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. 2021 May 7:19:2851-2860.
doi: 10.1016/j.csbj.2021.05.014. eCollection 2021.

RiboDoc: A Docker-based package for ribosome profiling analysis

Pauline François  1 , Hugo Arbes  1 , Stéphane Demais  1 , Agnès Baudin-Baillieu  1 , Olivier Namy  1
Affiliations

RiboDoc: A Docker-based package for ribosome profiling analysis

Pauline François et al. Comput Struct Biotechnol J. .

Abstract

Ribosome profiling (RiboSeq) has emerged as a powerful technique for studying the genome-wide regulation of translation in various cells. Several steps in the biological protocol have been improved, but the bioinformatics part of RiboSeq suffers from a lack of standardization, preventing the straightforward and complete reproduction of published results. Too many published studies provide insufficient detail about the bioinformatics pipeline used. The broad range of questions that can be asked with RiboSeq makes it difficult to use a single bioinformatics tool. Indeed, many scripts have been published for addressing diverse questions. Here (https://github.com/equipeGST/RiboDoc), we propose a unique tool (for use with multiple operating systems, OS) to standardize the general steps that must be performed systematically in RiboSeq analysis, together with the statistical analysis and quality control of the sample. The data generated can then be exploited with more specific tools. We hope that this tool will help to standardize bioinformatics analyses pipelines in the field of translation.

Keywords: Bioinformatics; Docker; FAIR; OAZ1; Polyamines; Riboseq; Ribosome; Tool for ribosome analysis; Translation.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Overview of the full RiboDoc workflow. Every tool used for each step is specified near the corresponding arrow. The different elements are categorized and differentiated by color: yellow: operating systems compatible with RiboDoc; green: files to be provided by the user as input; purple: actions performed by the user; blue: steps performed in the analysis; red: main final output files available at the end of the analysis. The * indicates optional steps that can be performed if the user provides specific files. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Read length distribution. A) The number of reads, according to length (between 25 and 35 nucleotides) for the two yeast samples, OAZwt and OAZif, is represented. The length of the main population is indicated. B) Read enrichment in CDS vs UTR for HEK293T samples.
Fig. 3
Fig. 3
Metagene periodicity. Each graph shows the coverage of the CDS and UTR regions of the metagene for a specific read length (here 28 nucleotides), determined by representing all the reads starting at a specific relative position. Each of the three possible reading frames is represented in a different color (blue, green and red). The coverage is shown according to the window chosen by the user. In this example: from 50 nucleotides before the start codon to 100 nucleotides after the start codon, and then from 100 nucleotides before the stop codon to 50 nucleotides after the stop codon. To help the reader, we manually added the red arrows to indicate the position of the START and STOP codons and the offset observed that is due to only the first nucleotide on the 5′ side of the reads being counted. A) Periodicity for the OAZwt yeast strain. B) Periodicity for the HEK293T cell line. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 4
Fig. 4
Statistical analysis. Scatter plots for the HEK293T cell and yeast data, with Spearman’s correlation coefficients for the relationship between each pair of samples. Each scatter plot shows the log10 read counts for every gene in a sample relative to those for another sample.
Fig. 5
Fig. 5
MA-plots and volcano plots. MA-plots display the difference in expression between two conditions (log2FC) as a function of the number of read counts for each gene. Volcano plots represent the difference in expression as a function of the −log10 adjusted p-value obtained by DESeq2 for the differential analysis for each gene. Red dots indicate genes that are differentially expressed, whereas black dots show genes that are not differentially regulated. The user can define the threshold for statistical significance. For example, for yeast data, the significance threshold for the MA-plots and volcano plots is set at 0.01. For HEK293T cells, the same threshold (0.01) was used for both graphs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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