gardiner
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, St Lucia, QLD, Australia
| | - Kemal Kazan
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, St Lucia, QLD, Australia
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