RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution.

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.
(© The Author(s) 2020. Published by Oxford University Press.)