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Computational and Structural Biotechnology Journal logoLink to Computational and Structural Biotechnology Journal
. 2024 Apr 22;23:1912–1918. doi: 10.1016/j.csbj.2024.04.051

A review of Ribosome profiling and tools used in Ribo-seq data analysis

Mingso Sherma Limbu 1,1, Tianze Xiong 1,1, Sufang Wang 1,⁎
PMCID: PMC11076270  PMID: 38721586

Abstract

Translational regulation plays the most critical role in gene expression. Ribosome profiling sequencing (Ribo-Seq) is one of the methods to study translation and its regulation. It is a high throughput technology based on deep sequencing, which targets ribosome protected mRNA fragments to produce a ‘global snapshot’ of translatome. There has been an annual increase in the number of publications incorporating Ribo-seq technology. Because of its importance, we used PubMed database to conduct a comprehensive bibliometric analysis on Ribo-seq. We identified 2744 published articles that utilized the term ‘Ribo-seq’ between 2009 and Jan 2024, and 684 articles that contained both Ribo-seq and RNA-seq terms. Based on keywords correlation analysis, we discovered that the primary focus of Ribo-seq articles lies in the areas of translation, transcriptome, and ribosome in the past few years and other topics such as single-cell ribo-seq and crispr within two years, reflecting current areas of interests in Ribo-seq research. The Ribo-seq data analysis applications were also explored and summarized, providing a guide for researchers to choose corresponding tools for different types of analysis. Overall, we highlighted the advances made by Ribo-seq technologies, and the possibilities of utilizing machine learning models to unravel information from multi-omics data. The integration of Ribo-seq with other omics data, such as RNA-seq, is essential to understand the gene expression in complex biological systems.

Keywords: Ribosome profiling, Ribo-seq, RNA-seq, Gene expression, Multi-omics analysis

1. Introduction

Gene expression is the process that contains transcription and translation. Translation involves the conversion of mRNA into protein, where a ribosome RNA moves along the mRNA and translates to corresponding amino acid sequences [1], [2]. Ribosomes may stall on certain regions of mRNA, translating that region slower than others, which is an important regulatory mechanism [3]. Regulation of eukaryotic gene expression is a multi-step process, and it can occur at many levels; epigenetic, transcriptional, post-transcriptional, translational, and post translational [4], [5]. Among these, translational control plays the most critical role as translation is a biosynthetic process which costs the largest investment of energy in cells [6]. Translational control modulates gene expression in several biological processes, as it allows cells to respond in a rapid and flexible manner to external signals. The failure within this mechanism is linked to a number of diseases [7].

One of the methods to study translational process is Ribosome profiling sequencing (Ribo-Seq) [8]. This is a high throughput technology based on deep sequencing, which provides a way to monitor translation [9]. With the development of ribosome profiling, we can examine translational regulation in vivo which was previously unprecedented [10]. The sequence fragments of Ribo-Seq are called ribosome footprints (RFs), which are typically ∼30 nucleotides long fragments that are protected by ribosomes against nuclease digestion [11]. Sequencing these RFs reveal the overall translational events including the precise position of each ribosome, the transcript that was being translated, and the proteins that were being made (Fig. 1). The ribosome-protected fragments (RPF, approximately 22–35 nucleotides) obtained from Ribo-seq experiments provide direct evidence of actively translated regions. As translating ribosomes are arrested in Ribo-seq experiments, each RPF reveals the position of one ribosome and what transcript the ribosome is translating. On a larger scale, when summed up, the overall density of RPFs indicates the translation rate, which quantitatively measures how rapidly the cell is producing proteins [2].

Fig. 1.

Fig. 1

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The principle of Ribosome profiling.

The steps involved in Ribo-seq workflow are (1) Cell lysis, (2) Footprinting and Ribosome recovery, (3) Footprint purification, and (4) Library preparation and High-throughput sequencing [12]. Ribo-seq begins with the isolation of the cells of interest. The ribosomes and their associated mRNA complexes need to be separated in order to study their exact positions, and not just the general vicinity. Therefore, these cells are lysed either by utilizing a translational inhibitor or by flash freezing, effectively halting elongation. They are then treated with RNases that act as scissors to cut up the unprotected mRNA segments. Then RPFs are obtained and converted into Illumina compatible cDNA libraries for sequencing. The last step is data analysis and interpretation of results.

With Ribo-seq being our primary focus, we conducted keyword extraction and analyzed the primary topics of Ribo-seq publications. We then followed with investigations into Ribo-seq and RNA-seq keywords and topics correlation analysis. Afterwards, we outlined tools and programs developed for Ribo-seq data analysis, specifically discussed the function and advantages for each tool and listed ones that were good for beginners, aiming to provide guide for researchers choosing corresponding tools.

2. Development and topics of Ribo-seq

The first article on the terms ‘Ribo-seq’ or ‘Ribosome Profiling’, which are used interchangeably with one another, was published in the year of 2009. We retrieved publications from PubMed using keyword “Ribo-seq” from 2009 to Jan 2024, which results in a total number of 2744 articles. In 2009, just 44 papers were published. Later, this number increased to 153 in 2014 and reached 386 in the year 2021 (Fig. 2A). The results showed an overall exponential growth, hinting at the widespread application of this cutting-edge technology, and the gravity of its significance in the field of genomic research. However, that was until the year of 2022 when the number suddenly dropped to 320 papers compared to the 386 papers published in the previous year of 2021. This declining pattern continued in 2023 with only 305 papers published (Fig. 2A). We hypothesized that the reason behind the decline might be due to COVID-19 pandemic. The World Health Organization (WHO) declared COVID-19 as a pandemic on March 11, 2020. This high-risk disease suspended various key activities, including many on-going scientific research [13], [14]. To test this hypothesis, we chose “CHIP-seq” and “ATAC-seq” to confirm the results because the appearance of these two sequencing technologies were similar as Ribo-seq. We conducted analysis of ‘CHIP-seq’ and noticed a similar trend, where the number of papers dropped from 1318 in 2021 to 1224, in 2022. We again met with the same pattern using ‘ATAC-seq’, which the number was 1045 papers in 2021 and 997 in 2022. Hence, the results provided complete support for our hypothesis that the declining pattern could be attributed to the COVID-19 pandemic.

Fig. 2.

Fig. 2

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The number of publications retrieved from PubMed database. A. The number of papers published per year with the keyword “Ribo-seq”. B. The number of papers published per year with the keyword “RNA-seq” and “Ribo-seq”.

Since gene expression involves transcription and translation, we are interested in the combination of RNA-seq and Ribo-seq because the combination of these two methods may provide comprehensive information of gene expression [15]. Owing to the significance of the insight it offers, there has been a notable increase in the number of published papers integrating these methods. To be specific, the number of articles which integrated Ribo-seq with RNA-seq was 684 from 2012 to 2023. As seen before in the ‘Ribo-seq’ search, the results from the query “RNA-seq” and “Ribo-seq” were increasing annually until 2021 (Fig. 2B). These terms then yielded a result of just 82 papers in 2022 and 78 papers in 2023, indicating a falling trend. However, the figures indicated an overall growth in multi-omics publications (Fig. 2B).

In order to gain a more generalized view of the major topics explored in Ribo-seq research, we performed a keyword correlation analysis. Firstly, the co-occurrence of the term “Ribo-seq” was analyzed with keywords extracted from the abstracts of the Ribo-seq articles (Fig. 3A). Keywords that occurred more than 4 times were chosen which leads to 340 keywords kept, and their total strength of co-occurrence links with other keywords were calculated. Ribosome profiling, ribosome, Ribo-seq and transcriptome were the most frequent words, represented by the largest circle; implying their roles as the focus of Ribo-seq research. Whereas the words in smaller circle are more general topics within it. Most of the keywords were generally showing the color from yellow to orange, indicating that although the first paper was released way back to 2009, Ribo-seq research mainly accelerated after 2015. Keywords such as metabolome [16], molecular biology [17] and crispr [18] that are colored orange are more recent. These topics reflect current areas of interest in Ribo-seq research field.

Fig. 3.

Fig. 3

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Visualization of A. Visualization of “Ribo-seq” keywords correlation. B. Visualization of “Ribo-seq” and “RNA-seq” keywords correlation. Closely related topics were organized into clusters distinguished by the same color. The size of the circle corresponds to the quantity of the published articles. The lines between the circles connect two words, signifying their origin from one article.

As Ribo-seq technology progresses, it can solve many challenges posed by translatomic research. For instance, Single cell ribosome sequencing (scRibo-seq) is a new tool that can evaluate translation in single cells at a single codon resolution [19]. This cutting-edge technology was recently utilized to investigate the cell cycle-dependent translational pausing. Examination of individual cells revealed the differences in translational activities, which were difficult to detect otherwise in bulk measurements [20]. Similarly, a ribosome-bound mRNA mapping technology called RIBOmap, was recently developed to investigate the translatome at both spatial and single-cell resolution [21]. In the foreseeable future, a shift towards technologies with enhanced spatial resolution is anticipated.

In a similar manner, we analyzed the co-occurrence of the keywords from “Ribo-seq” and “RNA-seq” (Fig. 3B). This time, keywords that appeared more than twice were chosen, resulting in a number of 302 keywords. The co-occurrence was highest for terms RNA-seq, translation, transcriptome, ribosome and translational regulation. The similarity between these results expresses the shared role of Ribo-seq and RNA-seq in translational research. The blue circles contained keywords that served as focal points of Ribo-seq research in the past, such as small open reading frames, mouse and human. Items that appear more orange indicate their recent emergence, for example, crispr [22], differential gene expression [23], non-coding RNA [24] and apoptosis [25], and therefore, showcase the current trends in Ribo-seq and RNA-seq research.

From keywords analysis, it clearly showed that multi-omics analysis plays a crucial role in advancing our understanding of complex biological systems, we cannot neglect the potential of Ribo-seq to uncover translational events when integrated with other omics data. In the living system, genetic information is expressed from DNA to the mRNA (transcription) to protein (translation), which is explained by the central dogma. Due to the internal correlation between transcription and translation, the combination of transcriptomics and translatomics is necessary. On one hand, at the transcription level, RNA-seq allows for studying the entire transcriptome of an organism [26]. On the other hand, Ribo-seq provides us insights into translation events. When RNA-seq data and Ribo-seq data are combined, new results such as translation efficiency can be produced. Furthermore, with the increasing accessibility of computer power and omics database, hidden information of integrated Ribo-seq and other omics data can be unraveled by machine learning methods. Based on machine learning models, multi-omics data are incorporated into a network [27]. More features underlying the network will be inferred than Ribo-seq data itself.

3. Tools and programs used in Ribo-seq data analysis

A variety of algorithms, tools and online resources have been developed to meet the ever-increasing demand for Ribo-seq data analysis. Essentially, the tools include the following analysis steps: 1) pre-processing and quality control steps, which are essential to remove adapter sequences and filter out non-coding RNA fragments; 2) read mapping, which maps RPF to coding sequence regions (CDS); 3) differential expression analysis, which compares gene expression levels between different experimental groups using statistical methods; 4) translated open reading frame (ORF) detection, which identifies regions within a genome that are being translated into proteins (a key application of Ribo-seq data analysis, particularly for short ORF detection [28]); 5) calculation of translation efficiency, which normalizes ribosome counts to transcript abundance (the integration of RNA-seq data and Ribo-seq data is needed [29]); 6) other analysis steps, such as metagene analysis, A-site detection, P-site detection and codon occupancy analysis, also provide intriguing results.

We summarized tools and programs that developed from 2019 to 2023 for Ribo-seq data analysis (Table 1). Most of them are based on R, Python, Perl and Java. According to Ribo-seq data analysis steps mentioned above, some tools showed their unique advantages. Tools such as RiboToolkit [30] and RiboChat [31] provide the comprehensive analysis steps mentioned above and offer a smooth user experience. For ORF detection, HRIBO specifically processes bacterial circular chromosomes by using special ORF prediction tools, performing differential transcription and translation analysis, and providing comprehensive expression information for detected ORF [32]. Ribotracer provides a novel method for assessing the three-nucleotide periodicity of RPF profiles and accurately detects actively translating ORFs at both the exon and transcript level [30]. Using a noise-tolerant method that integrates features describing ribosome behavior and CDS characteristics, RiboNT is able to tolerate noise arising from RPF size, offset and periodicity [33]. Moreover, smORFer provides a modular algorithm that integrates genomic information, structural features, Ribo-Seq and TIS-Ribo-Seq to identify small open reading frames (smORF) in prokaryotes [34].

Table 1.

Tools developed for Ribo-seq data analysis.

Name Year Programming Language Input Data Type Analysis steps Advantages Organism type
HRIBO [[32] 2021 Python, R Fastq file, yaml file, tsv file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Quality control

  • 4)

    ORF detection

  • 5)

    Translation efficiency calculation

  • 6)

    Differential expression analysis

  • 7)

    Metagene analysis
  • •

    Used for bacterial Ribo-seq data analysis
  • •

    Prokaryotes
ORFik[37] 2021 R, C++ Fastq file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Quality control

  • 4)

    ORF detection

  • 5)

    Differential translation efficiency analysis

  • 6)

    Ribosome stalling analysis
  • •

    Faster BAM file calculation

  • •

    Can be used for Ribo-seq data, translation complex profiling (TCP-seq) data, ribosome complex profiling (RCP-seq) data and cap analysis of gene expression (CAGE) data analysis
  • •

    Prokaryotes and eukaryotes
riboviz 2[38] 2022 R, Python, Java Fastq file, FASTA file, GFF3 file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Quality control

  • 4)

    Metagene analysis
  • •

    Aligned read count matrices in the ribogrid H5 file
  • •

    Prokaryotes and eukaryotes
RiboA[35] 2021 Python, PostgreSQL, HTML, Java, CSS BAM/SAM file, FASTA file, GFF/CDS file
  • 1)

    A-stie offset calculation

  • 2)

    A-site read density profiling
  • •

    The most accurate A-site offset values compared to other tools
  • •

    Prokaryotes and eukaryotes
RiboChat[31] 2022 HTML, CSS, Java Fastq file, SRA file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Quality control

  • 4)

    ORF detection

  • 5)

    Differential translation analysis

  • 6)

    Functional enrichment analysis
  • •

    A chat-style web interface

  • •

    On the Alibaba Cloud Elastic Compute Service
  • •

    Prokaryotes and eukaryotes
RiboDiPA[36] 2020 R BAM file, GTF file
  • 1)

    Exon concatenation

  • 2)

    P-site mapping

  • 3)

    Data binning

  • 4)

    Differential pattern analysis
  • •

    Pattern differences in ribosome footprints
  • •

    Prokaryotes and eukaryotes
RiboDoc[39] 2021 Python, R Fastq file, config file, FASTA file, GFF file, length file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Metagene analysis

  • 4)

    P-site offset calculation

  • 5)

    Differential expression analysis
  • •

    In a Docker container
  • •

    Prokaryotes and eukaryotes
RiboGalaxy[40], [41] 2023 Python, xml Fastq file
  • 1)

    Adapter trimming

  • 2)

    Read mapping
  • •

    Translation Initiation sequencing (TI-seq) and TCP-seq

  • •

    Read data and annotation file formats for upload to Trips-Viz
  • •

    Prokaryotes and eukaryotes
RiboNT[33] 2021 R, Python FASTA format, GTF file, BAM file
  • 1)

    Transcripts assembling and candidate ORFs extraction

  • 2)

    Quality control

  • 3)

    Offset extraction

  • 4)

    Weight balancing

  • 5)

    ORF detection

  • 6)

    ORF classification
  • •

    Analyzing RPFs with poor periodicity

  • •

    Accurately predicting ORFs and translation initiation sites (TISs).

  • •

    Identifying translated small ORFs (sORFs) accurately.
  • •

    Prokaryotes and eukaryotes
RiboPlotR[42] 2021 R GTF/GFF3 file, BAM file
  • 1)

    Annotation file loading

  • 2)

    Plot presentation
  • •

    Identification of translation events in unannotated coding regions

  • •

    Visual determination of which transcript isoform(s) is/are translated.
  • •

    Eukaryotes
RiboToolkit[30] 2020 Perl, R, Java Fastq/FASTA file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Codon usage analysis

  • 4)

    Metagene analysis

  • 5)

    Translation efficiency calculation

  • 6)

    Differential translation analysis

  • 7)

    Functional annotation analysis
  • •

    The first integrated one-stop web server for Ribo-seq data analysis
  • •

    Prokaryotes and eukaryotes
RiboVIEW[43] 2020 R, Python BAM file
  • 1)

    Periodicity calculation

  • 2)

    Metagene analysis

  • 3)

    Condon enrichment analysis

  • 4)

    Condon occupancy analysis
  • •

    Visualization of translation elongation at the codon level.

  • •

    Unbiased estimates of codon enrichment
  • •

    Prokaryotes and eukaryotes
RPiso[44] 2021 Perl, Python Fastq file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    mRNA isoform visualization
  • •

    More reliable and precise results of isoform abundance.
  • •

    Eukaryotes
RP-REP[45] 2021 Perl, R, Python Gene Matrix Transposed (GMT) files, XLSX file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Principal component analysis

  • 4)

    Differential expression analysis

  • 5)

    Functional enrichment analysis
  • •

    Vertical scaling of processing up to96 cores

  • •

    The Snakemake workflow management system
  • •

    Prokaryotes and eukaryotes
Ribotracer[23] 2019 Jupyter Notebook, Python BAM file, FASTA file, GTF file
  • 1)

    Candidate ORF preparation

  • 2)

    Mapped reads partitioning

  • 3)

    Metagene analysis

  • 4)

    RPF profile generation

  • 5)

    Translation status prediction
  • •

    A novel method for assessing the three-nucleotide periodicity of RPF profiles

  • •

    Accurate detection of translating ORFs at both the exon and transcript levels.
  • •

    Eukaryotes
smORFer[34] 2021 Perl, R FASTA file, Fastq file, BED/GFF/GTF file
  • 1)

    Genome-based ORF detection

  • 2)

    ORF detection based on Ribo-seq data

  • 3)

    Translation initiation site detection
  • •

    High accuracy in detecting small ORFs (smORFs) in prokaryotic organisms.

  • •

    Increasing accuracy of smORF prediction
  • •

    Prokaryotes
XPRESSyourself[46] 2020 Python, R, Julia FASTA file, Fastq file, GTF file
  • 1)

    Adapter trimming

  • 2)

    Read mapping

  • 3)

    Quality control

  • 4)

    metagene analysis

  • 5)

    Differential expression analysis
  • •

    A complete suite of tools necessary for comprehensive ribosome profiling and bulk RNA-Seq data processing and analysis.
  • •

    Prokaryotes and eukaryotes
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In addition to the above analysis steps, new results can be generated using dedicated tools. RiboA allows accurate generation of A-site offsets [35] and RiboDiPA provides unique insights into translation dynamics under different conditions [36]. Because Ribo-Seq data analysis is more complicated than traditional RNA-Seq data analysis, some tools mentioned above are not suitable for beginners. For example, RiboDiPA requires R programming experience, and most of its functionality is achieved by using R language function programming. smORFer, on the other hand, requires knowledge of both command line programming and R programming. Therefore, for beginners, we recommend website servers such as RiboToolkit and RiboChat, which don't require much programming experience. These programs not only provide a complete analysis process, but also do not require extensive programming skills.

4. Conclusion

Ribo-seq has changed the landscape of the research on translational regulation by allowing direct in vivo study. Since its discovery, numerous sites of ribosome translation beyond the presently annotated CDS have been revealed [47]. Furthermore, long noncoding RNA’s role in translational regulation has also been elucidated [48]. Despite its advances in translatome study, there are also many challenges that this method may come with. The initiation from multiple sites within a single transcript makes defining every open reading frame challenging. Furthermore, Ribo-seq does not report on the efficiency of translation directly or distinguish between stalled and active ribosomes in elongation. These shortcomings, however, can be overcome by a well-defined protocol [49].

Gene expression, as we have earlier mentioned, can be analyzed at many levels. Therefore, multi-omics data analysis is needed, which allows for a broader view of complex physiological processes and disease mechanisms. It is a combination of various “omics” layers; namely genomics, epigenomics, transcriptomics, translatomics, proteomics, etc [50]. Multi-omics research can reveal the interaction between various biological layers and explain the underlying pathogenic changes of the disease. Integrating RNA-seq with ATAC-seq and ChIP-seq is one of the examples for analyzing the interactions between various biological layers in depth [51]. ChIP-seq and ATAC-seq are other two commonly used high –throughput sequencing techniques. ATAC-seq, short for Assay for Transposase-Accessible Chromatin using sequencing, is a method of detecting open chromatins associated with active transcription [52]. Similarly, Chromatin immunoprecipitation or ChIP-seq, is a powerful tool to assess protein interactions with DNA. In ChIP-seq, a specific DNA-binding protein or a histone modification is immunoprecipitated to identify genome associated with that protein [53]. The integration of multi-omics, such as ATAC-seq, ChIP-seq, is a valuable approach to not only discover biomarkers and understand diseases, but also achieve precision medicine in medical research [54], [55], [56], [57]. For example, in cancer research, multi-omics analysis gave rise to the pan-cancer molecular classification that sets subtypes based on frequently mutated genes, regardless of their tissue of origin [58], [59]. Using this approach, we may witness the value of multi-omics analysis into medical research; ranging from cancer to aging, to neurodegenerative diseases, and more [60].

Funding

This work is supported by National Natural Science Foundation of China (31800781) and Innovation Program for the Undergraduate International Student of the School of Life Sciences, Northwestern Polytechnical University (SMSC017).

CRediT authorship contribution statement

Tianze Xiong: Formal analysis, Writing – original draft, Writing – review & editing. Mingso Sherma Limbu: Formal analysis, Writing – original draft. Sufang Wang: Conceptualization, Funding acquisition, Investigation, Supervision, Writing – review & editing.

Declaration of Competing Interest

The authors declare no competing interests.

References

  • 1.Beyer D., Skripkin E., Wadzack J., Nierhaus K.H. How the ribosome moves along the mRNA during protein synthesis. J Biol Chem. 1994;269:30713–30717. [PubMed] [Google Scholar]
  • 2.Ingolia N.T., Hussmann J.A., Weissman J.S. Ribosome profiling: Global views of translation. Cold Spring Harb Perspect Biol. 2019;11 doi: 10.1101/cshperspect.a032698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Chyżyńska K., Labun K., Jones C., Grellscheid S.N., Valen E. Deep conservation of ribosome stall sites across RNA processing genes. NAR Genom Bioinform. 2021;3 doi: 10.1093/nargab/lqab038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Gibney E.R., Nolan C.M. Epigenetics and gene expression. Heredity. 2010;105:4–13. doi: 10.1038/hdy.2010.54. [DOI] [PubMed] [Google Scholar]
  • 5.Istomine R., Pavey N., Piccirillo C.A. Posttranscriptional and translational control of gene regulation in CD4+ T cell subsets. J Immunol. 2016;196:533–540. doi: 10.4049/jimmunol.1501337. [DOI] [PubMed] [Google Scholar]
  • 6.Verduyn C., Stouthamer A.H., Scheffers W.A., van Dijken J.P. A theoretical evaluation of growth yields of yeasts. Antonie Van Leeuwenhoek. 1991;59:49–63. doi: 10.1007/BF00582119. [DOI] [PubMed] [Google Scholar]
  • 7.Hronová V., Valášek L.S. Translational control: an emergency brake for protein synthesis. Elife. 2017;6 doi: 10.7554/eLife.27085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Diament A., Tuller T. Estimation of ribosome profiling performance and reproducibility at various levels of resolution. Biol Direct. 2016;11 doi: 10.1186/s13062-016-0127-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Eastman G., Smircich P., Sotelo-Silveira J.R. Following ribosome footprints to understand translation at a genome wide Level. Comput Struct Biotechnol J. 2018;16:167–176. doi: 10.1016/j.csbj.2018.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.McGlincy N.J., Ingolia N.T. Transcriptome-wide measurement of translation by ribosome profiling. Methods. 2017;126:112–129. doi: 10.1016/j.ymeth.2017.05.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Ingolia N.T., Ghaemmaghami S., Newman J.R.S., Weissman J.S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science. 2009;324:218–223. doi: 10.1126/science.1168978. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Ingolia N.T., Brar G.A., Rouskin S., McGeachy A.M., Weissman J.S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat Protoc. 2012;7:1534–1550. doi: 10.1038/nprot.2012.086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Miyah Y., Benjelloun M., Lairini S., Lahrichi A. COVID-19 impact on public health, environment, human psychology, global socioeconomy, and education. ScientificWorldJournal. 2022;2022 doi: 10.1155/2022/5578284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Riccaboni M., Verginer L. The impact of the COVID-19 pandemic on scientific research in the life sciences. PLoS One. 2022;17 doi: 10.1371/journal.pone.0263001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Walker D.C., Lozier Z.R., Bi R., Kanodia P., Miller W.A., Liu P. Variational inference for detecting differential translation in ribosome profiling studies. Front Genet. 2023;14 doi: 10.3389/fgene.2023.1178508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Na D. User guides for biologists to learn computational methods. J Microbiol. 2020;58:173–175. doi: 10.1007/S12275-020-9723-1. [DOI] [PubMed] [Google Scholar]
  • 17.Paulet D., David A., Rivals E. Ribo-seq enlightens codon usage bias. DNA Res. 2017;24:303–310. doi: 10.1093/DNARES/DSW062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Hofman D.A., Ruiz-Orera J., Yannuzzi I., Murugesan R., Brown A., Clauser K.R., et al. Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma. BioRxiv. 2023 doi: 10.1101/2023.05.04.539399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Su D., Ding C., Qiu J., Yang G., Wang R., Liu Y., et al. Ribosome profiling: a powerful tool in oncological research. Biomark Res. 2024;12 doi: 10.1186/s40364-024-00562-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.VanInsberghe M., van den Berg J., Andersson-Rolf A., Clevers H., van Oudenaarden A. Single-cell Ribo-seq reveals cell cycle-dependent translational pausing. Nature. 2021;597:561–565. doi: 10.1038/s41586-021-03887-4. [DOI] [PubMed] [Google Scholar]
  • 21.Zeng H., Huang J., Ren J., Wang C.K., Tang Z., Zhou H., et al. Spatially resolved single-cell translatomics at molecular resolution. Science. 2023;380:1979. doi: 10.1126/science.add3067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Bao Y., Zhai J., Chen H., Wong C.C., Liang C., Ding Y., et al. Targeting m6A reader YTHDF1 augments antitumour immunity and boosts anti-PD-1 efficacy in colorectal cancer. Gut. 2023;72:1497–1509. doi: 10.1136/GUTJNL-2022-328845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Chotewutmontri P., Barkan A. Ribosome profiling elucidates differential gene expression in bundle sheath and mesophyll cells in maize. Plant Physiol. 2021;187:59–72. doi: 10.1093/PLPHYS/KIAB272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Zaheed O., Kiniry S.J., Baranov P.V., Dean K. Exploring evidence of non-coding RNA translation with trips-Viz and GWIPS-Viz browsers. Front Cell Dev Biol. 2021;9 doi: 10.3389/FCELL.2021.703374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Shen Z., Zeng L., Zhang Z. Translatome and transcriptome profiling of hypoxic-induced rat cardiomyocytes. Mol Ther Nucleic Acids. 2020;22:1016–1024. doi: 10.1016/J.OMTN.2020.10.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Wang Z., Gerstein M., Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63. doi: 10.1038/nrg2484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Reel P.S., Reel S., Pearson E., Trucco E., Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv. 2021;49 doi: 10.1016/j.biotechadv.2021.107739. [DOI] [PubMed] [Google Scholar]
  • 28.Kiniry S.J., Michel A.M., Baranov P.V. Computational methods for ribosome profiling data analysis. Wiley Inter Rev RNA. 2020;11 doi: 10.1002/wrna.1577. [DOI] [PubMed] [Google Scholar]
  • 29.Chothani S., Adami E., Ouyang J.F., Viswanathan S., Hubner N., Cook S.A., et al. deltaTE: detection of translationally regulated genes by integrative analysis of Ribo-seq and RNA-seq Data. Curr Protoc Mol Biol. 2019;129 doi: 10.1002/cpmb.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Liu Q., Shvarts T., Sliz P., Gregory R.I. 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. doi: 10.1093/NAR/GKAA395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.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 doi: 10.1093/bib/bbab559. [DOI] [PubMed] [Google Scholar]
  • 32.Gelhausen R., Svensson S.L., Froschauer K., Heyl F., Hadjeras L., Sharma C.M., et al. HRIBO: High-throughput analysis of bacterial ribosome profiling data. Bioinformatics. 2021;37:2061–2063. doi: 10.1093/bioinformatics/btaa959. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Song B., Jiang M., Gao L. Ribont: A noise-tolerant predictor of open reading frames from ribosome-protected footprints. Life. 2021;11 doi: 10.3390/life11070701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Bartholomaus A., Kolte B., Mustafayeva A., Goebel I., Fuchs S., Benndorf D., et al. SmORFer: a modular algorithm to detect small ORFs in prokaryotes. Nucleic Acids Res. 2021;49 doi: 10.1093/nar/gkab477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Shao D., Ahmed N., Soni N., O’Brien E.P. RiboA: a web application to identify ribosome A-site locations in ribosome profiling data. BMC Bioinforma. 2021;22 doi: 10.1186/s12859-021-04068-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Li K., Hope C.M., Wang X.A., Wang J.P. RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data. Nucleic Acids Res. 2021;48:12016–12029. doi: 10.1093/nar/gkaa1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Tjeldnes H., Labun K., Torres Cleuren Y., Chyżyńska K., Świrski M., Valen E. ORFik: a comprehensive R toolkit for the analysis of translation. BMC Bioinforma. 2021;22 doi: 10.1186/s12859-021-04254-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Cope A.L., Anderson F., Favate J., Jackson M., Mok A., Kurowska A., et al. Riboviz 2: a flexible and robust ribosome profiling data analysis and visualization workflow. Bioinformatics. 2022;38:2358–2360. doi: 10.1093/bioinformatics/btac093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.François P., Arbes H., Demais S., Baudin-Baillieu A., Namy O. RiboDoc: a Docker-based package for ribosome profiling analysis. Comput Struct Biotechnol J. 2021;19:2851–2860. doi: 10.1016/j.csbj.2021.05.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Michel A.M., Mullan J.P.A., Velayudhan V., O’Connor P.B.F., Donohue C.A., Baranov P.V. RiboGalaxy: A browser based platform for the alignment, analysis and visualization of ribosome profiling data. RNA Biol. 2016;13:316–319. doi: 10.1080/15476286.2016.1141862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Fedorova A.D., Tierney J.A.S., Michel A.M., Baranov P.V. RiboGalaxy: A Galaxy-based Web Platform for Ribosome Profiling Data Processing – 2023 Update. J Mol Biol. 2023;435 doi: 10.1016/j.jmb.2023.168043. [DOI] [PubMed] [Google Scholar]
  • 42.Wu H.Y.L., Hsu P.Y. RiboPlotR: a visualization tool for periodic Ribo-seq reads. Plant Methods. 2021;17 doi: 10.1186/s13007-021-00824-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Legrand C., Tuorto F. RiboVIEW: A computational framework for visualization, quality control and statistical analysis of ribosome profiling data. Nucleic Acids Res. 2020;48 doi: 10.1093/nar/gkz1074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wu W.S., Tsao Y.H., Shiue S.C., Chen T.Y., Tseng Y.Y., Tseng J.T. A tool for analyzing and visualizing ribo-seq data at the isoform level. BMC Bioinforma. 2021;22 doi: 10.1186/s12859-021-04192-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Jensen T.L., Hooper W.F., Cherikh S.R., Goll J.B. RP-REP ribosomal profiling reports: an open-source cloudenabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting. F1000Res. 2021;10:1–15. doi: 10.12688/F1000RESEARCH.40668.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Berg J.A., Belyeu J.R., Morgan J.T., Ouyang Y., Bott A.J., Quinlan A.R., et al. Xpressyourself: enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data. PLoS Comput Biol. 2020;16 doi: 10.1371/journal.pcbi.1007625. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Prensner J.R., Abelin J.G., Kok L.W., Clauser K.R., Mudge J.M., Ruiz-Orera J., et al. What can Ribo-Seq, immunopeptidomics, and proteomics tell us about the noncanonical proteome? Mol Cell Proteom. 2023;22 doi: 10.1016/j.mcpro.2023.100631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Bonilauri B., Holetz F.B., Dallagiovanna B. Long non-coding rnas associated with ribosomes in human adipose-derived stem cells: From rnas to microproteins. Biomolecules. 2021;11 doi: 10.3390/biom11111673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Ingolia N.T., Lareau L.F., Weissman J.S. Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes. Cell. 2011;147:789–802. doi: 10.1016/j.cell.2011.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Hasin Y., Seldin M., Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18 doi: 10.1186/s13059-017-1215-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Luo L., Gribskov M., Wang S. Bibliometric review of ATAC-Seq and its application in gene expression. Brief Bioinform. 2022;23 doi: 10.1093/bib/bbac061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Buenrostro J.D., Wu B., Chang H.Y., Greenleaf W.J. ATAC‐seq: a method for assaying chromatin accessibility genome‐wide. Curr Protoc Mol Biol. 2015;109 doi: 10.1002/0471142727.mb2129s109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Nakato R., Sakata T. Methods for ChIP-seq analysis: a practical workflow and advanced applications. Methods. 2021;187:44–53. doi: 10.1016/j.ymeth.2020.03.005. [DOI] [PubMed] [Google Scholar]
  • 54.Krassowski M., Das V., Sahu S.K., Misra B.B. State of the field in multi-omics research: from computational needs to data mining and sharing. Front Genet. 2020;11 doi: 10.3389/fgene.2020.610798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Buenrostro J.D., Wu B., Chang H.Y., Greenleaf W.J.. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Current Protocols in Molecular Biology / Edited by Frederick M Ausubel. [et Al] 2015;109:21.29.1. https://doi.org/10.1002/0471142727.MB2129S109. [DOI] [PMC free article] [PubMed]
  • 56.Landt S.G., Marinov G.K., Kundaje A., Kheradpour P., Pauli F., Batzoglou S., et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012;22:1813. doi: 10.1101/GR.136184.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Al-Amrani S., Al-Jabri Z., Al-Zaabi A., Alshekaili J., Al-Khabori M. Proteomics: concepts and applications in human medicine. World J Biol Chem. 2021;12:57–69. doi: 10.4331/wjbc.v12.i5.57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Raufaste-Cazavieille V., Santiago R., Droit A. Multi-omics analysis: paving the path toward achieving precision medicine in cancer treatment and immuno-oncology. Front Mol Biosci. 2022;9 doi: 10.3389/fmolb.2022.962743. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Petralia F., Ma W., Yaron T.M., Caruso F.P., Tignor N., Wang J.M., et al. Pan-cancer proteogenomics characterization of tumor immunity. Cell. 2024;187:1255–1277.e27. doi: 10.1016/j.cell.2024.01.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Chen C., Wang J., Pan D., Wang X., Xu Y., Yan J., et al. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023;4 doi: 10.1002/mco2.315. [DOI] [PMC free article] [PubMed] [Google Scholar]

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