Analyzing ChIP-chip Data Using Bioconductor

Analyzing ChIP-chip Data Using Bioconductor

The interpretation of ChIP-chip data poses two computational challenges: first, what can be termed primary statistical analysis, which includes quality assessment, data normalization and transformation, and the calling of regions of interest; second, integrative bioinformatic analysis, which interprets the data in the context of existing genome annotation and of related experimental results obt...

Supplement: Analyzing ChIP-chip data using Bioconductor

rMAT - an R/Bioconductor package for analyzing ChIP-chip experiments

SUMMARY Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) has evolved as a popular technique to study DNA-protein binding or post-translational chromatin/histone modifications at the genomic level. However, the raw microarray intensities generate a massive amount of data, creating a need for efficient analysis algorithms and statistical methods to identify enriched regions...

Basic Analysis of NimbleGen ChIP-on-chip Data using Bioconductor/R (PROT43)

Hybridization of chromatin immuno-precipitation (ChIP) material to tiling arrays at NimbleGen service facilities usually leaves the customer with a set of data files that are of limited use. Most information about the experiment is gained by either displaying immunoprecipitate(IP)/input ratio tracks (the GFF files provided) of individual hybridisation experiments with NimbleGen?s SignalMap soft...

A ChIP-Seq Data Analysis Pipeline Based on Bioconductor Packages

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPse...