RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution
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Abstract
Summary: Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named ‘ribo’. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling. Availability and implementation: For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling. Supplementary information: Supplementary data are available at Bioinformatics online.
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Cites
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 1× · see citing works
“This trans-formative approach has been applied to many organisms and can approximate translational efficiency, a major determinant of protein abundance (Kristensen et al. , 2013; Schwanha¨usser et al. , 2011).”
MethodIngolia, N. T., Lareau, L. F., & Weissman, J. S. (2011) · Cell · doi:10.1016/j.cell.2011.10.002 · Cited by 2,187 · see citing works
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Predicting the translation efficiency of messenger RNA in mammalian cells
Zheng, D., Persyn, L., Wang, J., Liu, Y., Ulloa-Montoya, F., Cenik, C., & Agarwal, V. (2026) · Nature Biotechnology · doi:10.1038/s41587-025-02712-x · Cited by 52 · see citing works
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- Authors
- Ozadam, H., Geng, M., & Cenik, C.
- Affiliations
- Department of Molecular Biosciences, University of Texas at Austin , Austin, TX 78705, USA
- The University of Texas at Austin (US)
- Corresponding author
- Cenik, Can
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-01-13
- Volume
- 36
- Issue
- 9
- Pages
- 2929-2931
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7203755
- OpenAlex
- W3000621797
- Cited by
- 41
- References
- 2
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 8 · 2023: 5 · 2022: 4 · 2021: 6 · 2020: 2 · 2018: 1
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (CA204522)
- Cancer Prevention and Research Institute of Texas (RR180042)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
- Keywords
- ribosome profiling, ribosome occupancy, protein abundance estimation, translation mechanisms, parallel computing, quality control metrics
- MeSH terms
- Proteins, Ribosomes, Software, Sequence Analysis, Ecosystem
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