RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing

Deciphering the Functional Long Non-Coding RNAs Derived from MicroRNA Loci

Albeit the majority of eukaryotic genomes can be pervasively transcribed to a diverse population of lncRNAs and various subtypes of lncRNA are discovered. However, the genome-wide study of miRNA-derived lncRNAs is still lacking. Here, it is reported that over 800 miRNA gene-originated lncRNAs (molncRNAs) are generated from miRNA loci. One of them, molnc-301b from miR-301b and miR-130b, functions as an "RNA decoy" to facilitate dissociation of the chromatin remodeling protein SMARCA5 from chromatin and thereby sequester transcription and mRNA translation. Specifically, molnc-301b attenuates erythropoiesis by mitigating the transcription of erythropoietic and translation-associated genes, such as GATA1 and FOS. In addition, a useful and powerful CRISPR screen platform to characterize the biological functions of molncRNAs at large-scale and single-cell levels is established and 29 functional molncRNAs in hematopoietic cells are identified. Collectively, the focus is on miRNA-derived lncRNAs, deciphering their landscape during normal hematopoiesis, and comprehensively evaluating their potential roles.

The translation initiation factor homolog eif4e1c regulates cardiomyocyte metabolism and proliferation during heart regeneration

The eIF4E family of translation initiation factors bind 5' methylated caps and act as the limiting step for mRNA translation. The canonical eIF4E1A is required for cell viability, yet other related eIF4E families exist and are utilized in specific contexts or tissues. Here, we describe a family called Eif4e1c, for which we find roles during heart development and regeneration in zebrafish. The Eif4e1c family is present in all aquatic vertebrates but is lost in all terrestrial species. A core group of amino acids shared over 500 million years of evolution forms an interface along the protein surface, suggesting that Eif4e1c functions in a novel pathway. Deletion of eif4e1c in zebrafish caused growth deficits and impaired survival in juveniles. Mutants surviving to adulthood had fewer cardiomyocytes and reduced proliferative responses to cardiac injury. Ribosome profiling of mutant hearts demonstrated changes in translation efficiency of mRNA for genes known to regulate cardiomyocyte proliferation. Although eif4e1c is broadly expressed, its disruption had most notable impact on the heart and at juvenile stages. Our findings reveal context-dependent requirements for translation initiation regulators during heart regeneration.

Tumor suppressor mediated ubiquitylation of hnRNPK is a barrier to oncogenic translation

Heterogeneous Nuclear Ribonucleoprotein K (hnRNPK) is a multifunctional RNA binding protein (RBP) localized in the nucleus and the cytoplasm. Abnormal cytoplasmic enrichment observed in solid tumors often correlates with poor clinical outcome. The mechanism of cytoplasmic redistribution and ensuing functional role of cytoplasmic hnRNPK remain unclear. Here we demonstrate that the SCFFbxo4 E3 ubiquitin ligase restricts the pro-oncogenic activity of hnRNPK via K63 linked polyubiquitylation, thus limiting its ability to bind target mRNA. We identify SCFFbxo4-hnRNPK responsive mRNAs whose products regulate cellular processes including proliferation, migration, and invasion. Loss of SCFFbxo4 leads to enhanced cell invasion, migration, and tumor metastasis. C-Myc was identified as one target of SCFFbxo4-hnRNPK. Fbxo4 loss triggers hnRNPK-dependent increase in c-Myc translation, thereby contributing to tumorigenesis. Increased c-Myc positions SCFFbxo4-hnRNPK dysregulated cancers for potential therapeutic interventions that target c-Myc-dependence. This work demonstrates an essential role for limiting cytoplasmic hnRNPK function in order to maintain translational and cellular homeostasis.

Tracing Translational Footprint by Ribo-Seq: Principle, Workflow, and Applications to Understand the Mechanism of Human Diseases

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.

Sensing of individual stalled 80S ribosomes by Fap1 for nonfunctional rRNA turnover

Cells can respond to stalled ribosomes by sensing ribosome collisions and employing quality control pathways. How ribosome stalling is resolved without collisions, however, has remained elusive. Here, focusing on noncolliding stalling exhibited by decoding-defective ribosomes, we identified Fap1 as a stalling sensor triggering 18S nonfunctional rRNA decay via polyubiquitination of uS3. Ribosome profiling revealed an enrichment of Fap1 at the translation initiation site but also an association with elongating individual ribosomes. Cryo-EM structures of Fap1-bound ribosomes elucidated Fap1 probing the mRNA simultaneously at both the entry and exit channels suggesting an mRNA stasis sensing activity, and Fap1 sterically hinders the formation of canonical collided di-ribosomes. Our findings indicate that individual stalled ribosomes are the potential signal for ribosome dysfunction, leading to accelerated turnover of the ribosome itself.

Innate, translation-dependent silencing of an invasive transposon in Arabidopsis

Co‐evolution between hosts’ and parasites’ genomes shapes diverse pathways of acquired immunity based on silencing small (s)RNAs. In plants, sRNAs cause heterochromatinization, sequence degeneration, and, ultimately, loss of autonomy of most transposable elements (TEs). Recognition of newly invasive plant TEs, by contrast, involves an innate antiviral‐like silencing response. To investigate this response’s activation, we studied the single‐copy element EVADÉ (EVD), one of few representatives of the large Ty1/Copia family able to proliferate in Arabidopsis when epigenetically reactivated. In Ty1/Copia elements, a short subgenomic mRNA (shGAG) provides the necessary excess of structural GAG protein over the catalytic components encoded by the full‐length genomic flGAG‐POL. We show here that the predominant cytosolic distribution of shGAG strongly favors its translation over mostly nuclear flGAG‐POL. During this process, an unusually intense ribosomal stalling event coincides with mRNA breakage yielding unconventional 5’OH RNA fragments that evade RNA quality control. The starting point of sRNA production by RNA‐DEPENDENT‐RNA‐POLYMERASE‐6 (RDR6), exclusively on shGAG, occurs precisely at this breakage point. This hitherto‐unrecognized “translation‐dependent silencing” (TdS) is independent of codon usage or GC content and is not observed on TE remnants populating the Arabidopsis genome, consistent with their poor association, if any, with polysomes. We propose that TdS forms a primal defense against EVD de novo invasions that underlies its associated sRNA pattern.

The effects of NCBP3 on METTL3-mediated m6A RNA methylation to enhance translation process in hypoxic cardiomyocytes

Hypoxia as a crucial pathogenesis factor usually results in huge harmful effects on cardiac injury and dysfunction. Our previous study has uncovered the global transcriptome and translatome profiles of cardiomyocytes in vitro and in vivo to response to hypoxia by RNA sequencing and ribosome profiling sequencing. We observe a series of differential expressed genes between transcription and translation, which may be attributed to the hypoxia‐specific binding affinity of nuclear cap‐binding subunit 3 (NCBP3) at 5' untranslation region of target genes. Although we observe that NCBP3 can facilitate translational process in myocardium under hypoxia stress, the underlying molecular mechanism of NCBP3 for gene translation modulation remains unclear. In this study, we performed NCBP3 immunoprecipitation for mass spectrum and found that METTL3 and eIF4A2 particularly interacted with NCBP3 in hypoxic rat H9C2 cardiomyocytes. Furthermore, we observed that METTL3‐mediated N6‐methyladenosine (m6A) methylation was elevated in hypoxia, but compromised by NCBP3 or METTL3 knockdown. Finally, we also demonstrated that NCBP3/METTL3/eIF4A2 regulatory axis plays a specific role in cardiomyocytes undergoing hypoxic stress. Taken together, we unmasked NCBP3, a novel hypoxia‐specific response protein functions as a scaffold to coordinate METTL3 and eIF4A2 for enhancing gene translation by m6A RNA methylation in cardiomyocytes upon hypoxic stress.

Genome-Scale Analysis of Acetobacterium woodii Identifies Translational Regulation of Acetogenesis

Acetogens synthesize acetyl-CoA via the CO₂-fixing Wood-Ljungdahl pathway. Despite their ecological and biotechnological importance, their translational regulation of carbon and energy metabolisms remains unclear. Here, we report how carbon and energy metabolisms in the model acetogen Acetobacterium woodii are translationally controlled under different growth conditions. Data integration of genome-scale transcriptomic and translatomic analyses revealed that the acetogenesis genes, including those of the Wood-Ljungdahl pathway and energy metabolism, showed changes in translational efficiency under autotrophic growth conditions. In particular, genes encoding the Wood-Ljungdahl pathway are translated at similar levels to achieve efficient acetogenesis activity under autotrophic growth conditions, whereas genes encoding the carbonyl branch present increased translation levels in comparison to those for the methyl branch under heterotrophic growth conditions. The translation efficiency of genes in the pathways is differentially regulated by 5′ untranslated regions and ribosome-binding sequences under different growth conditions. Our findings provide potential strategies to optimize the metabolism of syngas-fermenting acetogenic bacteria for better productivity.

IMPORTANCE Acetogens are capable of reducing CO₂ to multicarbon compounds (e.g., ethanol or 2,3-butanediol) via the Wood-Ljungdahl pathway. Given that protein synthesis in bacteria is highly energy consuming, acetogens living at the thermodynamic limit of life are inevitably under translation control. Here, we dissect the translational regulation of carbon and energy metabolisms in the model acetogen Acetobacterium woodii under heterotrophic and autotrophic growth conditions. The latter may be experienced when acetogen is used as a cell factory that synthesizes products from CO₂ during the gas fermentation process. We found that the methyl and carbonyl branches of the Wood-Ljungdahl pathway are activated at similar translation levels during autotrophic growth. Translation is mainly regulated by the 5′-untranslated-region structure and ribosome-binding-site sequence. This work reveals novel translational regulation for coping with autotrophic growth conditions and provides the systematic data set, including the transcriptome, translatome, and promoter/5′-untranslated-region bioparts.

Trips-Viz: an environment for the analysis of public and user-generated ribosome profiling data

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.

ORFik: a comprehensive R toolkit for the analysis of translation

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 .

smORFer: a modular algorithm to detect small ORFs in prokaryotes

Emerging evidence places small proteins (≤50 amino acids) more centrally in physiological processes. Yet, their functional identification and the systematic genome annotation of their cognate small open-reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use the 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. They have difficulties evaluating prokaryotic genomes due to the unique architecture (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present a new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting putative smORFs. The unique feature of smORFer is that it uses an integrated approach and considers structural features of the genetic sequence along with in-frame translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way, and dependent on the data available for a particular organism, different modules can be selected for smORF search.

A tool for analyzing and visualizing ribo-seq data at the isoform level

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.

RiboA: a web application to identify ribosome A-site locations in ribosome profiling data

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.

Translational landscape and protein biogenesis demands of the early secretory pathway in Komagataella phaffii

Background

Eukaryotes use distinct networks of biogenesis factors to synthesize, fold, monitor, traffic, and secrete proteins. During heterologous expression, saturation of any of these networks may bottleneck titer and yield. To understand the flux through various routes into the early secretory pathway, we quantified the global and membrane-associated translatomes of Komagataella phaffii.

Results

By coupling Ribo-seq with long-read mRNA sequencing, we generated a new annotation of protein-encoding genes. By using Ribo-seq with subcellular fractionation, we quantified demands on co- and posttranslational translocation pathways. During exponential growth in rich media, protein components of the cell-wall represent the greatest number of nascent chains entering the ER. Transcripts encoding the transmembrane protein PMA1 sequester more ribosomes at the ER membrane than any others. Comparison to Saccharomyces cerevisiae reveals conservation in the resources allocated by gene ontology, but variation in the diversity of gene products entering the secretory pathway.

Conclusion

A subset of host proteins, particularly cell-wall components, impose the greatest biosynthetic demands in the early secretory pathway. These proteins are potential targets in strain engineering aimed at alleviating bottlenecks during heterologous protein production.

Codon Resolution Analysis of Ribosome Profiling Data

Translation is a central biological process in living cells. Ribosome profiling approach enables assessing translation on a global, cell-wide level. Extracting versatile information from the ribosome profiling data usually requires specialized expertise for handling the sequencing data that is not available to the broad community of experimentalists. Here, we provide an easy-to-use and modifiable workflow that uses a small set of commands and enables full data analysis in a standardized way, including precise positioning of the ribosome-protected fragments, for determining codon-specific translation features. The workflow is complemented with simple step-by-step explanations and is accessible to scientists with no computational background.

RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data

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.

What determines eukaryotic translation elongation: recent molecular and quantitative analyses of protein synthesis

Protein synthesis from mRNA is an energy-intensive and tightly controlled cellular process. Translation elongation is a well-coordinated, multifactorial step in translation that undergoes dynamic regulation owing to cellular state and environmental determinants. Recent studies involving genome-wide approaches have uncovered some crucial aspects of translation elongation including the mRNA itself and the nascent polypeptide chain. Additionally, these studies have fuelled quantitative and mathematical modelling of translation elongation. In this review, we provide a comprehensive overview of the key determinants of translation elongation. We discuss consequences of ribosome stalling or collision, and how the cells regulate translation in case of such events. Next, we review theoretical approaches and widely used mathematical models that have become an essential ingredient to interpret complex molecular datasets and study translation dynamics quantitatively. Finally, we review recent advances in live-cell reporter and related analysis techniques, to monitor the translation dynamics of single cells and single-mRNA molecules in real time.

Improved computational analysis of ribosome dynamics from 5'P degradome data using fivepseq

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.

Translatome and Transcriptome Profiling of Hypoxic-Induced Rat Cardiomyocytes

Adult cardiac hypoxia as a crucial pathogenesis factor can induce detrimental effects on cardiac injury and dysfunction. The global transcriptome and translatome reflecting the cellular response to hypoxia have not yet been extensively studied in myocardium. In this study, we conducted RNA sequencing (RNA-seq) and ribosome profiling technique (polyribo-seq) in rat heart tissues and H9C2 cells exposed to different periods of hypoxia stress in vivo and in vitro. The temporal gene-expression profiling displayed the distinction of transcriptome and translatome, which were mainly concentrated in cell apoptosis, autophagy, DNA repair, angiogenesis, vascular process, and cardiac cell proliferation and differentiation. A large number of genes such as GNAI3, SEPT4, FANCL, BNIP3, TBX3, ESR2, PTGS2, KLF4, and ADRB2, whose transcript and translation levels are closely correlated, were identified to own a common RNA motif “5′-GAAGCUGCC-3′” in 5′ UTR. NCBP3 was further determined to recognize this RNA motif and facilitate translational process in myocardium under hypoxia stress. Taken together, our data show the close connection between alterations of transcriptome and translatome after hypoxia exposure, emphasizing the significance of translational regulation in related studies. The profiled molecular responses in current study may be valuable resources for advanced understanding of the mechanisms underlying hypoxia-induced effect on heart diseases.

RiboToolkit: an integrated platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution

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.

RiboMiner: a toolset for mining multi-dimensional features of the translatome with ribosome profiling data

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.

Recommendations for bacterial ribosome profiling experiments based on bioinformatic evaluation of published data

Ribosome profiling (RIBO-Seq) has improved our understanding of bacterial translation, including finding many unannotated genes. However, protocols for RIBO-Seq and corresponding data analysis are not yet standardized. Here, we analyzed 48 RIBO-Seq samples from nine studies of Escherichia coli K12 grown in lysogeny broth medium and particularly focused on the size-selection step. We show that for conventional expression analysis, a size range between 22 and 30 nucleotides is sufficient to obtain protein-coding fragments, which has the advantage of removing many unwanted rRNA and tRNA reads. More specific analyses may require longer reads and a corresponding improvement in rRNA/tRNA depletion. There is no consensus about the appropriate sequencing depth for RIBO-Seq experiments in prokaryotes, and studies vary significantly in total read number. Our analysis suggests that 20 million reads that are not mapping to rRNA/tRNA are required for global detection of translated annotated genes. We also highlight the influence of drug-induced ribosome stalling, which causes bias at translation start sites. The resulting accumulation of reads at the start site may be especially useful for detecting weakly expressed genes. As different methods suit different questions, it may not be possible to produce a "one-size-fits-all" ribosome profiling data set. Therefore, experiments should be carefully designed in light of the scientific questions of interest. We propose some basic characteristics that should be reported with any new RIBO-Seq data sets. Careful attention to the factors discussed should improve prokaryotic gene detection and the comparability of ribosome profiling data sets.

Computational methods for ribosome profiling data analysis

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.

RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution

SUMMARY

Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named 'ribo'. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling.

AVAILABILITY AND IMPLEMENTATION

For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RiboVIEW: a computational framework for visualization, quality control and statistical analysis of ribosome profiling data

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.

XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data

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.

Construction of High-Quality Rice Ribosome Footprint Library

High-throughput sequencing of ribosome footprints precisely maps and quantifies in vivo mRNA translation. The ribosome footprint sequencing has undergone continuing development since its original report. Here we provide a detailed protocol for construction of high-quality ribosome footprint library of rice. Rice total polysomes are isolated with a modified low ionic polysome extraction buffer. After nuclease digestion, rice ribosome footprints are extracted using SDS method followed by column purification. High-quality rice ribosome footprint library with peak reads of approximately 28-nucleotide (nt) length and strong 3-nt periodicity is constructed via key steps including rRNA depletion, end repair, 3' adapter ligation, reverse transcription, circularization, PCR enrichment and several rounds of purification. Biological significance of rice ribosome footprint library is further revealed by the comparison of transcriptomic and translatomic responses to salt stress and the utilization for novel open reading frame (ORF) identification. This improved protocol for rice ribosome footprint library construction will facilitate the global comprehension and quantitative measurement of dynamic translation in rice.

DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data

Background

Ribosome profiling brings insight to the process of translation. A basic step in profile construction at transcript level is to map Ribo-seq data to transcripts, and then assign a huge number of multiple-mapped reads to similar isoforms. Existing methods either discard the multiple mapped-reads, or allocate them randomly, or assign them proportionally according to transcript abundance estimated from RNA-seq data.

Results

Here we present DeepShape, an RNA-seq free computational method to estimate ribosome abundance of isoforms, and simultaneously compute their ribosome profiles using a deep learning model. Our simulation results demonstrate that DeepShape can provide more accurate estimations on both ribosome abundance and profiles when compared to state-of-the-art methods. We applied DeepShape to a set of Ribo-seq data from PC3 human prostate cancer cells with and without PP242 treatment. In the four cell invasion/metastasis genes that are translationally regulated by PP242 treatment, different isoforms show very different characteristics of translational efficiency and regulation patterns. Transcript level ribosome distributions were analyzed by “Codon Residence Index (CRI)” proposed in this study to investigate the relative speed that a ribosome moves on a codon compared to its synonymous codons. We observe consistent CRI patterns in PC3 cells. We found that the translation of several codons could be regulated by PP242 treatment.

Conclusion

In summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.

A novel eIF4E-interacting protein that forms non-canonical translation initiation complexes

Translation is a fundamental step in gene expression that regulates multiple developmental and stress responses. One key step of translation initiation is the association between eIF4E and eIF4G. This process is regulated in different eukaryotes by proteins that bind to eIF4E; however, evidence of eIF4E-interacting proteins able to regulate translation is missing in plants. Here, we report the discovery of CERES, a plant eIF4E-interacting protein. CERES contains an LRR domain and a canonical eIF4E-binding site. Although the CERES-eIF4E complex does not include eIF4G, CERES forms part of cap-binding complexes, interacts with eIF4A, PABP and eIF3, and co-sediments with translation initiation complexes in vivo. Moreover, CERES promotes translation in vitro and general translation in vivo, while it modulates the translation of specific mRNAs related to light and carbohydrate response. These data suggest that CERES is a non-canonical translation initiation factor that modulates translation in plants.

mQC: A post-mapping data exploration tool for ribosome profiling

BACKGROUND AND OBJECTIVE

Ribosome profiling is a recent next generation sequencing technique enabling the genome-wide study of gene expression in biomedical research at the translation level. Too often, researchers precipitously start trying to test their hypotheses after alignment of their data, without checking the quality and the general features of their mapped data. Despite the fact that these checks are essential to prevent errors and ensure valid conclusions afterwards, easy-to-use tools for visualizing the quality and overall outlook of mapped ribosome profiling data are lacking.

METHODS

We present mQC, a modular tool implemented as a Bioconda package and also available in the Galaxy tool shed. Herewith both bio-informaticians as well as non-experts can easily perform the indispensable visualization of both the quality and the general features of their mapped P-site corrected ribosome profiling reads. The user manual, the raw code and more information can be found on its GitHub repository (https://github.com/Biobix/mQC).

RESULTS

mQC was tested on multiple datasets to assess its general applicability and was compared to other tools that partly perform similar tasks.

CONCLUSIONS

Our results demonstrate that mQC can accomplish an unfilled but essential position in the ribosome profiling data analysis procedure by performing a thorough RIBO-Seq-specific exploration of aligned and P-site corrected ribosome profiling data.

Computational resources for ribosome profiling: from database to Web server and software

Ribosome profiling is emerging as a powerful technique that enables genome-wide investigation of in vivo translation at sub-codon resolution. The increasing application of ribosome profiling in recent years has achieved remarkable progress toward understanding the composition, regulation and mechanism of translation. This benefits from not only the awesome power of ribosome profiling but also an extensive range of computational resources available for ribosome profiling. At present, however, a comprehensive review on these resources is still lacking. Here, we survey the recent computational advances guided by ribosome profiling, with a focus on databases, Web servers and software tools for storing, visualizing and analyzing ribosome profiling data. This review is intended to provide experimental and computational biologists with a reference to make appropriate choices among existing resources for the question at hand.

RiboStreamR: a web application for quality control, analysis, and visualization of Ribo-seq data

BACKGROUND

Ribo-seq is a popular technique for studying translation and its regulation. A Ribo-seq experiment produces a snap-shot of the location and abundance of actively translating ribosomes within a cell's transcriptome. In practice, Ribo-seq data analysis can be sensitive to quality issues such as read length variation, low read periodicities, and contaminations with ribosomal and transfer RNA. Various software tools for data preprocessing, quality assessment, analysis, and visualization of Ribo-seq data have been developed. However, many of these tools require considerable practical knowledge of software applications, and often multiple different tools have to be used in combination with each other.

RESULTS

We present riboStreamR, a comprehensive Ribo-seq quality control (QC) platform in the form of an R Shiny web application. RiboStreamR provides visualization and analysis tools for various Ribo-seq QC metrics, including read length distribution, read periodicity, and translational efficiency. Our platform is focused on providing a user-friendly experience, and includes various options for graphical customization, report generation, and anomaly detection within Ribo-seq datasets.

CONCLUSIONS

RiboStreamR takes advantage of the vast resources provided by the R and Bioconductor environments, and utilizes the Shiny R package to ensure a high level of usability. Our goal is to develop a tool which facilitates in-depth quality assessment of Ribo-seq data by providing reference datasets and automatically highlighting quality issues and anomalies within datasets.

Identifying A- and P-site locations on ribosome-protected mRNA fragments using Integer Programming

Identifying the A- and P-site locations on ribosome-protected mRNA fragments from Ribo-Seq experiments is a fundamental step in the quantitative analysis of transcriptome-wide translation properties at the codon level. Many analyses of Ribo-Seq data have utilized heuristic approaches applied to a narrow range of fragment sizes to identify the A-site. In this study, we use Integer Programming to identify the A-site by maximizing an objective function that reflects the fact that the ribosome's A-site on ribosome-protected fragments must reside between the second and stop codons of an mRNA. This identifies the A-site location as a function of the fragment's size and its 5' end reading frame in Ribo-Seq data generated from S. cerevisiae and mouse embryonic stem cells. The correctness of the identified A-site locations is demonstrated by showing that this method, as compared to others, yields the largest ribosome density at established stalling sites. By providing greater accuracy and utilization of a wider range of fragment sizes, our approach increases the signal-to-noise ratio of underlying biological signals associated with translation elongation at the codon length scale.

Bacterial sepsis triggers an antiviral response that causes translation shutdown

In response to viral pathogens, the host upregulates antiviral genes that suppress translation of viral mRNAs. However, induction of such antiviral responses may not be exclusive to viruses, as the pathways lie at the intersection of broad inflammatory networks that can also be induced by bacterial pathogens. Using a model of Gram-negative sepsis, we show that propagation of kidney damage initiated by a bacterial origin ultimately involves antiviral responses that result in host translation shutdown. We determined that activation of the eukaryotic translation initiation factor 2-α kinase 2/eukaryotic translation initiation factor 2α (Eif2ak2/Eif2α) axis is the key mediator of translation initiation block in late-phase sepsis. Reversal of this axis mitigated kidney injury. Furthermore, temporal profiling of the kidney translatome revealed that multiple genes involved in formation of the initiation complex were translationally altered during bacterial sepsis. Collectively, our findings imply that translation shutdown is indifferent to the specific initiating pathogen and is an important determinant of tissue injury in sepsis.

PausePred and Rfeet: webtools for inferring ribosome pauses and visualizing footprint density from ribosome profiling data

The process of translation is characterized by irregularities in the local decoding rates of specific mRNA codons. This includes the occurrences of long pauses that can take place when ribosomes decode certain peptide sequences, encounter strong RNA secondary structures, or decode "hungry" codons. Examples are known where such pausing or stalling is used for regulating protein synthesis. This can be achieved at the level of translation via direct alteration of ribosome progression through mRNA or by altering mRNA stability via NoGo decay. Ribosome pausing has also been implicated in the cotranslational folding of proteins. Ribosome profiling data often are used for inferring the locations of ribosome pauses. However, no dedicated online software is available for this purpose. Here we present PausePred (https://pausepred.ucc.ie/), which can be used to infer ribosome pauses from ribosome profiling (Ribo-seq) data. Peaks of ribosome footprint density are scored based on their magnitude relative to the background density within the surrounding area. The scoring allows the comparison of peaks across the transcriptome or genome. In addition to the score, PausePred reports the coordinates of the pause, the footprint density at the pause site, and the surrounding nucleotide sequence. The pauses can be visualized in the context of Ribo-seq and RNA-seq density plots generated for specific transcripts or genomic regions with the Rfeet tool. PausePred does not require input on the location of protein coding ORFs (although gene annotations can be optionally supplied). As a result, it can be used universally and its output does not depend on ever evolving annotations.

riboWaltz: Optimization of ribosome P-site positioning in ribosome profiling data

Ribosome profiling is a powerful technique used to study translation at the genome-wide level, generating unique information concerning ribosome positions along RNAs. Optimal localization of ribosomes requires the proper identification of the ribosome P-site in each ribosome protected fragment, a crucial step to determine the trinucleotide periodicity of translating ribosomes, and draw correct conclusions concerning where ribosomes are located. To determine the P-site within ribosome footprints at nucleotide resolution, the precise estimation of its offset with respect to the protected fragment is necessary. Here we present riboWaltz, an R package for calculation of optimal P-site offsets, diagnostic analysis and visual inspection of ribosome profiling data. Compared to existing tools, riboWaltz shows improved accuracies for P-site estimation and neat ribosome positioning in multiple case studies. riboWaltz was implemented in R and is available as an R package at https://github.com/LabTranslationalArchitectomics/RiboWaltz.

Shoelaces: an interactive tool for ribosome profiling processing and visualization

Background

The emergence of ribosome profiling to map actively translating ribosomes has laid the foundation for a diverse range of studies on translational regulation. The data obtained with different variations of this assay is typically manually processed, which has created a need for tools that would streamline and standardize processing steps.

Results

We present Shoelaces, a toolkit for ribosome profiling experiments automating read selection and filtering to obtain genuine translating footprints. Based on periodicity, favoring enrichment over the coding regions, it determines the read lengths corresponding to bona fide ribosome protected fragments. The specific codon under translation (P-site) is determined by automatic offset calculations resulting in sub-codon resolution. Shoelaces provides both a user-friendly graphical interface for interactive visualisation in a genome browser-like fashion and a command line interface for integration into automated pipelines. We process 79 libraries and show that studies typically discard excessive amounts of quality data in their manual analysis pipelines.

Conclusions

Shoelaces streamlines ribosome profiling analysis offering automation of the processing, a range of interactive visualization features and export of the data into standard formats. Shoelaces stores all processing steps performed in an XML file that can be used by other groups to exactly reproduce the processing of a given study. We therefore anticipate that Shoelaces can aid researchers by automating what is typically performed manually and contribute to the overall reproducibility of studies. The tool is freely distributed as a Python package, with additional instructions, tutorial and demo datasets available at https://bitbucket.org/valenlab/shoelaces.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4912-6) contains supplementary material, which is available to authorized users.

Following Ribosome Footprints to Understand Translation at a Genome Wide Level

Protein translation is a key step in gene expression. The development of Ribosome Profiling has allowed the global analysis of this process at sub-codon resolution. In the last years the method has been applied to several models ranging from bacteria to mammalian cells yielding a surprising amount of insight on the mechanism and the regulation of translation. In this review we describe the key aspects of the experimental protocol and comment on the main conclusions raised in different models.

Understanding biopharmaceutical production at single nucleotide resolution using ribosome footprint profiling

Biopharmaceuticals such as monoclonal antibodies have revolutionised the treatment of a variety of diseases. The production of recombinant therapeutic proteins, however, remains expensive due to the manufacturing complexity of mammalian expression systems and the regulatory burden associated with administrating these medicines to patients in a safe and efficacious manner. In recent years, academic and industrial groups have begun to develop a greater understanding of the biology of host cell lines, such as Chinese hamster ovary (CHO) cells and utilise that information for process development and cell line engineering. In this review, we focus on ribosome footprint profiling (RiboSeq), an exciting next generation sequencing (NGS) method that provides genome-wide information on translation, and discuss how its application can transform our understanding of therapeutic protein production.

A Plant Biologist's Toolbox to Study Translation

Across a broad range of species and biological questions, more and more studies are incorporating translation data to better assess how gene regulation occurs at the level of protein synthesis. The inclusion of translation data improves upon, and has been shown to be more accurate than, transcriptional studies alone. However, there are many different techniques available to measure translation and it can be difficult, especially for young or aspiring scientists, to determine which methods are best applied in specific situations. We have assembled this review in order to enhance the understanding and promote the utilization of translational methods in plant biology. We cover a broad range of methods to measure changes in global translation (e.g., radiolabeling, polysome profiling, or puromycylation), translation of single genes (e.g., fluorescent reporter constructs, toeprinting, or ribosome density mapping), sequencing-based methods to uncover the entire translatome (e.g., Ribo-seq or translating ribosome affinity purification), and mass spectrometry-based methods to identify changes in the proteome (e.g., stable isotope labeling by amino acids in cell culture or bioorthogonal noncanonical amino acid tagging). The benefits and limitations of each method are discussed with a particular note of how applications from other model systems might be extended for use in plants. In order to make this burgeoning field more accessible to students and newer scientists, our review includes an extensive glossary to define key terms.

Genome-wide Quantification of Translation in Budding Yeast by Ribosome Profiling

Translation of mRNA into proteins is a complex process involving several layers of regulation. It is often assumed that changes in mRNA transcription reflect changes in protein synthesis, but many exceptions have been observed. Recently, a technique called ribosome profiling (or Ribo-Seq) has emerged as a powerful method that allows identification, with high accuracy, which regions of mRNA are translated into proteins and quantification of translation at the genome-wide level. Here, we present a generalized protocol for genome-wide quantification of translation using Ribo-Seq in budding yeast. In addition, combining Ribo-Seq data with mRNA abundance measurements allows us to simultaneously quantify translation efficiency of thousands of mRNA transcripts in the same sample and compare changes in these parameters in response to experimental manipulations or in different physiological states. We describe a detailed protocol for generation of ribosome footprints using nuclease digestion, isolation of intact ribosome-footprint complexes via sucrose gradient fractionation, and preparation of DNA libraries for deep sequencing along with appropriate quality controls necessary to ensure accurate analysis of in vivo translation.

Comparative ribosome profiling uncovers a dominant role for translational control in Toxoplasma gondii

Background

The lytic cycle of the protozoan parasite Toxoplasma gondii, which involves a brief sojourn in the extracellular space, is characterized by defined transcriptional profiles. For an obligate intracellular parasite that is shielded from the cytosolic host immune factors by a parasitophorous vacuole, the brief entry into the extracellular space is likely to exert enormous stress. Due to its role in cellular stress response, we hypothesize that translational control plays an important role in regulating gene expression in Toxoplasma during the lytic cycle. Unlike transcriptional profiles, insights into genome-wide translational profiles of Toxoplasma gondii are lacking.

Methods

We have performed genome-wide ribosome profiling, coupled with high throughput RNA sequencing, in intracellular and extracellular Toxoplasma gondii parasites to investigate translational control during the lytic cycle.

Results

Although differences in transcript abundance were mostly mirrored at the translational level, we observed significant differences in the abundance of ribosome footprints between the two parasite stages. Furthermore, our data suggest that mRNA translation in the parasite is potentially regulated by mRNA secondary structure and upstream open reading frames.

Conclusion

We show that most of the Toxoplasma genes that are dysregulated during the lytic cycle are translationally regulated.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4362-6) contains supplementary material, which is available to authorized users.

Beyond Read-Counts: Ribo-seq Data Analysis to Understand the Functions of the Transcriptome

By mapping the positions of millions of translating ribosomes in the cell, ribosome profiling (Ribo-seq) has established its role as a powerful tool to study gene expression. Several laboratories have introduced modifications to the experimental protocol and expanded the repertoire of biochemical methods to study translation transcriptome-wide. However, the diversity of protocols highlights a need for standardization. At the same time, different computational analysis strategies have used Ribo-seq data to identify the set of translated sequences with high confidence. In this review we present an overview of such methodologies, outlining their assumptions, data requirements, and availability. At the interface between RNA and proteins, Ribo-seq can complement data from multiple omics approaches, zooming in on the central role of translation in the molecular cell.

Transcriptome-wide measurement of translation by ribosome profiling

Translation is one of the fundamental processes of life. It comprises the assembly of polypeptides whose amino acid sequence corresponds to the codon sequence of an mRNA's ORF. Translation is performed by the ribosome; therefore, in order to understand translation and its regulation we must be able to determine the numbers and locations of ribosomes on mRNAs in vivo. Furthermore, we must be able to examine their redistribution in different physiological contexts and in response to experimental manipulations. The ribosome profiling method provides us with an opportunity to learn these locations, by sequencing a cDNA library derived from the short fragments of mRNA covered by the ribosome. Since its original description, the ribosome profiling method has undergone continuing development; in this article we describe the method's current state. Important improvements include: the incorporation of sample barcodes to enable library multiplexing, the incorporation of unique molecular identifiers to enable to removal of duplicated sequences, and the replacement of a gel-purification step with the enzymatic degradation of unligated linker.

Bayesian prediction of RNA translation from ribosome profiling

Ribosome profiling via high-throughput sequencing (ribo-seq) is a promising new technique for characterizing the occupancy of ribosomes on messenger RNA (mRNA) at base-pair resolution. The ribosome is responsible for translating mRNA into proteins, so information about its occupancy offers a detailed view of ribosome density and position which could be used to discover new translated open reading frames (ORFs), among other things. In this work, we propose Rp-Bp, an unsupervised Bayesian approach to predict translated ORFs from ribosome profiles. We use state-of-the-art Markov chain Monte Carlo techniques to estimate posterior distributions of the likelihood of translation of each ORF. Hence, an important feature of Rp-Bp is its ability to incorporate and propagate uncertainty in the prediction process. A second novel contribution is automatic Bayesian selection of read lengths and ribosome P-site offsets (BPPS). We empirically demonstrate that our read length selection technique modestly improves sensitivity by identifying more canonical and non-canonical ORFs. Proteomics- and quantitative translation initiation sequencing-based validation verifies the high quality of all of the predictions. Experimental comparison shows that Rp-Bp results in more peptide identifications and proteomics-validated ORF predictions compared to another recent tool for translation prediction.

Insights into the mechanisms of eukaryotic translation gained with ribosome profiling

The development of Ribosome Profiling (RiboSeq) has revolutionized functional genomics. RiboSeq is based on capturing and sequencing of the mRNA fragments enclosed within the translating ribosome and it thereby provides a ‘snapshot’ of ribosome positions at the transcriptome wide level. Although the method is predominantly used for analysis of differential gene expression and discovery of novel translated ORFs, the RiboSeq data can also be a rich source of information about molecular mechanisms of polypeptide synthesis and translational control. This review will focus on how recent findings made with RiboSeq have revealed important details of the molecular mechanisms of translation in eukaryotes. These include mRNA translation sensitivity to drugs affecting translation initiation and elongation, the roles of upstream ORFs in response to stress, the dynamics of elongation and termination as well as details of intrinsic ribosome behavior on the mRNA after translation termination. As the RiboSeq method is still at a relatively early stage we will also discuss the implications of RiboSeq artifacts on data interpretation.