How much input material is required for the enhanced ribosome profiling workflow?

We accept input from remarkably low amounts of starting material, including small tissue biopsies or low-cell-number samples, thanks to our optimised enrichment protocol; this makes the method applicable when sample quantity is limited and supports studies from rare or precious biological specimens.

What is the minimal change in translational efficiency that can be reliably detected?

Because we compute translation efficiency by normalising ribosome footprint density to matched mRNA levels, we routinely detect fold-changes in translational efficiency at the gene level that are biologically meaningful; paired with differential translation gene analysis, we deliver profiles for genes showing statistically significant shifts in translational state.

Can this service identify non-canonical translation events such as uORFs or micropeptides?

Yes — our pipeline includes ORF prediction including non-ATG initiation sites, novel peptide discovery, codon usage frequency analysis and lncRNA/circRNA coding-potential assessment, enabling researchers to uncover previously unannotated translation events and micropeptides.

What species and sample types are supported?

The service currently supports human, mouse and rat as standard, and we offer exploratory evaluation for other organisms or single-cell applications; please contact our team to discuss custom sample types and special requirements.

How are results delivered and how should they be interpreted?

You will receive raw sequencing (FASTQ) files, aligned read datasets, translation efficiency metrics, ORF/start-site calls, codon usage reports, and integrative RNA-seq comparison outputs; our comprehensive report is designed for direct interpretation, figure production and publication readiness.

How do I design controls and replicates for a translational profiling experiment?

We recommend including at least two biological groups (e.g., control vs treatment) and ideally three or more biological replicates per group to support rigorous differential translational analysis and pathway enrichment; matched RNA-seq data should be collected for each sample to enable integrated transcriptome–translatome comparisons.

Enhanced Ribosome Profiling (Enhanced Ribo-seq) Service - High-Resolution Translational Analysis for Active Protein Synthesis

CD Genomics introduces an enhanced ribosome profiling (enhanced Ribo-seq) workflow that isolates actively translating ribosomes for a clearer, more dynamic view of gene expression and protein synthesis.

This next-generation platform improves conventional Ribo-seq by delivering single-nucleotide precision, detecting alternative ORFs, and enabling low-input compatibility across human, mouse, and rat models.

Key Highlights:

Quantify translation efficiency genome-wide at single-base resolution.
Distinguish actively translating ribosomes from stalled complexes for accurate efficiency measurement.
Identify alternative ORFs and translation start sites missed by standard methods.
Integrate Enhanced Ribo-seq and RNA-seq data for a complete translatome–transcriptome perspective.

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Enhanced ribosome profiling illustration showing active ribosomes translating along mRNA with single-nucleotide resolution and RNA-seq integration

Why Accurate Ribosome Profiling Matters

Studies show that nearly 40% of ribosomes captured by standard Ribo-seq are stalled, leading to an underestimation of translation efficiency.

Conventional Ribo-seq measures all ribosomes bound to mRNA — both active and paused — reducing the accuracy of translation readouts.

CD Genomics' Enhanced Ribosome Profiling focuses only on actively translating ribosomes, improving translation efficiency accuracy and correlation with proteomics.

For a comparison with polysome-based methods, explore our Polysome Profiling Service.

Enhanced vs Standard Ribosome Profiling

Feature	Enhanced Ribosome Profiling	Standard Ribo-seq
Target ribosomes	Actively translating ribosomes only	All ribosomes bound to mRNA (active + stalled)
Translation efficiency accuracy	High — directly tied to protein synthesis	Moderate — includes paused/stalled ribosomes
Input material requirement	Significantly reduced — supports low-input or small samples	Higher input required
Sensitivity	Detects low-abundance translation events, novel ORFs	May miss subtle or rare translational events
Workflow type	Tag- and antibody-free enrichment, streamlined protocol	Often requires gradient fractionation or large volumes
Proteomics correlation	Stronger match with protein output	Weaker correlation due to mixed ribosome states
Compatible sample types	Bulk, low-input, potentially single-cell	Typically bulk only
Ideal for applications	Translational regulation, drug response, novel peptide discovery	Global ribosome occupancy, broader translation load studies

Technical Advantages

Specific identification of translating RNA molecules

Our enhanced ribosome profiling workflow isolates ribosomes engaged in active peptide elongation, accurately depicting the translation landscape at single-gene and codon levels.

Low sample requirement — suitable for limited or single-cell material

Optimized chemistry and streamlined sequencing preparation enable reliable results from minimal input, making it ideal for rare tissues, primary cells, or precious clinical research samples.

High-precision active ribosome maps at single-nucleotide resolution

Achieve codon-level mapping of ribosome footprints to uncover translation start sites, elongation dynamics, and frame-specific activity with unmatched accuracy.

These technical advantages allow researchers to monitor translation efficiency and protein synthesis more precisely than with standard ribosome profiling or polysome-based methods.

Enhanced Ribosome Profiling Workflow Overview

We follow a refined ribosome profiling workflow designed for precision and reproducibility.

Sample Preparation – Cells or tissues are rapidly lysed under optimized conditions to preserve ribosome–mRNA complexes.
Selective Ribosome Enrichment – We isolate actively translating ribosomes and remove inactive complexes to ensure reliable results.
Library Construction – Ribosome-protected fragments (~28–34 nt) are purified and converted into sequencing-ready libraries through adapter ligation and reverse transcription.
Next-Generation Sequencing (NGS) – High-depth sequencing delivers codon-level resolution for accurate translation efficiency analysis.

Enhanced ribosome profiling workflow diagram showing sample preparation, ribosome enrichment, sequencing, and data analysis with single-nucleotide precision

Bioinformatics and Data Analysis

Analysis Type	Content Description
Active Ribosome Profiling (Ribo-seq)
1. Genome-wide translational activity profiling	Identify actively translating regions and ribosome density across all transcripts.
2. Gene-level translation efficiency (TE) calculation	Compute TE by normalising ribosome footprints to RNA abundance.
3. Differential TE gene analysis	Detect genes showing significant shifts in translational efficiency between groups.
4. GO enrichment of differential TE genes	Reveal functional categories associated with translation-level regulation.
5. KEGG pathway enrichment of differential TE genes	Highlight metabolic or signalling pathways influenced by translational control.
6. Start codon prediction (including non-ATG initiation)	Detect canonical and non-canonical translation initiation sites.
7. ORF (open reading frame) prediction	Map precise ORF boundaries and identify alternative coding frames.
8. Novel protein and micropeptide identification	Discover small peptides and unannotated coding sequences from ribosome footprints.
9. Codon usage and frequency analysis	Evaluate codon bias and its impact on translational efficiency.
10. lncRNA / circRNA coding potential prediction	Assess ribosome association with non-coding RNAs to identify potential translation events.
longRNA-seq (Transcriptome Control Dataset)
1. Data quality control (QC)	Evaluate sequencing depth, base quality, and read distribution.
2. Read alignment and quantification	Map reads to reference genomes and quantify expression levels.
3. mRNA differential expression analysis	Identify transcriptional changes between experimental conditions.
4. lncRNA differential expression analysis	Characterise long non-coding RNA expression trends.
5. GO enrichment of differential genes	Annotate biological processes altered at the transcriptional level.
6. KEGG enrichment of differential genes	Reveal pathways regulated at the mRNA stage.
7. circRNA differential expression analysis	Detect and quantify circular RNA expression changes.

We combine ribosome profiling (Ribo-seq) data with RNA-seq datasets to calculate translation efficiency (TE), predict ORFs and novel peptides, and uncover translational control mechanisms across conditions.

This integrated analysis links transcription and translation, helping researchers interpret gene regulation with greater biological accuracy.

Analytical Strategy

Understanding gene expression requires studying not only transcription but also translation — the final step that defines protein output. Traditional RNA-seq measures transcript abundance, yet protein levels often diverge from mRNA expression. Our Enhanced Ribosome Profiling workflow captures this missing layer by directly quantifying translational activity through ribosome-protected fragments.

1. Expression-Level Integration

By combining translational profiling and longRNA-seq, researchers can correlate RNA abundance with translation efficiency. This dual-omics design distinguishes whether changes in protein production arise from transcriptional or translational regulation.

When RNA–protein correlation is poor

Translational profiling pinpoints post-transcriptional regulation and identifies transcripts that are ribosome-bound but translationally silent.

When differential expression lists are extensive

Integrating translation data refines target selection, highlighting genes whose ribosome occupancy truly changes rather than those affected by transcriptional noise.

When validating expression patterns

Co-expression and clustering analyses between translation efficiency and RNA expression reveal consistent regulatory modules, guiding downstream validation or functional studies.

Integrated ribosome profiling and RNA-seq workflow showing translation efficiency analysis and gene regulation mapping.

2. Analytical Concept Flow

Input – Parallel RNA-seq and ribosome profiling datasets
Processing – Normalization, differential expression, and translation efficiency computation
Integration – Correlation of RNA abundance with ribosome occupancy
Output – Prioritized gene list showing verified translational regulation

This workflow delivers a complete view of the transcriptome-to-proteome continuum, enabling more accurate biological interpretation in areas such as functional genomics, drug response studies, and agricultural trait development.

Technical Applications (with longRNA-seq Integration)

Pairing ribosome profiling (Ribo-seq) with longRNA-seq links translation to transcriptional context. This joint design improves biological interpretation and strengthens downstream decision-making.

Map active ribosome distribution and translational activity

Define ribosome occupancy along each transcript at single-base resolution, separating active from stalled complexes for reliable translation efficiency readouts.

Infer translation start sites and ORF positions

Detect canonical and non-canonical initiation, upstream ORFs, and alternative coding frames missed by expression-only assays.

Quantify protein synthesis efficiency per gene

Compute TE by normalising footprints to matched RNA abundance, enabling robust ribosome profiling data analysis across conditions.

Resolve translational regulation versus gene expression changes

Distinguish transcription-driven shifts from true translational control when Ribo-seq and RNA-seq trends diverge.

Discover novel proteins and micropeptides

Reveal translated sORFs within presumed non-coding RNAs, guiding target validation and proteomics follow-up.

Sample Requirements

Sample Type	Minimum Requirement	Notes
Cell samples	≥ 1 × 10⁶ cells / sample	Cultured or primary cells
Tissue samples	≥ 50 mg / sample	Flash-frozen without preservatives
Supported species	Human, Mouse, Rat	Other species upon consultation

Experimental Design:

Minimum of two groups (e.g., control vs treatment)
Three biological replicates per group recommended
Each sample analysed for both ribosome profiling and RNA-seq

Example Results

Our Enhanced Ribosome Profiling service provides publication-ready data illustrating active translation dynamics across the genome. Below are representative outputs from internal validation experiments.

ribosome profiling read-length distribution showing 28–32 nt footprint peak for active translation analysis Figure 1. Read-length distribution of ribosome footprints
Ribosome-protected fragments (RPFs) cluster predominantly between 28–32 nt, reflecting genuine ribosome footprints and confirming high data quality.

P-site distribution plot showing enrichment in coding regions for translation efficiency mapping Figure 2. P-site signal distribution across transcript regions
The majority of P-site signals are enriched in coding sequences (CDS), with minor presence in 5′-UTRs and 3′-UTRs, consistent with active protein synthesis.

ribosome profiling periodicity plot demonstrating 3-nt signal pattern across CDS regions Figure 3. Three-nucleotide periodicity pattern
Footprint density exhibits a clear 3-nt periodicity along coding regions—an established hallmark of active elongating ribosomes.

codon usage frequency chart comparing translational bias across experimental groups Figure 4. Codon usage frequency comparison
Differential codon usage between control and treatment samples reveals changes in translational efficiency and tRNA adaptation under varying conditions.

Ribosome Footprint Integrity

Short protected fragments (~28–32 nt) confirm authentic ribosome-bound RNA and high data precision.

Active Translation Region Mapping

P-site enrichment within CDS demonstrates active translation and correct frame assignment.

High-Fidelity Periodicity Signal

Clear 3-nt periodicity validates codon-level resolution, ensuring accurate translation efficiency analysis.

Codon Usage and Translational Bias

Differences in codon usage reflect dynamic adaptation of tRNA pools and condition-specific translational control.

FAQs – Frequently Asked Questions

- How much input material is required for the enhanced ribosome profiling workflow?
  - We accept input from remarkably low amounts of starting material, including small tissue biopsies or low-cell-number samples, thanks to our optimised enrichment protocol; this makes the method applicable when sample quantity is limited and supports studies from rare or precious biological specimens.
- What is the minimal change in translational efficiency that can be reliably detected?
  - Because we compute translation efficiency by normalising ribosome footprint density to matched mRNA levels, we routinely detect fold-changes in translational efficiency at the gene level that are biologically meaningful; paired with differential translation gene analysis, we deliver profiles for genes showing statistically significant shifts in translational state.
- Can this service identify non-canonical translation events such as uORFs or micropeptides?
  - Yes — our pipeline includes ORF prediction including non-ATG initiation sites, novel peptide discovery, codon usage frequency analysis and lncRNA/circRNA coding-potential assessment, enabling researchers to uncover previously unannotated translation events and micropeptides.
- What species and sample types are supported?
  - The service currently supports human, mouse and rat as standard, and we offer exploratory evaluation for other organisms or single-cell applications; please contact our team to discuss custom sample types and special requirements.
- How are results delivered and how should they be interpreted?
  - You will receive raw sequencing (FASTQ) files, aligned read datasets, translation efficiency metrics, ORF/start-site calls, codon usage reports, and integrative RNA-seq comparison outputs; our comprehensive report is designed for direct interpretation, figure production and publication readiness.
- How do I design controls and replicates for a translational profiling experiment?
  - We recommend including at least two biological groups (e.g., control vs treatment) and ideally three or more biological replicates per group to support rigorous differential translational analysis and pathway enrichment; matched RNA-seq data should be collected for each sample to enable integrated transcriptome–translatome comparisons.

References:

Hu, W., Zeng, H., Shi, Y. et al. Single-cell transcriptome and translatome dual-omics reveals potential mechanisms of human oocyte maturation. Nat Commun 13, 5114 (2022).
Zhang C, Wang M, Li Y, et al. Profiling and functional characterization of maternal mRNA translation during mouse maternal-to-zygotic transition. Sci Adv 2022 Feb 04;8(5)
[3] JaegerAM, Stopfer LE, Ahn R, et al. Deciphering the immunopeptidome in vivo reveals new tumour antigens. Nature 2022 Jul;607(7917)