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Bagheri A, Astafev A, Al-Hashimy T, Jiang P. Tracing Translational Footprint by Ribo-Seq: Principle, Workflow, and Applications to Understand the Mechanism of Human Diseases. Cells 2022; 11:cells11192966. [PMID: 36230928 PMCID: PMC9562884 DOI: 10.3390/cells11192966] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/02/2022] [Accepted: 09/19/2022] [Indexed: 11/30/2022] Open
Abstract
RNA-seq has been widely used as a high-throughput method to characterize transcript dynamic changes in a broad context, such as development and diseases. However, whether RNA-seq-estimated transcriptional dynamics can be translated into protein level changes is largely unknown. Ribo-seq (Ribosome profiling) is an emerging technology that allows for the investigation of the translational footprint via profiling ribosome-bounded mRNA fragments. Ribo-seq coupled with RNA-seq will allow us to understand the transcriptional and translational control of the fundamental biological process and human diseases. This review focuses on discussing the principle, workflow, and applications of Ribo-seq to study human diseases.
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Affiliation(s)
- Atefeh Bagheri
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
- Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, Cleveland, OH 44115, USA
| | - Artem Astafev
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
- Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, Cleveland, OH 44115, USA
| | - Tara Al-Hashimy
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
| | - Peng Jiang
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
- Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, Cleveland, OH 44115, USA
- Center for Applied Data Analysis and Modeling (ADAM), Cleveland State University, Cleveland, OH 44115, USA
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
- Correspondence: ; Tel.: +1-(216)-687-3917
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2
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Parmar BS, Peeters MKR, Boonen K, Clark EC, Baggerman G, Menschaert G, Temmerman L. Identification of Non-Canonical Translation Products in C. elegans Using Tandem Mass Spectrometry. Front Genet 2021; 12:728900. [PMID: 34759956 PMCID: PMC8575065 DOI: 10.3389/fgene.2021.728900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/16/2021] [Indexed: 11/22/2022] Open
Abstract
Transcriptome and ribosome sequencing have revealed the existence of many non-canonical transcripts, mainly containing splice variants, ncRNA, sORFs and altORFs. However, identification and characterization of products that may be translated out of these remains a challenge. Addressing this, we here report on 552 non-canonical proteins and splice variants in the model organism C. elegans using tandem mass spectrometry. Aided by sequencing-based prediction, we generated a custom proteome database tailored to search for non-canonical translation products of C. elegans. Using this database, we mined available mass spectrometric resources of C. elegans, from which 51 novel, non-canonical proteins could be identified. Furthermore, we utilized diverse proteomic and peptidomic strategies to detect 40 novel non-canonical proteins in C. elegans by LC-TIMS-MS/MS, of which 6 were common with our meta-analysis of existing resources. Together, this permits us to provide a resource with detailed annotation of 467 splice variants and 85 novel proteins mapped onto UTRs, non-coding regions and alternative open reading frames of the C. elegans genome.
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Affiliation(s)
- Bhavesh S. Parmar
- Animal Physiology and Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium
| | - Marlies K. R. Peeters
- Laboratory of Bioinformatics and Computational Genomics (BioBix), Department of Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kurt Boonen
- Centre for Proteomics (CFP), University of Antwerp, Antwerp, Belgium
| | - Ellie C. Clark
- Animal Physiology and Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium
| | - Geert Baggerman
- Centre for Proteomics (CFP), University of Antwerp, Antwerp, Belgium
| | - Gerben Menschaert
- Laboratory of Bioinformatics and Computational Genomics (BioBix), Department of Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Liesbet Temmerman
- Animal Physiology and Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium
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3
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Vangala D, Ladigan S, Liffers ST, Noseir S, Maghnouj A, Götze TM, Verdoodt B, Klein-Scory S, Godfrey L, Zowada MK, Huerta M, Edelstein DL, de Villarreal JM, Marqués M, Kumbrink J, Jung A, Schiergens T, Werner J, Heinemann V, Stintzing S, Lindoerfer D, Mansmann U, Pohl M, Teschendorf C, Bernhardt C, Wolters H, Stern J, Usta S, Viebahn R, Admard J, Casadei N, Fröhling S, Ball CR, Siveke JT, Glimm H, Tannapfel A, Schmiegel W, Hahn SA. Secondary resistance to anti-EGFR therapy by transcriptional reprogramming in patient-derived colorectal cancer models. Genome Med 2021; 13:116. [PMID: 34271981 PMCID: PMC8283888 DOI: 10.1186/s13073-021-00926-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The development of secondary resistance (SR) in metastatic colorectal cancer (mCRC) treated with anti-epidermal growth factor receptor (anti-EGFR) antibodies is not fully understood at the molecular level. Here we tested in vivo selection of anti-EGFR SR tumors in CRC patient-derived xenograft (PDX) models as a strategy for a molecular dissection of SR mechanisms. METHODS We analyzed 21 KRAS, NRAS, BRAF, and PI3K wildtype CRC patient-derived xenograft (PDX) models for their anti-EGFR sensitivity. Furthermore, 31 anti-EGFR SR tumors were generated via chronic in vivo treatment with cetuximab. A multi-omics approach was employed to address molecular primary and secondary resistance mechanisms. Gene set enrichment analyses were used to uncover SR pathways. Targeted therapy of SR PDX models was applied to validate selected SR pathways. RESULTS In vivo anti-EGFR SR could be established with high efficiency. Chronic anti-EGFR treatment of CRC PDX tumors induced parallel evolution of multiple resistant lesions with independent molecular SR mechanisms. Mutations in driver genes explained SR development in a subgroup of CRC PDX models, only. Transcriptional reprogramming inducing anti-EGFR SR was discovered as a common mechanism in CRC PDX models frequently leading to RAS signaling pathway activation. We identified cAMP and STAT3 signaling activation, as well as paracrine and autocrine signaling via growth factors as novel anti-EGFR secondary resistance mechanisms. Secondary resistant xenograft tumors could successfully be treated by addressing identified transcriptional changes by tailored targeted therapies. CONCLUSIONS Our study demonstrates that SR PDX tumors provide a unique platform to study molecular SR mechanisms and allow testing of multiple treatments for efficient targeting of SR mechanisms, not possible in the patient. Importantly, it suggests that the development of anti-EGFR tolerant cells via transcriptional reprogramming as a cause of anti-EGFR SR in CRC is likely more prevalent than previously anticipated. It emphasizes the need for analyses of SR tumor tissues at a multi-omics level for a comprehensive molecular understanding of anti-EGFR SR in CRC.
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Affiliation(s)
- Deepak Vangala
- Department of Molecular GI Oncology, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany
- Department of Internal Medicine, Ruhr University Bochum, Knappschaftskrankenhaus, Bochum, Germany
| | - Swetlana Ladigan
- Department of Molecular GI Oncology, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany
- Department of Internal Medicine, Ruhr University Bochum, Knappschaftskrankenhaus, Bochum, Germany
| | - Sven T Liffers
- Institute of Pathology, Ruhr University of Bochum, Bochum, Germany
- Present Address Division of Solid Tumor Translational Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany
| | - Soha Noseir
- Department of Molecular GI Oncology, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany
| | - Abdelouahid Maghnouj
- Department of Molecular GI Oncology, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany
| | - Tina-Maria Götze
- Department of Molecular GI Oncology, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany
| | | | - Susanne Klein-Scory
- Department of Internal Medicine, Ruhr University Bochum, Knappschaftskrankenhaus, Bochum, Germany
| | - Laura Godfrey
- Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, Essen, Germany
- Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK, partner site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - Martina K Zowada
- Translational Functional Cancer Genomics, NCT Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Mario Huerta
- Translational Functional Cancer Genomics, NCT Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT), Dresden, and German Cancer Research Center (DKFZ), Dresden, Germany
| | | | | | - Miriam Marqués
- Epithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO) and CIBERONC, Madrid, Spain
| | - Jörg Kumbrink
- Institute of Pathology, Ludwig Maximilian University (LMU), Munich, Germany
- German Cancer Consortium (DKTK, partner site Munich), Munich, Germany
| | - Andreas Jung
- Institute of Pathology, Ludwig Maximilian University (LMU), Munich, Germany
- German Cancer Consortium (DKTK, partner site Munich), Munich, Germany
| | - Tobias Schiergens
- Department of General, Visceral, and Transplantation Surgery, University Hospital, LMU Munich, Munich, Germany
| | - Jens Werner
- Department of General, Visceral, and Transplantation Surgery, University Hospital, LMU Munich, Munich, Germany
| | - Volker Heinemann
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Sebastian Stintzing
- Department of Hematology, Oncology, and Tumor Immunology (CCM) Charité Universitaetsmedizin Berlin, Berlin, Germany
| | - Doris Lindoerfer
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Michael Pohl
- Department of Internal Medicine, Ruhr University Bochum, Knappschaftskrankenhaus, Bochum, Germany
| | | | | | - Heiner Wolters
- Department of Visceral and General Surgery, St. Josef Hospital, Dortmund, Germany
| | - Josef Stern
- Department of Visceral and General Surgery, St. Josef Hospital, Dortmund, Germany
| | - Selami Usta
- Department of Visceral and General Surgery, St. Josef Hospital, Dortmund, Germany
| | - Richard Viebahn
- Department of Surgery, Ruhr University Bochum, Knappschaftskrankenhaus, Bochum, Germany
| | - Jacob Admard
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Nicolas Casadei
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Stefan Fröhling
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Deptartment of Translational Medical Oncology, NCT Heidelberg and German Cancer Research Center, Heidelberg, Germany
| | - Claudia R Ball
- Translational Functional Cancer Genomics, NCT Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT), Dresden, and German Cancer Research Center (DKFZ), Dresden, Germany
- Center for Personalized Oncology, NCT Dresden and University Hospital Carl Gustav Carus Dresden at TU Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
| | - Jens T Siveke
- Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, Essen, Germany
- Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK, partner site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - Hanno Glimm
- Translational Functional Cancer Genomics, NCT Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT), Dresden, and German Cancer Research Center (DKFZ), Dresden, Germany
- Center for Personalized Oncology, NCT Dresden and University Hospital Carl Gustav Carus Dresden at TU Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Dresden, Germany
| | - Andrea Tannapfel
- Institute of Pathology, Ruhr University of Bochum, Bochum, Germany
| | - Wolff Schmiegel
- Department of Internal Medicine, Ruhr University Bochum, Knappschaftskrankenhaus, Bochum, Germany
| | - Stephan A Hahn
- Department of Molecular GI Oncology, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany.
- Department of Internal Medicine, Ruhr University Bochum, Knappschaftskrankenhaus, Bochum, Germany.
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Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics 2021; 20:100076. [PMID: 33823297 PMCID: PMC8214147 DOI: 10.1016/j.mcpro.2021.100076] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 11/17/2022] Open
Abstract
Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting. First proteogenomics with PSM rescoring using machine learning–predicted spectra Demonstrated on both ribosome profiling and nanopore RNA-Seq–derived databases Rescoring leads to elevated stringency and increased identification rates Rescoring compensates for the search space size issues in proteogenomics
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Affiliation(s)
- Steven Verbruggen
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; OHMX.bio, Ghent, Belgium
| | - Siegfried Gessulat
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Ralf Gabriels
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | | | | | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Sven Degroeve
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Lennart Martens
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Wim Van Criekinge
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Gerben Menschaert
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; OHMX.bio, Ghent, Belgium.
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5
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Liu Q, Shvarts T, Sliz P, Gregory RI. RiboToolkit: an integrated platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution. Nucleic Acids Res 2020; 48:W218-W229. [PMID: 32427338 PMCID: PMC7319539 DOI: 10.1093/nar/gkaa395] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/23/2020] [Accepted: 05/15/2020] [Indexed: 12/31/2022] Open
Abstract
Ribosome profiling (Ribo-seq) is a powerful technology for globally monitoring RNA translation; ranging from codon occupancy profiling, identification of actively translated open reading frames (ORFs), to the quantification of translational efficiency under various physiological or experimental conditions. However, analyzing and decoding translation information from Ribo-seq data is not trivial. Although there are many existing tools to analyze Ribo-seq data, most of these tools are designed for specific or limited functionalities and an easy-to-use integrated tool to analyze Ribo-seq data is lacking. Fortunately, the small size (26–34 nt) of ribosome protected fragments (RPFs) in Ribo-seq and the relatively small amount of sequencing data greatly facilitates the development of such a web platform, which is easy to manipulate for users with or without bioinformatic expertise. Thus, we developed RiboToolkit (http://rnabioinfor.tch.harvard.edu/RiboToolkit), a convenient, freely available, web-based service to centralize Ribo-seq data analyses, including data cleaning and quality evaluation, expression analysis based on RPFs, codon occupancy, translation efficiency analysis, differential translation analysis, functional annotation, translation metagene analysis, and identification of actively translated ORFs. Besides, easy-to-use web interfaces were developed to facilitate data analysis and intuitively visualize results. Thus, RiboToolkit will greatly facilitate the study of mRNA translation based on ribosome profiling.
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Affiliation(s)
- Qi Liu
- Stem Cell Program, Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Tanya Shvarts
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Piotr Sliz
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Richard I Gregory
- Stem Cell Program, Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Harvard Initiative for RNA Medicine, Boston, MA 02115, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA
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6
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Li F, Xing X, Xiao Z, Xu G, Yang X. RiboMiner: a toolset for mining multi-dimensional features of the translatome with ribosome profiling data. BMC Bioinformatics 2020; 21:340. [PMID: 32738892 PMCID: PMC7430821 DOI: 10.1186/s12859-020-03670-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/20/2020] [Indexed: 02/08/2023] Open
Abstract
Background Ribosome profiling has been widely used for studies of translation under a large variety of cellular and physiological contexts. Many of these studies have greatly benefitted from a series of data-mining tools designed for dissection of the translatome from different aspects. However, as the studies of translation advance quickly, the current toolbox still falls in short, and more specialized tools are in urgent need for deeper and more efficient mining of the important and new features of the translation landscapes. Results Here, we present RiboMiner, a bioinformatics toolset for mining of multi-dimensional features of the translatome with ribosome profiling data. RiboMiner performs extensive quality assessment of the data and integrates a spectrum of tools for various metagene analyses of the ribosome footprints and for detailed analyses of multiple features related to translation regulation. Visualizations of all the results are available. Many of these analyses have not been provided by previous methods. RiboMiner is highly flexible, as the pipeline could be easily adapted and customized for different scopes and targets of the studies. Conclusions Applications of RiboMiner on two published datasets did not only reproduced the main results reported before, but also generated novel insights into the translation regulation processes. Therefore, being complementary to the current tools, RiboMiner could be a valuable resource for dissections of the translation landscapes and the translation regulations by mining the ribosome profiling data more comprehensively and with higher resolution. RiboMiner is freely available at https://github.com/xryanglab/RiboMiner and https://pypi.org/project/RiboMiner.
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Affiliation(s)
- Fajin Li
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China.,Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China.,Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, 100084, China
| | - Xudong Xing
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China.,Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China.,Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing, 100084, China
| | - Zhengtao Xiao
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China.,Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Gang Xu
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China. .,Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China.
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7
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Kiniry SJ, Michel AM, Baranov PV. Computational methods for ribosome profiling data analysis. WILEY INTERDISCIPLINARY REVIEWS. RNA 2020; 11:e1577. [PMID: 31760685 DOI: 10.1002/wrna.1577] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 10/12/2019] [Accepted: 10/16/2019] [Indexed: 12/15/2022]
Abstract
Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.
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Affiliation(s)
- Stephen J Kiniry
- 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
| | - 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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8
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Legrand C, Tuorto F. RiboVIEW: a computational framework for visualization, quality control and statistical analysis of ribosome profiling data. Nucleic Acids Res 2020; 48:e7. [PMID: 31777932 PMCID: PMC6954398 DOI: 10.1093/nar/gkz1074] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 10/15/2019] [Accepted: 11/04/2019] [Indexed: 12/18/2022] Open
Abstract
Recently, newly developed ribosome profiling methods based on high-throughput sequencing of ribosome-protected mRNA footprints allow to study genome-wide translational changes in detail. However, computational analysis of the sequencing data still represents a bottleneck for many laboratories. Further, specific pipelines for quality control and statistical analysis of ribosome profiling data, providing high levels of both accuracy and confidence, are currently lacking. In this study, we describe automated bioinformatic and statistical diagnoses to perform robust quality control of ribosome profiling data (RiboQC), to efficiently visualize ribosome positions and to estimate ribosome speed (RiboMine) in an unbiased way. We present an R pipeline to setup and undertake the analyses that offers the user an HTML page to scan own data regarding the following aspects: periodicity, ligation and digestion of footprints; reproducibility and batch effects of replicates; drug-related artifacts; unbiased codon enrichment including variability between mRNAs, for A, P and E sites; mining of some causal or confounding factors. We expect our pipeline to allow an optimal use of the wealth of information provided by ribosome profiling experiments.
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Affiliation(s)
- Carine Legrand
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.,Independent researcher, Kreuzstr. 5, 68259 Mannheim, Germany
| | - Francesca Tuorto
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
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9
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XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data. PLoS Comput Biol 2020; 16:e1007625. [PMID: 32004313 PMCID: PMC7015430 DOI: 10.1371/journal.pcbi.1007625] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 02/12/2020] [Accepted: 12/20/2019] [Indexed: 11/19/2022] Open
Abstract
Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit’s performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourself software is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues.
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Verbruggen S, Ndah E, Van Criekinge W, Gessulat S, Kuster B, Wilhelm M, Van Damme P, Menschaert G. PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms. Mol Cell Proteomics 2019; 18:S126-S140. [PMID: 31040227 PMCID: PMC6692777 DOI: 10.1074/mcp.ra118.001218] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 04/30/2019] [Indexed: 12/20/2022] Open
Abstract
PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation of data-specific translation product candidates and these can improve the mass spectrometry-based identification. Since its first publication, different upgrades, new features and extensions have been added to the PROTEOFORMER pipeline. Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data pre-exploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. The different proteoform calling strategies are used alongside one another and in the end combined together with reference sequences from UniProt. Matching mass spectrometry data are searched against this extended search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline leads to the identification and validation of different categories of new proteoforms, including translation products of up- and downstream open reading frames, 5' and 3' extended and truncated proteoforms, single amino acid variants, splice variants and translation products of so-called noncoding regions. Further, proof-of-concept is reported for the improvement of spectrum matching by including Prosit, a deep neural network strategy that adds extra fragmentation spectrum intensity features to the analysis. In the light of ribosome profiling-driven proteogenomics, it is shown that this allows validating the spectrum matches of newly identified proteoforms with elevated stringency. These updates and novel conclusions provide new insights and lessons for the ribosome profiling-based proteogenomic research field. More practical information on the pipeline, raw code, the user manual (README) and explanations on the different modes of availability can be found at the GitHub repository of PROTEOFORMER: https://github.com/Biobix/proteoformer.
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Affiliation(s)
- Steven Verbruggen
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
| | - Elvis Ndah
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
| | - Wim Van Criekinge
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Siegfried Gessulat
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Munich, Germany; SAP SE, Potsdam, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Munich, Germany
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Munich, Germany
| | - Petra Van Damme
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biochemistry and Microbiology, Faculty of Sciences, Ghent University, Ghent, Belgium
| | - Gerben Menschaert
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
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Fallmann J, Videm P, Bagnacani A, Batut B, Doyle MA, Klingstrom T, Eggenhofer F, Stadler PF, Backofen R, Grüning B. The RNA workbench 2.0: next generation RNA data analysis. Nucleic Acids Res 2019; 47:W511-W515. [PMID: 31073612 PMCID: PMC6602469 DOI: 10.1093/nar/gkz353] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 04/11/2019] [Accepted: 04/29/2019] [Indexed: 12/30/2022] Open
Abstract
RNA has become one of the major research topics in molecular biology. As a central player in key processes regulating gene expression, RNA is in the focus of many efforts to decipher the pathways that govern the transition of genetic information to a fully functional cell. As more and more researchers join this endeavour, there is a rapidly growing demand for comprehensive collections of tools that cover the diverse layers of RNA-related research. However, increasing amounts of data, from diverse types of experiments, addressing different aspects of biological questions need to be consolidated and integrated into a single framework. Only then is it possible to connect findings from e.g. RNA-Seq experiments and methods for e.g. target predictions. To address these needs, we present the RNA Workbench 2.0 , an updated online resource for RNA related analysis. With the RNA Workbench we created a comprehensive set of analysis tools and workflows that enables researchers to analyze their data without the need for sophisticated command-line skills. This update takes the established framework to the next level, providing not only a containerized infrastructure for analysis, but also a ready-to-use platform for hands-on training, analysis, data exploration, and visualization. The new framework is available at https://rna.usegalaxy.eu , and login is free and open to all users. The containerized version can be found at https://github.com/bgruening/galaxy-rna-workbench.
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Affiliation(s)
- Jörg Fallmann
- Bioinformatics Group, Department of Computer Science; Leipzig University, Härtelstraße 16-18, D-04107 Leipzig
| | - Pavankumar Videm
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
| | - Andrea Bagnacani
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany
| | - Bérénice Batut
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
| | - Maria A Doyle
- Research Computing Facility, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia
| | - Tomas Klingstrom
- SLU-Global Bioinformatics Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences
| | - Florian Eggenhofer
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science; Leipzig University, Härtelstraße 16-18, D-04107 Leipzig
- Interdisciplinary Center of Bioinformatics; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; Competence Center for Scalable Data Services and Solutions; and Leipzig Research Center for Civilization Diseases, Leipzig University, Härtelstraße 16-18, D-04107 Leipzig
- Max-Planck-Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig Inst. f. Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria; Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Colombia Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
- Signalling Research Centres BIOSS and CIBSS, Albert-Ludwigs-University Freiburg, Schänzlestr. 18, Freiburg 79104, Germany
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, Habsburgerstr. 49, 79104 Freiburg, Germany
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