R-notebook-ralstonia-proteome

Pipelines for data analysis and visualization of the Ralstonia proteome

Overview

This repository contains data processing pipelines for the analysis of the litho-autotrophic bacterium Ralstonia eutropha, also known as Cupriavidus necator. The repository contains raw data, processed data and R notebooks (*.Rmd) for three types of data:

• mass spectrometry proteomics
• genome scale model simulations (FBA/RBA)
• barcoded transposon knockout library

All care was taken to guarantee scientific accuracy and adhere to good scientific practice in terms of statistics, reproducibility and code documentation. Please report any errors by filing a github issue for this repository, or contact michael.jahn@scilifelab.se.

How to run the pipelines

Data and pipelines collected in this repository are (to the most extent) self-contained and executable. The code and the documentation are part of one and the same R markdown document for each pipeline. The pipelines themselves can be downloaded and executed from the pipeline sub-folder. To simply view the rendered pipelines follow the links to the *.html reports under Contents.

To download the repository on your local drive use git clone in a (linux) terminal:

cd /your-target-folder
git clone https://github.com/m-jahn/R-notebook-ralstonia-proteome

Open a pipeline with Rstudio and execute code (chunks) with the Run button. Alternatively, open an interactive R session and render the R markdown pipeline:

require(rmarkdown)
rmarkdown::render("pipeline.Rmd")

Contents

• Ralstonia eutropha cultivations and physiological data, obtained from substrate limited chemostats
• Ralstonia eutropha processing of MS proteomics data
• Ralstonia eutropha model constraints, determination of k_app and protein mass per compartment
• Ralstonia eutropha RBA model, variability analysis based on random sampling of k_app
• Ralstonia eutropha enzyme abundance and utilization by comparison of measured and RBA model predicted enzyme abundance
• Ralstonia eutropha gene essentiality analysis (TnSeq), based on transposon insertion frequency
• Ralstonia eutropha condition dependent fitness (BarSeq), based on transposon mutant depletion/enrichment over time