MRA.TA - Multi-Resolution Analysis of Tiling Arrays

Overview

MRA.TA is an R package for multi-resolution representation and segmentation of genomic profiles from tiling arrays.

This package was originally developed to detect enriched regions of arbitrary size from Chromosome Conformation Capture on Chip (4C) data, the microarray version of 4C employed in early genomic studies of chromosome conformations, as well as from Chromatin Immuno-Precipitation on chip (ChIP-on-chip) data, the precursor of the ChIP-seq technique.

More precisely, MRA.TA provides a set of normalization, assignment of probes to restriction fragments and probe filtering functions dedicated to 4C data¹, as well as multi-resolution analysis methods that are relevant for both 4C and ChIP-on-chip profiles.

Example

The example above shows a multi-resolution analysis performed with MRA.TA, including the domaingram representation² (panel Piw) and the corresponding multi-resolution segmentation (panel segmentation), from simulated data (bottom panel, M). Segmentations computed with MRA.TA are based on an algorithm tracing locally optimal enrichment statistics (top panel Fi). This algorithm produces raw segmentation trees which are then refined and simplified using rules of internal consistency (automated post-processing).

For further details and examples based on real 4C data see Leblanc et al. 2016 and the associated MiMB.4C workflow.

Package installation

Installation from github

Run the R code below to install MRA.TA.

library("devtools")
install_github("benja0x40/MRA.TA")

If the installation fails, try to install dependencies manually as follows.

Dependencies

• R environment version 3.x
• CRAN packages devtools, stringr, getopt, plotrix
• Bioconductor packages Biostrings, GenomicRanges

Run the R code below to install CRAN and Bioconductor package dependencies for MRA.TA.

# Already installed
pkg <- installed.packages()[, "Package"]

# CRAN packages
lst <- c("devtools", "stringr", "getopt", "plotrix")
lst <- setdiff(lst, pkg)
if(length(lst) > 0) install.packages(lst, repos = "https://cloud.r-project.org/")

# Bioconductor packages
lst <- c("Biostrings", "GenomicRanges")
lst <- setdiff(lst, pkg)
if(length(lst) > 0) {
  source("https://bioconductor.org/biocLite.R")
  biocLite(lst)
}

Acknowledgements

Thanks to Elzo de Wit for kindly sharing his source code and suggestions on the multi-resolution methods, and to Bas Tolhuis who greatly helped with Nimblegen tiling array data analyses, also sharing source code as well as unpublished biological data. Thanks to Jean-Philippe Villemin for testing the installation and execution of the associated MiMB.4C workflow and reporting issues and suggestions.

References

1. Leblanc B., Comet I., Bantignies F., and Cavalli G., Chromosome Conformation Capture on Chip (4C): data processing. Book chapter in Polycomb Group Proteins: Methods and Protocols. Lanzuolo C., Bodega B. editors, Methods in Molecular Biology (2016).
publisher | pubmed

2. de Wit E., Braunschweig U., Greil F., Bussemaker H. J. & van Steensel B. Global chromatin domain organization of the Drosophila genome. PLoS Genetics (2008).
publisher | pubmed