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. 2024 Apr 11:9:179.
doi: 10.12688/wellcomeopenres.21000.1. eCollection 2024.

Riboseq-flow: A streamlined, reliable pipeline for ribosome profiling data analysis and quality control

Ira A Iosub  1   2   3 , Oscar G Wilkins  1   4 , Jernej Ule  1   2   3
Affiliations

Riboseq-flow: A streamlined, reliable pipeline for ribosome profiling data analysis and quality control

Ira A Iosub et al. Wellcome Open Res. .

Abstract

Ribosome profiling is a powerful technique to study translation at a transcriptome-wide level. However, ensuring good data quality is paramount for accurate interpretation, as is ensuring that the analyses are reproducible. We introduce a new Nextflow DSL2 pipeline, riboseq-flow, designed for processing and comprehensive quality control of ribosome profiling experiments. Riboseq-flow is user-friendly, versatile and upholds high standards in reproducibility, scalability, portability, version control and continuous integration. It enables users to efficiently analyse multiple samples in parallel and helps them evaluate the quality and utility of their data based on the detailed metrics and visualisations that are automatically generated. Riboseq-flow is available at https://github.com/iraiosub/riboseq-flow.

Keywords: Nextflow; ribo-seq; ribosome profiling.

Plain language summary

Ribosome profiling is a cutting-edge method that provides a detailed view of protein synthesis across the entire set of RNA molecules within cells. To ensure the reliability of such studies, high-quality data and the ability to replicate analyses are crucial. To address this, we present riboseq-flow, a new tool built with Nextflow DSL2, tailored for analysing data from ribosome profiling experiments. This pipeline stands out for its ease of use, flexibility, and commitment to high reproducibility standards. It's designed to handle multiple samples simultaneously, ensuring efficient analysis for large-scale studies. Moreover, riboseq-flow automatically generates detailed reports and visual representations to assess the data quality, enhancing researchers' understanding of their experiments and guiding future decisions. This valuable resource is freely accessible at https://github.com/iraiosub/riboseq-flow.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Overview of the main data processing steps of riboseq-flow.
The outputs useful for most users are shown. Icons from https://nf-co.re/eager under a CC-BY 4.0 license.
Figure 2.
Figure 2.. Snapshot of the MultiQC report generated by riboseq-flow.
The left sidebar helps users navigate through the report sections. A full example report for the Use case I dataset can be browsed here. More reports for the Use case II examples can be downloaded here for human brain data and here for mouse brain data.
Figure 3.
Figure 3.. Ribo-seq QC plots with read-length stratified metrics for one RNase I 293T sample re-analysed with riboseq-flow.
( A) Triplet periodicity plot showing the distribution of sub-codon positions of 5’ ends of RPFs to the protein coding regions. ( B) Percentage of useful reads: ratio of unique reads that mapped uniquely to protein-coding transcripts to the total number of adaptor-trimmed input reads. ( C) Heatmaps showing the count and position of 5’ ends of reads around the start (left) and stop codons of transcripts (right). For ribo-seq, it is expected to see the reduction of signal after the stop codon, an accumulation of read starts upstream of the start codon, and a repeating pattern every 3 nt. ( D) Read length distribution of adaptor-trimmed input reads (top) and useful reads (mapped uniquely to protein-coding transcripts). ( E) Percentage of reads mapping to abundant non-coding RNA contaminants such as rRNA. ( F) Number of reads of specific lengths before and after pre-mapping and genome alignment (before deduplication). ( G) Duplication percentage for reads of expected length.
Figure 4.
Figure 4.. Ribo-seq QC plots generated by riboseq-flow using the riboWaltz-identified P-sites for one RNase I 293T replicate .
( A) Heatmaps of P-site percentage in the three frames across untranslated and coding regions generated by riboWaltz . ( B) Meta-profiles showing the periodicity of ribosomes around start and stop codons generated by riboWaltz . ( C) P-site percentage across protein-coding transcript regions, compared to a theoretical distribution derived from feature lengths (RNAs). ( D) Ribo-seq Unit Step Transformation (RUST) analysis showing read-length resolved meta-profiles of Kullback–Leibler divergence as a measure of sequence bias at positions relative to the inferred P-sites (sub-sampled to 1 million reads).
Figure 5.
Figure 5.. Example visualisation of read breakdown through the main steps of riboseq-flow.
( A) Sankey network for RNase I (hR1) re-analysed 293T ribo-seq data from . ( B) Sankey network for P1 RNase (hP1) re-analysed 293T ribo-seq data from .
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References

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Grants and funding

This work was supported by Wellcome [215593]; the Francis Crick Institute which receives its core funding from Cancer Research UK (CC0102), the UK Medical Research Council (CC0102), and Wellcome (CC0102); and by the UK Dementia Research Institute [RE21605] which receives its funding from the UK Medical Research Council.

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