compare ChIP-seq data for different conditions and different transcription factors?

I have done a lot of ChIP-seq data analysis, but most of them are just mapping to reference genome and then calling peaks (use MACS from Shirley liu's lab at Harvard). A little bit more is to intersect different peak bed files with bedtools and to make heatmaps.

The reads mapping is much simpler and faster than RNA-seq data. The data are smaller (~5Gb compare to RNA-seq ~25Gb). I recently asked myself how to compare different ChIP-seq data for different conditions ( control vs knockdown, different developmental stages etc).

I quick google search:
http://seqanswers.com/forums/showthread.php?t=31048&highlight=compare+ChIP-seq
http://www.biostars.org/p/10609/
http://www.biostars.org/p/42291/

The available packages are:
"- ChIPDiff, which you already mentioned but which I haven't tried, but it was actually developed for histone marks so I'd be surprised if it didn't work for those?! I recall someone saying that there was some other issue with it (sorry I can't be more specific)

- DiffBind, which you also mentioned (http://www.bioconductor.org/packages...iffBind.html); it uses DESeq internally

- DBChIP (http://pages.cs.wisc.edu/~kliang/DBChIP/), which appears to use edgeR

- You can also use edgeR and DESeq directly. The DESeq paper shows you how to re-analyze differential TF binding data in the Kasowski et al Science paper (http://www.sciencemag.org/content/328/5975/232.short). "

After reading a little bit  http://seqanswers.com/forums/showthread.php?t=31048&highlight=compare+ChIP-seq, I decided to give diffReps http://code.google.com/p/diffreps/ a try.  The author says it is suitable for both histone modifications and transcription factors.
The paper was published in PLOSONE, it compares the results  with  that from DESeq and others.
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0065598
The author Shen Li developed the other package ngsplot.

Another interesting R package is here http://genomebiology.com/2013/14/4/R38
mentioned by me before http://crazyhottommy.blogspot.com/2013/07/jmosaics-joint-analysis-of-multiple.html
I will have to give it a try also.

At the same time, I am reading the Nature protocol:
http://www.nature.com/nprot/journal/v7/n1/full/nprot.2011.420.html

Nat Protoc. 2011 Dec 15;7(1):45-61. doi: 10.1038/nprot.2011.420.

A computational pipeline for comparative ChIP-seq analyses.

it gives you a much better understanding of how to make the comparisons.

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update 11/19/13


A table from the newly published paper

Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data

http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003326

Table S3.  Software packages for the analysis of differential binding in ChIP-seq.

Software tool	Availability	Notes
ChIPDiff [36]	http://cmb.gis.a-star.edu.sg/ChIPSeq/paperChIPDiff.htm	Differential histone modification sites using a hidden Markov model
Comparative ChIP-seq [25]	http://www.starklab.org/data/bardet_natprotoc_2011/	Fold change ratio between normalized peak heights
DBChIP [33]	http://pages.cs.wisc.edu/~kliang/DBChIP/	Assigns uncertainty measures in a test of non-differential binding (uses edgeR)
DESeq§ [31]	http://www.bioconductor.org/packages/release/bioc/html/DESeq.html	Test based on a model using the negative binomial distribution
DiffBind	http://www.bioconductor.org/packages/release/bioc/html/DiffBind.html	Differential binding affinity analysis (uses edgeR and DESeq)
DIME [35]	http://cran.r-project.org/web/packages/DIME/	Differential identification using mixtures ensemble
edgeR§ [32]	http://www.bioconductor.org/packages/release/bioc/html/edgeR.html	Empirical Bayes estimation and exact tests based on the negative binomial distribution
MACS [17] (version 2)	https://github.com/taoliu/MACS/	Differential peak detection based on paired four bedGraph files
MAnorm [34]	http://bcb.dfci.harvard.edu/~gcyuan/MAnorm/MAnorm.htm	Robust regression to derive a linear model
MMDiff	http://bioconductor.org/packages/release/bioc/html/MMDiff.html	Differences in shape using Kernel methods
NarrowPeaks	http://bioconductor.org/packages/release/bioc/html/NarrowPeaks.html	Shape-based analysis of variation using functional PCA
POLYPHEMUS [37]	http://cran.r-project.org/web/packages/polyphemus/	Non-linear normalization on RNA Pol II profiling

^§ Originally developed for gene expression count data.

=====================
updated on 02/06/2014

Two more useful links:
http://www.r-bloggers.com/methods-of-calling-differential-region-of-chip-seq/
http://omictools.com/differential-peak-calling/