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4.6 Gene-level analysis

Function

Gene-level analysis summarizes Ribo-seq or RNA-seq density at transcript and gene scale. These modules quantify CDS-level abundance, detect in-gene reading-frame switches, and draw IGV-like density profiles for genes of interest.

Submodules

Section Command Description
4.6.1 Gene quantification rpf_Quant Summarize RPF counts, RPM, RPKM, and TPM for CDS and optional UTR regions.
4.6.2 Frameshift detection rpf_Shift Detect in-gene reading-frame switches (programmed ribosomal frameshifts) from the merged density file.
4.6.3 Gene density plot rpf_Geneplot Draw IGV-like transcript/genome-coordinate density profiles for genes of interest.

Suggested directory structure

The gene-level modules are organized under sce/4.ribo-seq/5.riboparser/ (see the project layout in 3 New project). Sub-directories 01.qc to 09.correlation are created by the quality-control modules (4.5); create the gene-level ones (10.quantification, 20.frame_shift, and 23.geneplot, matching sections 4.6.1-4.6.3) as follows:

mkdir -p 10.quantification 20.frame_shift 23.geneplot

Input dependency

The gene-level modules consume the merged density matrix produced by rpf_Merge in 05.merge (see 4.5.5 Merge density), usually <prefix>_rpf_merged.jsonl.gz (or the plain TXT table). The per-module input and output are summarized below:

Section Main input Main output
Gene quantification merged density (-r) raw RPF counts and RPM/RPKM/TPM tables plus QC plots
Frameshift detection merged frame-resolved density (-r, use the JSONL file) frameshift candidates, classification summary, and per-candidate plots
Gene density plot merged density (-r) and a target gene or target list (-g / --target-list) IGV-like per-target, per-sample density figures

rpf_Shift needs the frame-resolved density records, so it should be run on the *_rpf_merged.jsonl.gz output of rpf_Merge rather than a frame-unresolved summary. rpf_Geneplot reads the same merged file and accepts both the compact JSONL and the plain TXT table.

Notes

  • For standard translation-level quantification, use CDS density and trim codons near the start and stop codons to reduce initiation and termination artifacts.
  • Gene-level metrics are useful for expression overview, replicate quality control, and downstream differential analysis, but they should be interpreted together with read periodicity, metagene profiles, and coverage uniformity.
  • Frameshift candidates from rpf_Shift are statistical detections: always validate the shift site and direction manually (e.g. with the per-candidate gene plots) before reporting biological conclusions. rpf_Geneplot is the general-purpose tool for visually inspecting the density of any gene of interest.