De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
February 18th, 2022
Translating ribosomes decode three nucleotides per codon into peptides. Their movement along mRNA, captured by ribosome profiling, produces the footprints exhibiting characteristic triplet periodicity. This protocol describes how to use RiboCode to decipher this prominent feature from ribosome profiling data to identify actively translated open reading frames at the whole-transcriptome level.
Interpretation of the sequencing data generated by the ribosome profiling experiment is critical for quantitatively measuring the translational activities of ribosomes on mRNA, and for studying the mechanisms of translational regulation. In this protocol, we will describe the computational procedure for utilizing the ribosome profiling data and RiboCode, a command line tool to decode mRNA translation at the genome-wide scale and single nucleotide resolution. This method allows to search for the novel peptides arising from the genomic regions outside of the annotated protein coding genes, and offers an opportunity to quantify the rate of mRNA translation.
To begin, open a Linux terminal window, and create a conda environment by executing the command. Switch to the created environment, and install RiboCode and dependencies by executing the command. To get the genome reference files for the reference sequence, go to the Ensembl website, then click download, followed by FTP download.
Click the FASTA option in the column DNA FASTA, and select the row where species is human, shown in the table on the website page. On the Ensembl website page, copy the link as mentioned in the text, then download and unzip the files in the terminal by executing the command. For reference annotation, right click GTF in the column gene sets in the last open webpage.
Copy the link, and download it using the command. To get rRNA sequences, open UCSC genome browser, then click tools, and select table browser in the dropdown list. On the UCSC genome browser page, specify mammal for clade, human for genome, all tables for group, R mask for table, and genome for region.
For filter, click create to go to a new page, and set rep class as does match rRNA. Click submit, and then set the output format to sequence, and output file name as HG38_rRNA. FA.Finally, click get output, and then select get sequence to retrieve the rRNA sequence.
To get ribosome profiling datasets from sequence read archive, download the replicate samples of the si-eIFe treatment group, and rename them by executing the command. Then download the replicate samples of the control group, and rename them by executing the command. To remove rRNA contamination, start indexing rRNA reference sequences by executing the command.
After indexing, align the reads to rRNA reference to rule out the reads originating from rRNA by executing the command. Start by creating a genome index by executing the command. Then align the clean reads with no rRNA contamination to the created reference by executing the command, and then sort and index alignment files by executing the command.
Prepare the transcript annotations by executing the command. Select ribosome protected fragments of specific lengths, and identify their P-site positions by executing the command. Edit the configuration files for each sample and merge them.
Then run RiboCode by executing the command. The frequency distribution of the lengths of the reads showed that most ribosome protected fragments correspond to 25 to 35 nucleotides. The P-site locations for different lengths of ribosome protected fragments were determined by examining the distances from their five prime ends to the annotated start and stop codons.
The mapping results show that 10, 394 genes encode for annotated open reading frames. Further, 509 and 168 genes encode for upstream and downstream open reading frames, while 939 genes encode for either upstream or downstream open reading frames, overlapped with known annotated open reading frames. Further, 68 protein coding genes and 2, 601 non-coding genes encode for novel open reading frames.
Length distribution showed that upstream, downstream, novel, and overlapped open reading frames were shorter than the annotated open reading frames. Relative ribosome protected fragment counts were calculated for each open reading frame, revealing that the ribosome densities of upstream open reading frames were significantly higher in eIF3e deficient cells than in control cells. The metagene analysis revealed that a mass of ribosomes stalled between codons 25 and 75 downstream of the start codon, suggesting that the translation elongation might be blocked early in eIF3e deficient cells.
The P-sites density profiles for upstream open reading frames of PSMA6 and downstream open reading frames of gene SENP3-EIF4A1 were examined, demonstrating the periodicity patterns and densities of ribosome protected fragments. Checking the locations of reads around the start and stop codons of known protein coding regions is necessary for evaluating the periodic properties of reads for each length. RiboCode, together with another command line tool, RiboMiner can also perform quality control and multiple analyses such as quantifying and visualizing the ribosomes'occupancies on the predicted open reading frames.
This computational tool provides a high throughput way to identify uncanonical translation events with ribosome profiling data in specific physiological contexts, and how the translation modulates in response to the stimulus.
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Overview
This study explores the computational interpretation of ribosome profiling data to enhance the understanding of translational regulation in mRNA. Using RiboCode, researchers can identify actively translated open reading frames (ORFs) at a genome-wide level, unveiling insights into non-canonical peptides and their translation dynamics.
Key Study Components
Research Area
- Translational regulation
- Gene expression
- Computational biology
Background
- Ribosome profiling offers quantitative insights into ribosomal activity on mRNA.
- Understanding the dynamics of translation provides critical information about gene regulation.
- The study aims to utilize RiboCode for genome-wide translation analysis.
Methods Used
- Ribosome profiling experiments to gather sequencing data.
- Utilization of RiboCode for decoding mRNA translation.
- Analysis of ribosome protected fragments and P-site positions to study translation dynamics.
Main Results
- Identification of 10,394 genes with annotated ORFs and various novel translation events.
- Increased ribosome density in upstream ORFs in eIF3e deficient cells compared to control.
- Halted translation elongation observed in certain genes, highlighted by P-site density profiles.
Conclusions
- This study demonstrates the efficacy of RiboCode in analyzing ribosome profiling data for uncovering novel translation events.
- Highlights the importance of translational modulation in response to physiological stimuli.
Frequently Asked Questions
Executive Industry Relevance
Ribosome profiling enables genome-wide detection of actively translated open reading frames, supporting target validation by revealing context-dependent translation events. RiboCode provides a scalable computational workflow to identify novel peptides and quantify ribosome occupancy, aiding mechanistic de-risking in early discovery. This approach enhances predictive confidence in lead identification by linking translational activity to physiological stimuli.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses through detection of actively translated ORFs in specific physiological contexts.
- Operational Value: Supports biological de-risking by identifying functional translation events outside annotated coding regions.
- Scientific Value: Facilitates target confidence by linking ribosome density changes to genetic perturbations such as eIF3e deficiency.
Screening & Assay Development
- Scientific Value: Prepares validated biological systems for downstream screening by mapping ribosome-protected fragment densities across ORFs.
- Operational Value: Enables assay standardization through quantification of relative ribosome protected fragment counts per ORF.
- Scientific Value: Supports predictive confidence by revealing periodicity patterns and P-site density profiles indicative of active translation.
Translational & Preclinical Research
- Scientific Value: Aligns with disease-relevant systems by identifying translation modulation in response to stimuli.
- Operational Value: Ensures translational continuity from discovery through preclinical validation via visualization of ribosome occupancies.
- Scientific Value: Aids risk-adjusted advancement decisions by highlighting stalled translation elongation as a regulatory mechanism.
Pipeline & Workflow Integration
RiboCode integrates into the discovery continuum from early target validation through lead identification by providing genome-wide translation readouts.
- Discovery Biology: Supports hypothesis testing and pathway clarification by identifying novel translation events in non-coding regions.
- Screening: Delivers assay readiness through quantification and visualization of ribosome occupancy on predicted ORFs.
- Analytics: Provides quantitative dependent variable measurements such as ribosome density and P-site periodicity for comparative condition analysis.
- Translational Research: Connects to preclinical continuity by linking translation dynamics to physiological stimuli like eIF3e deficiency.
- Enterprise Reuse: Functions as a reusable computational platform for analyzing ribosome profiling data across multiple experimental contexts.
Operational & Enterprise Impact
- Scientific Value: Predictive confidence through detection of actively translated ORFs and quantification of translation rates.
- Operational Value: Standardization and reproducibility via a streamlined pipeline from raw data to interpreted output files.
- Strategic Value: Improved go/no-go decisions by reducing mechanistic ambiguity in target validation.
- Portfolio Impact: Risk-adjusted prioritization based on translation evidence in disease-relevant contexts.
Implementation Considerations
- Requires expertise in computational biology and ribosome profiling data interpretation.
- Dependent on access to genome reference files, annotation databases, and rRNA sequence resources.
- Necessitates cross-team standardization of configuration files and read length selection parameters.
- Involves adaptation considerations when applying the pipeline to different model systems or stimulus conditions.
- Practical limitation: Efficient identification of translating ORFs remains challenging due to distorted and ambiguous signals in ribosome profiling data.
Biopharma FAQs
Chapters in this video
0:04
Introduction
1:01
Environment Setup and RiboCode Installation
1:23
Data Preparation
3:35
Removing Ribosomal RNA Contamination
3:59
Aligning the Clean Reads to the Genome
4:25
Size Selection of Ribosome Protected Fragments, Identification of P-Sites, and De novo Annotate Translating Open Reading Frames
4:59
Results: Identification of the Novel Open Reding Frames (ORFs) with the Application of RiboCode
7:20
Conclusion
