To answer the issue, I extend the
covplot function to support viewing coverage of a list of
GRanges objects or
library(ChIPseeker) files <- getSampleFiles() peak=GenomicRanges::GRangesList(CBX6=readPeakFile(files[]), CBX7=readPeakFile(files[])) p <- covplot(peak) print(p)
ChIP-seq is rapidly becoming a common technique and there are a large number of dataset available in the public domain. Results from individual experiments provide a limited understanding of chromatin interactions, as there is many factors cooperate to regulate transcription. Unlike other tools that designed for single dataset, ChIPseeker is designed for comparing profiles of ChIP-seq datasets at different levels.
We provide functions to compare profiles of peaks binding to TSS regions, annotation, and enriched functional profiles. More importantly, ChIPseeker incorporates statistical testing of co-occurrence of different ChIP-seq datasets and can be used to identify co-factors.
I found a Bioconductor package, seq2pathway, that can apply functional analysis to NGS data. It consists of two components, seq2gene and gene2pathway. seq2gene converts genomic coordination to genes while gene2pathway performs functional analysis at gene level.
I think it would be interesting to incorporate seq2gene with clusterProfiler. But it fail to run due to it call absolute path of python installed in the author’s computer.
Although I found that the chromStart positions in HOMER output have a +1 shift compare to other software, I did not realize this issue since all other software are consistent.
ChIPseeker had been cited by http://www.biomedcentral.com/1471-2164/16/292 and http://www.jbc.org/content/early/2015/06/18/jbc.M115.668558.short, and was used (not cited) in http://nar.oxfordjournals.org/content/early/2015/06/27/nar.gkv642.abstract and http://emboj.embopress.org/content/early/2014/12/18/embj.201490061.abstract.