Abstract
Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is invaluable for identifying genome-wide binding of transcription factors and mapping of epigenomic profiles. We present a statistical protocol for analyzing ChIP-seq data. We describe guidelines for data preprocessing and quality control and provide detailed examples of identifying ChIP-enriched regions using the Bioconductor package “mosaics.”
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Acknowledgments
This work is supported by National Institutes of Health Grants (HG0067161, HG003747) to S.K. We thank Audrey Gasch and Jeff Lewis (yeast TFx), John Svaren and Rajini Srinivasan (Sox10 in rat), and Qiang Chang and Emily Cunningham (human ChIP-seq) for the datasets and useful discussions regarding the analysis.
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Appendix: R Script for the Analysis of Yeast TFx ChIP-seq Datasets
Appendix: R Script for the Analysis of Yeast TFx ChIP-seq Datasets
library( mosaics)
library( hexbin)
# construct bin-level files for each replicate ChIP sample #
constructBins(infile = "TFx_EtOH_IP_1_2mis_bowtie_uni.txt", outfileLoc = "/bin_cap0",
fileFormat = "bowtie", byChr = FALSE, fragLen = 200, binSize = 200, capping = 0, PET = FALSE)
constructBins(infile = "TFx_EtOH_IP_2_2mis_bowtie_uni.txt", outfileLoc = "/bin_cap0",
fileFormat = "bowtie", byChr = FALSE, fragLen = 200, binSize = 200, capping = 0, PET = FALSE)
constructBins( infile = "TFx_EtOH_IP_3_2mis_bowtie_uni.txt", outfileLoc = "/bin_cap0",
fileFormat = "bowtie", byChr = FALSE, fragLen = 200, binSize = 200, capping = 0, PET = FALSE)
# construct bin-level files for pooled ChIP (uncapped and capped by 3) and pooled input samples #
constructBins( infile = "TFx_EtOH_input_2mis_bowtie_uni.txt", outfileLoc = "/bin_cap0",
fileFormat = "bowtie", byChr = FALSE, fragLen = 200, binSize = 200, capping = 0, PET = FALSE)
constructBins( infile = "TFx_EtOH_input_2mis_bowtie_uni.txt", outfileLoc = "/bin_cap3",
fileFormat = "bowtie", byChr = FALSE, fragLen = 200, binSize = 200, capping = 3, PET = FALSE)
constructBins( infile = "TFx_EtOH_chip_2mis_bowtie_uni.txt", outfileLoc = "/bin_cap0",
fileFormat = "bowtie", byChr = FALSE, fragLen = 200, binSize = 200, capping = 0, PET = FALSE)
# generate hexbin plots between replicates to decide on pooling #
bin_rep = vector( “list”,3)
bin_rep[[1]] = readBins(type = c("chip","input"), fileName = c( "/bin_cap0/TFx_EtOH_IP_1_2mis_bowtie_uni.txt_fragL200_bin200.txt"," /bin_cap0/TFx_EtOH_IP_2_2mis_bowtie_uni.txt_fragL200_bin200.txt"))
bin_rep[[2]] = readBins(type = c("chip","input"), fileName = c( "/bin_cap0/TFx_EtOH_IP_1_2mis_bowtie_uni.txt_fragL200_bin200.txt","/bin_cap0/TFx_EtOH_IP_3_2mis_bowtie_uni.txt_fragL200_bin200.txt"))
bin_rep[[3]] = readBins(type = c("chip","input"), fileName = c( "/bin_cap0/TFx_EtOH_IP_2_2mis_bowtie_uni.txt_fragL200_bin200.txt"," /bin_cap0/TFx_EtOH_IP_3_2mis_bowtie_uni.txt_fragL200_bin200.txt"))
xlabel = c( “rep1”, ”rep1”, ”rep2”); ylabel = c( “rep2”, ”rep3”, ”rep3”)
for (i in 1:3) {
a = hexbin( bin_rep[[i]]@tagCount, bin_rep[[i]]@input, xbins = 100)
plot( a, trans = log, inv = exp, xlab = xlabel[i], ylab = ylabel[i], colramp = rainbow)
}
# generate hexbin plots of uncapped and capped bin-level read count data #
bin_cap = readBins(type = c("chip","input"), filename = c( "/bin_cap0/TFx_EtOH_IP_2mis_bowtie_uni.txt_fragL200_bin200.txt", "/bin_cap3/TFx_EtOH_IP_2mis_bowtie_uni.txt_fragL200_bin200.txt"))
a = hexbin (bin_cap@ input, bin_cap@ tagCount, xbins = 100)
plot(a, trans = log, inv = exp, xlab = "Capped counts", ylab = "Uncapped counts", colramp = rainbow)
# load bin-level files for MOSAiCS analysis #
bin = readBins(type = c("chip","input"), filename = c("/bin_cap0/TFx_EtOH_IP_2mis_bowtie_uni.txt_fragL200_bin200.txt", "/bin_cap0/TFx_EtOH_input_2mis_bowtie_uni.txt_fragL200_bin200.txt"))
# generate hexbin plot to check ChIP enrichment over Input#
a = hexbin (bin@input, bin@tagCount, xbins = 100)
plot(a, trans = log, inv = exp, xlab = "Input", ylab = "ChIP", colramp = rainbow)
# histogram of bin-level files #
plot(bin)
# use Input-only model to fit data #
fit = mosaicsFit(bin, analysisType = "IO", bgEst = "automatic", truncProb = 0.08)
# goodness-of-fit plot of the model #
plot(fit)
# call peaks #
thres = quantile (fit@tagCount, probs = 0.95)
peak = mosaicsPeak(fit, signalModel = "2S", FDR = 0.05, maxgap = 200, minsize = 50, thres = thres)
# export peaks #
export(peak, type = "txt", filename = "TFx_TSpeakList.txt")
export(peak, type = "bed", filename = "TFx_TSpeakList.bed")
# generate wig files #
generateWig(infile = "TFx_EtOH_IP_2mis_bowtie_uni.txt ", PET = FALSE, fileFormat = "bowtie", outfileLoc = "/bin_cap0/", byChr = FALSE, useChrfile = FALSE, chrfile = NULL, fragLen = 200, span = 200, capping = 0)
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Sun, G., Chung, D., Liang, K., Keleş, S. (2013). Statistical Analysis of ChIP-seq Data with MOSAiCS. In: Shomron, N. (eds) Deep Sequencing Data Analysis. Methods in Molecular Biology, vol 1038. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-514-9_12
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DOI: https://doi.org/10.1007/978-1-62703-514-9_12
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