R-notebook-ralstonia-energy

Pipelines for data analysis and visualization of the Ralstonia energy metabolism

Overview

This repository contains pipelines for the analysis of the energy metabolism of Ralstonia eutropha, also known as Cupriavidus necator, a versatile litho-autotrophic bacterium. The repository contains next generation sequencing data obtained from a barcoded transposon library. Data processing visualization is documented in R notebooks (*.Rmd).

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 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 Pipelines.

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-energy

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")

Data

• data/barseq/20201222_fru_fitness.Rdata, competition experiments on fructose
• data/barseq/20210407_suc_for_fitness.Rdata, competition experiments on formate and succinate
• data/barseq/20210624_H2_NO3_fitness.Rdata, competition experiments on hydrogen and nitrate (anoxic growth)
• data/ref/Ralstonia_H16_genome_annotation.csv, table with extensive genome annotation, from uniprot.org

Libraries

• lattice
• latticeExtra
• latticetools from github
• tidyverse (metapackage)
• dendextend
• colorspace
• stringi

Pipelines

• Exploring energy metabolism in R. eutropha using a barcoded transposon library. The library was cultivated in bioreactors with different substrate limitations. The depletion/enrichment of transposon mutants over time was tracked using next generation sequencing. This data was used to estimate the fitness contribution of each gene in each condition.