Fondufe-Mittendorf Breast Cancer PARP1 Correlation Analysis Package

This package contains functions and vignettes used in generating correlation results for the publication:

Genome-wide profiling of PARP1 reveals an interplay with gene regulatory regions and DNA methylation, Nalabothula Narasimharao, Taha Al-jumaily, Robert M. Flight, Shao Xiaorong, Hunter N. B. Moseley, F. Lisa Barcellos, Yvonne Fondufe-Mittendorf, Submitted

Contents

• Functions used in the analysis
• Vignettes providing the full calculations of the correlation of nucleosome associated PARP1 reads in the MCF-7 and MDA-MB123 cell lines with
  • CTCF ChIP-Seq
  • Methylation reads
  • Histone Marks ChIP-Seq
  • Transcript Expression

In addition, the correlations are stored in plain text files available here. These values were reported in the paper.

Citation

If this package is used for other analyses, it should be cited as:

fmcorrelationbreastcaparp1: Functions for calculating the correlations in PARP1 nucleosome data in breast cancer cell lines, R. M. Flight, Y. Fondufe-Mittendorf, H. N. B. Moseley doi:10.6084/m9.figshare.1267544

Installation

To install this package, you should use devtools:

library("devtools")
install_github("rmflight/fmcorrelationbreastcaparp1")

Recreating Vignettes

If you want to completely repeat the analysis in the vignettes, you will also need to install the data package, and you will want to clone this package locally and run devtools::build_vignettes.

On the command line, first install the data package, and clone this analysis package

# clone the packages
git clone https://github.com/rmflight/fmdatabreastcaparp1.git
git clone https://github.com/rmflight/fmcorrelationbreastcaparp1.git

# create the data directory
mkdir fmdatabreastcaparp1/data

# pull down the data
wget http://downloads.figshare.com/article/public/1266451.zip
unzip 1266451.zip -d fmdatabreastcaparp1/data

# start R and use devtools to install the packages
R
library(devtools)
install("fmdatabreastcaparp1")
install("fmcorrelationbreastcaparp1")

Then in R, use devtools to re-build the vignettes:

# assumes you started R in the cloned directory
devtools::build_vignettes()

This will generate the correlation files as well, hopefully they will give the same results as are already stored.

Memory

Re-generating the vignettes is a memory hog. Tracking memory usage during build_vignettes resulted in ~16GB used at peak areas. Keep that in mind if you want to redo the vignettes.