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4.7 Codon-level analysis

Codon-level analysis summarizes ribosome profiling signal at codon resolution. These modules are useful for studying codon pausing, codon occupancy, codon decoding time, codon selection time, coverage-dependent variation, local meta-codon profiles, and differential codon enrichment between experimental groups.

Required upstream results

Before running codon-level analysis, make sure that the upstream Ribo-seq results have passed the basic quality-control steps:

  • A reliable offset table has been generated and used for RPF density construction.
  • The selected reads show clear 3-nt periodicity.
  • CDS enrichment is reasonable.
  • The merged RPF density table has enough reads for the selected genes.
  • Biological replicates show acceptable reproducibility.

Most codon-level modules use the merged density table generated by rpf_Merge, for example:

RIBO_merged.txt

For CDT and CST analysis, a matched RNA-seq density table is also required:

RNA_merged.txt

Submodules

Section Command Purpose
4.7.1 rpf_Pausing Calculate relative codon pausing scores from codon-level RPF density.
4.7.2 rpf_Occupancy Calculate absolute and relative codon occupancy.
4.7.3 rpf_CDT Estimate codon decoding time by combining Ribo-seq and RNA-seq density.
4.7.4 rpf_CST Estimate codon selection time using iterative Ribo-seq/RNA-seq codon-level calculations.
4.7.5 rpf_CoV, rpf_Cumulative_CoV Quantify coefficient of variation across CDS regions or cumulative CDS positions.
4.7.6 rpf_Meta_Codon Extract and plot local RPF density around selected codons or codon motifs.
4.7.7 rpf_Odd_Ratio Compare codon-associated RPF enrichment between control and treatment samples.

A typical codon-level analysis can be organized as follows:

  1. Run rpf_Pausing and rpf_Occupancy to obtain basic codon-level density summaries.
  2. Run rpf_CDT or rpf_CST when matched RNA-seq density is available.
  3. Run rpf_CoV or rpf_Cumulative_CoV to evaluate coverage-dependent variability.
  4. Run rpf_Meta_Codon for focused analysis around selected codons or motifs.
  5. Run rpf_Odd_Ratio when comparing codon-level enrichment between two groups.

Notes

  • Use the same offset and frame-selection strategy across modules when results need to be compared.
  • For high-quality Ribo-seq data, frame 0 is commonly used after offset correction, but all can be useful during exploratory analysis.
  • Parameters such as --tis, --tts, and --stop help avoid start/stop-proximal artifacts.
  • Codon-level statistics are sensitive to low coverage, so choose -m carefully according to sequencing depth and study design.