The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
RiboFlow, RiboR and RiboPy: An ecosystem for analyzing ribosome profiling data at read length resolution
Open accessPreprint — published as 10.1093/bioinformatics/btaa028 · Paper page
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 complete 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
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Metadata
- Authors
- Ozadam, H., Geng, M., & Cenik, C.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Cenik, Can
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2019-11-26
- Volume
- 36
- Issue
- 9
- Pages
- 2929-2931
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC7203755
- OpenAlex
- W3000621797
- Cited by
- 41
- References
- 1
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 8 · 2023: 5 · 2022: 4 · 2021: 6 · 2020: 2
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Cancer Prevention and Research Institute of Texas (RR180042)
- University of Texas at Austin
- National Institutes of Health (CA204522)
- Department of Mechanical Engineering, University of Texas at Austin
- National Cancer Institute (R00CA204522, K99CA204522)
- 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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