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.

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Subsetting is commonly used in ggtree as we would like to for example separating internal nodes from tips. We may also want to display annotation to specific node(s)/tip(s).

Some software may stored clade information (e.g. bootstrap value) as internal node labels. Indeed we want to manipulate such information and taxa labels separately.

In current ggplot2 (version=1.0.1, access date:2015-09-23), it support subset. For instance:

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ggtree applies the concepts of grammar of graphic in phylogenetic tree presentation and make it easy to add multiple layers of text and even figures above a 🌲.

Here, I cartoonize a phylogenetic tree generated by ggtree with comicR, which is a funny package to generate comic (xkcd-like) graph in R. Have fun with ggtree and comicR.

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I implemented a function, subview, in ggtree that make it easy to embed a subplot in ggplot.

An example is shown below:

library(ggplot2)
library(ggtree)

dd <- data.frame(x=LETTERS[1:3], y=1:3)
pie <- ggplot(dd, aes(x=1, y, fill=x)) + 
	geom_bar(stat="identity", width=1) + 
	coord_polar(theta="y") + theme_tree() + 
	xlab(NULL) + ylab(NULL) + 
	theme_transparent()

x <- sample(2:9)
y <- sample(2:9)
width <- sample(seq(0.05, 0.15, length.out=length(x)))
height <- width

p <- ggplot(data=data.frame(x=c(0, 10), y=c(0, 10)), aes(x, y))+geom_blank()
print(p)
for (i in seq_along(x)) {
	p %<>% subview(pie, x[i], y[i], width[i], height[i])
	print(p)
}

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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.

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The ENCODE consortium has recently published a great paper on Gene Expression from the GTEx dataset. A criticism raised on pubpeer is that the gene ontology enrichment analysis was done with DAVID which has not been updated in the last five years.

The result is shown below:

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In supplemental file of ChIPseeker paper, I compare distances to TSS reported by several ChIP annotation software, including ChIPseeker, ChIPpeakAnno, HOMER and PeakAnalyzer.

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.

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