XPRESSyourself: Enhancing, Standardizing, and Automating Ribosome Profiling Computational Analyses Yields Improved Insight into Data

Abstract

Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit's performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourselfsoftware is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues.

Mitochondrial fatty acid synthesis coordinates oxidative metabolism in mammalian mitochondria

“…Heatmaps were generated by calculating the average value for the GFP replicates and dividing each replicate (experimental and control) by this value for each protein. A list of ETC component proteins was passed into the xpressplot.heatmap function from XPRESSplot ( Berg et al, 2020 ; Waskom et al, 2018 ) and further formatting was performed using Matplotlib ( Hunter, 2007 ). The volcano plot for the Mecr vs GFP proteomics data was generated by providing lists of Mitocarta ( Calvo et al, 2016 ; Pagliarini et al, 2008 ) and ETC proteins and the technical-replicate, geometric-normalized dataframe for both GFP and Mecr replicates to the xpressplot.volcano function ( Berg et al, 2020 ).…”

Cysteine Toxicity Drives Age-Related Mitochondrial Decline by Altering Iron Homeostasis

“…RNA-seq read counts were RPKM normalized. Heatmaps were generated by gene-standardizing the RPKM values (mean = 0, stdev = 1) and plotting using XPRESSplot v0.0.4-beta (https://doi.org/10.1101/704320 Berg et al, 2019) and Matplotlib (Hunter, 2007). Scripts used to perform these analyses can be found at https://github.com/j-berg/hughes_rnaseq_2019.…”

Msp1/ATAD1 restores mitochondrial function in Zellweger Spectrum Disease

“…Common samples between experiments were not combined. Data for heatmaps were z-score normalized across each protein and plots were generated using the xpressplot.heatmap() function from XPRESSplot [58], [59]) and further formatting was performed using Matplotlib [60]. Volcano plots were generated using the geometric-normalized datasets with no further normalization.…”

“…xpressplot.volcano() function [58], [59] and further formatting was performed using Matplotlib [60].…”