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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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2
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Cirzi C, Dyckow J, Legrand C, Schott J, Guo W, Perez Hernandez D, Hisaoka M, Parlato R, Pitzer C, van der Hoeven F, Dittmar G, Helm M, Stoecklin G, Schirmer L, Lyko F, Tuorto F. Queuosine-tRNA promotes sex-dependent learning and memory formation by maintaining codon-biased translation elongation speed. EMBO J 2023; 42:e112507. [PMID: 37609797 PMCID: PMC10548180 DOI: 10.15252/embj.2022112507] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 07/26/2023] [Accepted: 07/28/2023] [Indexed: 08/24/2023] Open
Abstract
Queuosine (Q) is a modified nucleoside at the wobble position of specific tRNAs. In mammals, queuosinylation is facilitated by queuine uptake from the gut microbiota and is introduced into tRNA by the QTRT1-QTRT2 enzyme complex. By establishing a Qtrt1 knockout mouse model, we discovered that the loss of Q-tRNA leads to learning and memory deficits. Ribo-Seq analysis in the hippocampus of Qtrt1-deficient mice revealed not only stalling of ribosomes on Q-decoded codons, but also a global imbalance in translation elongation speed between codons that engage in weak and strong interactions with their cognate anticodons. While Q-dependent molecular and behavioral phenotypes were identified in both sexes, female mice were affected more severely than males. Proteomics analysis confirmed deregulation of synaptogenesis and neuronal morphology. Together, our findings provide a link between tRNA modification and brain functions and reveal an unexpected role of protein synthesis in sex-dependent cognitive performance.
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Affiliation(s)
- Cansu Cirzi
- Division of Epigenetics, DKFZ‐ZMBH AllianceGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Julia Dyckow
- Department of Neurology, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
- Interdisciplinary Center for NeurosciencesHeidelberg UniversityHeidelbergGermany
| | - Carine Legrand
- Division of Epigenetics, DKFZ‐ZMBH AllianceGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Université Paris Cité, Génomes, Biologie Cellulaire et Thérapeutique U944, INSERM, CNRSParisFrance
| | - Johanna Schott
- Center for Molecular Biology of Heidelberg University (ZMBH)DKFZ‐ZMBH AllianceHeidelbergGermany
- Division of Biochemistry, Mannheim Institute for Innate Immunoscience (MI3), Mannheim Cancer Center (MCC), Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Wei Guo
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
- Center for Molecular Biology of Heidelberg University (ZMBH)DKFZ‐ZMBH AllianceHeidelbergGermany
- Division of Biochemistry, Mannheim Institute for Innate Immunoscience (MI3), Mannheim Cancer Center (MCC), Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | | | - Miharu Hisaoka
- Center for Molecular Biology of Heidelberg University (ZMBH)DKFZ‐ZMBH AllianceHeidelbergGermany
- Division of Biochemistry, Mannheim Institute for Innate Immunoscience (MI3), Mannheim Cancer Center (MCC), Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Rosanna Parlato
- Division of Neurodegenerative Disorders, Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational NeurosciencesHeidelberg UniversityMannheimGermany
| | - Claudia Pitzer
- Interdisciplinary Neurobehavioral Core (INBC), Medical Faculty HeidelbergHeidelberg UniversityHeidelbergGermany
| | | | - Gunnar Dittmar
- Department of Infection and ImmunityLuxembourg Institute of HealthStrassenLuxembourg
- Department of Life Sciences and MedicineUniversity of LuxembourgLuxembourg
| | - Mark Helm
- Institute of Pharmaceutical and Biomedical Science (IPBS)Johannes Gutenberg‐University MainzMainzGermany
| | - Georg Stoecklin
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
- Center for Molecular Biology of Heidelberg University (ZMBH)DKFZ‐ZMBH AllianceHeidelbergGermany
- Division of Biochemistry, Mannheim Institute for Innate Immunoscience (MI3), Mannheim Cancer Center (MCC), Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Lucas Schirmer
- Department of Neurology, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
- Interdisciplinary Center for NeurosciencesHeidelberg UniversityHeidelbergGermany
- Mannheim Center for Translational Neuroscience and Institute for Innate Immunoscience, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Frank Lyko
- Division of Epigenetics, DKFZ‐ZMBH AllianceGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Francesca Tuorto
- Division of Epigenetics, DKFZ‐ZMBH AllianceGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Center for Molecular Biology of Heidelberg University (ZMBH)DKFZ‐ZMBH AllianceHeidelbergGermany
- Division of Biochemistry, Mannheim Institute for Innate Immunoscience (MI3), Mannheim Cancer Center (MCC), Medical Faculty MannheimHeidelberg UniversityMannheimGermany
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3
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Liu Q, Peng X, Shen M, Qian Q, Xing J, Li C, Gregory R. Ribo-uORF: a comprehensive data resource of upstream open reading frames (uORFs) based on ribosome profiling. Nucleic Acids Res 2023; 51:D248-D261. [PMID: 36440758 PMCID: PMC9825487 DOI: 10.1093/nar/gkac1094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/27/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
Upstream open reading frames (uORFs) are typically defined as translation sites located within the 5' untranslated region upstream of the main protein coding sequence (CDS) of messenger RNAs (mRNAs). Although uORFs are prevalent in eukaryotic mRNAs and modulate the translation of downstream CDSs, a comprehensive resource for uORFs is currently lacking. We developed Ribo-uORF (http://rnainformatics.org.cn/RiboUORF) to serve as a comprehensive functional resource for uORF analysis based on ribosome profiling (Ribo-seq) data. Ribo-uORF currently supports six species: human, mouse, rat, zebrafish, fruit fly, and worm. Ribo-uORF includes 501 554 actively translated uORFs and 107 914 upstream translation initiation sites (uTIS), which were identified from 1495 Ribo-seq and 77 quantitative translation initiation sequencing (QTI-seq) datasets, respectively. We also developed mRNAbrowse to visualize items such as uORFs, cis-regulatory elements, genetic variations, eQTLs, GWAS-based associations, RNA modifications, and RNA editing. Ribo-uORF provides a very intuitive web interface for conveniently browsing, searching, and visualizing uORF data. Finally, uORFscan and UTR5var were developed in Ribo-uORF to precisely identify uORFs and analyze the influence of genetic mutations on uORFs using user-uploaded datasets. Ribo-uORF should greatly facilitate studies of uORFs and their roles in mRNA translation and posttranscriptional control of gene expression.
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Affiliation(s)
- Qi Liu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Xin Peng
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Mengyuan Shen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Qian Qian
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Junlian Xing
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Chen Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - 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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4
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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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5
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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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6
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Legrand C, Duc KD, Tuorto F. Analysis of Ribosome Profiling Data. Methods Mol Biol 2022; 2428:133-156. [PMID: 35171478 DOI: 10.1007/978-1-0716-1975-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ribosome profiling methods are based on high-throughput sequencing of ribosome-protected mRNA footprints and allow to study in detail translational changes. Bioinformatic and statistical tools are necessary to analyze sequencing data. Here, we describe our developed methods for a fast and reliable quality control of ribosome profiling data, to efficiently visualize ribosome positions and to estimate ribosome speed in an unbiased way. The methodology described here is applicable to several genetic and environmental conditions including stress and are based on the R package RiboVIEW and calculation of quantitative estimates of local and global translation speed, based on a biophysical model of translation dynamics.
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Affiliation(s)
| | - Khanh Dao Duc
- Department of Mathematics, University of British Columbia, Vancouver, BC, Canada
| | - Francesca Tuorto
- Division of Biochemistry, Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Mannheim, Germany.
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7
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Shirokikh NE. Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells. Crit Rev Biochem Mol Biol 2021; 57:261-304. [PMID: 34852690 DOI: 10.1080/10409238.2021.2006599] [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] [Indexed: 01/28/2023]
Abstract
During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.
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Affiliation(s)
- Nikolay E Shirokikh
- Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, The Australian National University, Canberra, Australia
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8
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Tjeldnes H, Labun K, Torres Cleuren Y, Chyżyńska K, Świrski M, Valen E. ORFik: a comprehensive R toolkit for the analysis of translation. BMC Bioinformatics 2021; 22:336. [PMID: 34147079 PMCID: PMC8214792 DOI: 10.1186/s12859-021-04254-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/09/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND With the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays. RESULTS Here, we introduce ORFik, a user-friendly R/Bioconductor API and toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5'UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames (uORFs). As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5' UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions. CONCLUSION In summary, ORFik introduces hundreds of tested, documented and optimized methods. ORFik is designed to be easily customizable, enabling users to create complete workflows from raw data to publication-ready figures for several types of sequencing data. Finally, by improving speed and scope of many core Bioconductor functions, ORFik offers enhancement benefiting the entire Bioconductor environment. AVAILABILITY http://bioconductor.org/packages/ORFik .
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Affiliation(s)
- Håkon Tjeldnes
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Kornel Labun
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Yamila Torres Cleuren
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.,Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway
| | - Katarzyna Chyżyńska
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Michał Świrski
- Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Eivind Valen
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway. .,Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway.
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9
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Shao D, Ahmed N, Soni N, O'Brien EP. RiboA: a web application to identify ribosome A-site locations in ribosome profiling data. BMC Bioinformatics 2021; 22:156. [PMID: 33765913 PMCID: PMC7992832 DOI: 10.1186/s12859-021-04068-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/10/2021] [Indexed: 12/12/2022] Open
Abstract
Background Translation is a fundamental process in gene expression. Ribosome profiling is a method that enables the study of transcriptome-wide translation. A fundamental, technical challenge in analyzing Ribo-Seq data is identifying the A-site location on ribosome-protected mRNA fragments. Identification of the A-site is essential as it is at this location on the ribosome where a codon is translated into an amino acid. Incorrect assignment of a read to the A-site can lead to lower signal-to-noise ratio and loss of correlations necessary to understand the molecular factors influencing translation. Therefore, an easy-to-use and accurate analysis tool is needed to accurately identify the A-site locations. Results We present RiboA, a web application that identifies the most accurate A-site location on a ribosome-protected mRNA fragment and generates the A-site read density profiles. It uses an Integer Programming method that reflects the biological fact that the A-site of actively translating ribosomes is generally located between the second codon and stop codon of a transcript, and utilizes a wide range of mRNA fragment sizes in and around the coding sequence (CDS). The web application is containerized with Docker, and it can be easily ported across platforms. Conclusions The Integer Programming method that RiboA utilizes is the most accurate in identifying the A-site on Ribo-Seq mRNA fragments compared to other methods. RiboA makes it easier for the community to use this method via a user-friendly and portable web application. In addition, RiboA supports reproducible analyses by tracking all the input datasets and parameters, and it provides enhanced visualization to facilitate scientific exploration. RiboA is available as a web service at https://a-site.vmhost.psu.edu/. The code is publicly available at https://github.com/obrien-lab/aip_web_docker under the MIT license.
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Affiliation(s)
- Danying Shao
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, USA
| | - Nabeel Ahmed
- Department of Chemistry, Pennsylvania State University, University Park, USA
| | - Nishant Soni
- Department of Chemistry, Pennsylvania State University, University Park, USA
| | - Edward P O'Brien
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, USA. .,Department of Chemistry, Pennsylvania State University, University Park, USA.
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10
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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: 41] [Impact Index Per Article: 10.3] [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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