Ribosome profiling provides a detailed global snapshot of protein synthesis in a cell. At its core, this technique makes use of the observation that a translating ribosome protects around 30 nucleotides of the mRNA from nuclease activity. High-throughput sequencing of these ribosome protected fragments (called ribosome footprints) offers a precise record of the number and location of the ribosomes at the time at which translation is stopped. Mapping the position of the ribosome protected fragments indicates the translated regions within the transcriptome. Moreover, ribosomes spend different periods of time at different positions, leading to variation in the footprint density along the mRNA transcript. This provides an estimate of how much protein is being produced from each mRNA. Importantly, ribosome profiling is as precise and detailed as RNA sequencing. Even in a short time, since its introduction in 2009, ribosome profiling has been playing a key role in driving biological discovery.

We have developed this bioinformatics toolkit, riboviz, for analyzing ribosome profiling datasets. riboviz consists of a comprehensive and flexible analysis pipeline. The current version, riboviz 2, has been extensively tested on datasets from yeast, various other fungi, mouse, bacteria, and archaea.

All the code for processing the raw reads is available in this repository.

Configuration files and annotation files for many datasets from many organisms are available at the riboviz/example-datasets repository.

Quick start:

• Install riboviz and dependencies
• Quick install scripts (Ubuntu and CentOS only)
• Map mRNA and ribosome protected reads to transcriptome and collect data into an HDF5 file. Run a "vignette" of the riboviz workflow to see riboviz's capabilities.
• Run an example of a configuration using environment variable tokens.
• Run UMI extraction, deduplication and demultiplexing examples. Run riboviz on simulated data, to see how riboviz handles duplicated and multiplexed data.
• Upgrade configuration files to current version

Usage:

• What the riboviz workflow does
• Configuring the riboviz workflow
• Generate YAML configuration file. Use an online tool to generate a riboviz YAML configuration file.
• Running the riboviz Nextflow workflow
• How To Run the riboviz Interactive Data Visualization On Your Data
• riboviz output files and figures
• Running the riboviz workflow on Eddie
• Memory and storage. Information and advice relating to riboviz's memory and storage requirements.

General:

• Git branching model
• Style guide: Style guidelines for the riboviz source code, documentation, parameters and files.

Development:

• Install developer dependencies
• Developing Python components
• Developing R components
• Developing Nextflow workflow
• Adding, using, renaming, and removing configuration parameters
• Adding, renaming, and removing temporary or output files
• Adding and updating dependencies
• Developing and running integration tests
• Writing and updating documentation
• Related riboviz repositories

Releasing:

• Creating a test data repository
• Building a release

• riboviz command-line tools.
• Content and provenance of repository data files.

To cite riboviz, please use both of the following references:

Cope AL, Anderson F, Favate J, Jackson M, Mok A, Kurowska A, MacKenzie E, Shivakumar V, Tilton P, Winterbourne SM, Xue S, Kavoussanakis K, Lareau LF, Shah P, Wallace EWJ. 2021. riboviz 2: A flexible and robust ribosome profiling data analysis and visualization workflow. bioRxiv. doi: 10.1101/2021.05.14.443910.

Wallace, Edward; Anderson, Felicity; Kavoussanakis, Kostas; Jackson, Michael; Shah, Premal; Lareau, Liana; et al. (2021): riboviz: software for analysis and visualization of ribosome profiling datasets. figshare. Software. doi: 10.6084/m9.figshare.12624200

The reference for riboviz version 1, which focused on yeast, is:

riboviz: analysis and visualization of ribosome profiling datasets, Carja et al., BMC Bioinformatics 2017. doi:10.1186/s12859-017-1873-8.

For contributors and funders, see Acknowledgements.

For citations of third-party software used by riboviz, see References.

riboviz is Copyright (2016-2021) The University of Edinburgh; Rutgers University; University of California, Berkeley; The University of Pennsylvania.

riboviz is released under the Apache License 2.0.