Ribo-seQC: comprehensive analysis of cytoplasmic and organellar ribosome profiling data
Lorenzo CalvielloiD, Dominique SydowiD, Dermot HarnettiD, Uwe OhleriD
Abstract
SummaryRibosome profiling enables genome-wide analysis of translation with unprecedented resolution. We present Ribo-seQC, a versatile tool for the comprehensive analysis of Ribo-seq data, providing in-depth insights on data quality and translational profiles for cytoplasmic and organelle ribosomes. Ribo-seQC automatically generates platform-independent HTML reports, offering a detailed and easy-to-share basis for collaborative Ribo-seq projects.\n\nAvailabilityRibo-seQC is available at https://github.com/ohlerlab/RiboseQC and submitted to Bioconductor.\n\nContactuwe.ohler@mdc-berlin.de
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Abstract summary
Claude Opus 5.5 · xhigh effort · 2 Nov 2026 · from the abstract · CC BY-ND 4.0
Ribo-seQC is a software package that evaluates ribosome profiling data quality and translational profiles across cytoplasmic and organellar ribosomes, outputting shareable platform-independent HTML reports.
Ribo-seQC provides automated quality control and translational profile analysis for both cytoplasmic and organellar ribosome profiling datasets, producing platform-independent HTML reports to support collaborative studies.
Key results
ClaimedRibo-seQC performs quality control and translational profile analysis on Ribo-seq datasets covering both cytoplasmic and organellar ribosomes and compiles results into platform-independent HTML reports.
Presented as a software tool available on GitHub and submitted to Bioconductor; no benchmark datasets or quantitative metrics are reported in the abstract. · Abstract
Methods, data and reproducibility
The authors developed Ribo-seQC, a Bioconductor-submitted software tool that analyzes Ribo-seq datasets to assess data quality and translational profiles for cytoplasmic and organellar ribosomes and outputs platform-independent HTML reports.
- Methods & tools
- Ribo-seQC, Bioconductor
- Software & environment
- Bioconductor
- Reproducibility gaps
- No test datasets, software version numbers, or runtime benchmarks are described in the abstract.
Limitations (1)
- The abstract does not describe the specific quality metrics computed or present empirical validation on Ribo-seq datasets. · noted in this summary
Who should read:Read this if you process ribosome profiling (Ribo-seq) data and need automated quality control and HTML reporting for cytoplasmic and organellar translation.
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