3 Ribo-seq QC check
3.1 Importance of Ribo-seq Quality Control
Ribosome profiling (Ribo-seq) provides high-resolution insights into translation by capturing ribosome-protected fragments (RPFs). To ensure the biological interpretability of Ribo-seq data, rigorous quality control (QC) is essential before proceeding with downstream analyses such as P-site mapping, translation efficiency assessment, and metagene profiling.
QC not only evaluates the technical reliability of the experiment but also helps detect and troubleshoot problems such as poor nuclease digestion, contamination by non-ribosomal fragments, or alignment errors.
3.2 Key Ribo-seq QC Metrics
Below is a list of commonly used QC indicators in Ribo-seq analysis, along with their biological significance and how to interpret them:
| QC Metric | Description | Good Quality | Potential Issues |
|---|---|---|---|
| Read Length Distribution | Distribution of lengths of aligned reads (typically 26–34 nt for RPFs) | Clear peak at expected length (e.g., ~28-30 nt) | Broad or irregular distribution (non-specific cleavage or contamination) |
| Mapping Rate | Percentage of reads aligned to genome/transcriptome | ≥ 70–80% | < 50% may indicate contamination or poor library complexity |
| rRNA Contamination | Proportion of reads mapped to ribosomal RNA | ~80% | A lower percentage indicates less rRNA contamination and results in more usable RPF reads for downstream analysis. |
| Fraction Mapping to CDS | Percentage of mapped reads falling within coding sequences | > 70% | < 50% may reflect non-specific digestion or contamination |
| 3-Nucleotide Periodicity | Triplet periodicity of reads within CDS regions | Strong periodic pattern in 1 frame | Weak/no signal may reflect mixed fragments or poor run-off conditions |
| Metagene Profile | Aggregated read density near TIS or stop codons | Clear peaks around start/stop codons | Flat or noisy signal indicates low signal-to-noise |
| Sample Correlation | Correlation between replicates or conditions | High within-group correlation (e.g. ≥ 0.9) | Low correlation suggests biological/technical variability |
| P-site Offset Calibration | Position of the ribosome P-site within RPFs | Consistent offset (e.g., 12 nt for 28-mers) | Inconsistent offsets reduce positional accuracy |
3.3 Interpretation Guidelines
💚 Good QC results indicate that the RPFs are predominantly ribosome-associated, length-specific, and mapped correctly — enabling accurate ribosome profiling.
⚠️ Deviation in one or more metrics does not always render a dataset unusable but warrants closer inspection.
❗ Consistent failure across multiple metrics suggests technical issues during library preparation or sequencing and may require repeating the experiment.
3.4 Ribo-seq Quality Assessment with riboTransVis
The riboTransVis package provides a comprehensive suite of visualization tools specifically designed for assessing the quality of Ribo-seq data. These functions enable users to evaluate data fidelity from multiple angles, helping determine whether the experimental library faithfully captures ribosome-protected fragments (RPFs) with the expected biological characteristics.
Rather than relying on a single metric, riboTransVis supports a multi-dimensional approach to quality control (QC), incorporating both read-level features and transcript-level patterns. This enables users to comprehensively inspect the integrity, specificity, and biological relevance of their data.
3.4.1 Core QC Visualization Functions
Below is a summary of key QC functions available in riboTransVis, each targeting a specific aspect of Ribo-seq data quality:
| Function Name | Description |
|---|---|
frame_plot() |
Evaluates 3-nt periodicity by plotting the frame distribution of P-sites within CDS regions. |
length_plot() |
Visualizes the distribution of read lengths after filtering, helping identify dominant RPF sizes. |
feature_plot() |
Assesses read distributions across genomic features (CDS, UTRs, introns), estimating data purity. |
whole_metagene_plot() |
Aggregates read density across whole transcript. |
relative_dist_plot() |
Shows how periodicity relative to start/stop codons. |
relative_heatmap_plot() |
Generates a heatmap showing positional enrichment of reads near start/stop codons. |
relative_offset_plot() |
Determines optimal P-site offsets by comparing positional patterns across read lengths. |
metagene_plot() |
Displays transcript-specific metagene profiles, useful for assessing translation initiation bias. |
3.4.2 Interpretation and Utility
These visualizations collectively allow researchers to:
Confirm the presence of strong triplet periodicity across CDS regions;
Verify RPF enrichment near annotated start and stop codons;
Detect contamination or non-ribosome-associated fragments (e.g., reads mapping primarily to UTRs or introns);
Calibrate and validate P-site offset estimates across read lengths;
Identify variations between replicates or conditions that may reflect biological or technical differences.
Using riboTransVis alongside RiboTrans enables automated, reproducible, and publication-ready QC assessments of Ribo-seq datasets.