Nucleoli serve as major organizing hubs for the three-dimensional structure of mammalian heterochromatin1. Specific loci, termed nucleolar-associated domains (NADs), form frequent three-dimensional associations with nucleoli.2 Early mammalian development is a critical period to study NAD biological function, because interactions between pericentromeric chromatin and perinucleolar regions are particularly dynamic during embryonic development.4 Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including normalization, smoothing, peak calling, peak trimming and annotation. We demonstrate the application of NADfinder in mapping the NADs in the mouse genome, determining how these associations are altered during embryonic stem cell (ESC) differentiation, and develop tools for study of these higher-order chromosome interactions in fixed and live single cells (Fig. 1).
Here is an example to use NADfinder for peak calling.
Here is the code snippet for calculating coverage with a sliding window in a given step along the genome using a pair of bam files from genomic sample as background and purified nucleoleus-associated DNA as target signal.
## load the library library(NADfinder) library(SummarizedExperiment) ## test bam files in the package path <- system.file("extdata", package = "NADfinder", lib.loc = NULL, mustWork = TRUE) bamFiles <- dir(path, ".bam$") ## window size for tile of genome. Here we set it to a big window size, ## ie., 50k because of the following two reasons: ## 1. peaks of NADs are wide; ## 2. data will be smoothed better with bigger window size. ws <- 50000 ## step for tile of genome step <- 5000 ## Set the background. ## 0.25 means 25% of the lower ratios will be used for background training. backgroundPercentage <- 0.25 ## Count the reads for each window with a given step in the genome. ## The output will be a GRanges object. library(BSgenome.Mmusculus.UCSC.mm10)
se <- tileCount(reads=file.path(path, bamFiles), genome=Mmusculus, windowSize=ws, step=step, mode = IntersectionNotStrict, dataOverSamples = FALSE)
Here we load the pre-computed coverage data single.count to save build time.
data(single.count) se <- single.count
For quality asessment,
cumulativePercentage can be used to plot the
cumulative sums of sorted read counts for nucleolus-associated DNA and
whole genomic DNA. We expect the cumulative sum in the genomic DNA to
be close to a straight line because the coverage for the genomic DNA
sample should be uniformly distributed. Unlike ChIP-seq data, the
cumulative sum for the nucleoleus sample will not exhibit sharp changes
because most of NADs are broad regions as wide as 100 kb. However, we
should observe clear differences between the two curves.
## Calculate ratios for peak calling. We use nucleoleus vs genomic DNA. dat <- log2se(se, nucleolusCols = "N18.subsampled.srt.bam", genomeCols ="G18.subsampled.srt.bam", transformation = "log2CPMRatio") ## Smooth the ratios for each chromosome. ## We found that for each chromosome, the ratios are higher in 5'end than 3'end. datList <- smoothRatiosByChromosome(dat, chr = c("chr18", "chr19"), N = 100) ## check the difference between the cumulative percentage tag allocation ## in genome and nucleoleus samples. cumulativePercentage(datList[["chr18"]])
Before peak calling, the function
smoothRatiosByChromosome is used for
log ratios calculation, normalization and smoothing.
The peaks will be called if the ratios are significantly higher than
chromosome-specific background determined by
The following figure shows the peaks (black rectangles) called
using normalized (green curve) and smoothed (red curve) log2 ratios.
## check the results of smooth function chr18 <- datList[["chr18"]] ## we only have reads in chr18 in test data. chr18subset <- subset(chr18, seq.int(floor(length(chr18)/10))*10) chr18 <- assays(chr18subset) ylim <- range(c(chr18$ratio[, 1], chr18$bcRatio[, 1], chr18$smoothedRatio[, 1])) plot(chr18$ratio[, 1], ylim=ylim*c(.9, 1.1), type="l", main="chr18") abline(h=0, col="cyan", lty=2) points(chr18$bcRatio[, 1], type="l", col="green") points(chr18$smoothedRatio[, 1], type="l", col="red") legend("topright", legend = c("raw_ratio", "background_corrected", "smoothed"), fill = c("black", "green", "red")) ## call peaks for each chromosome peaks <- lapply(datList, trimPeaks, backgroundPercentage=backgroundPercentage, cutoffAdjPvalue=0.05, countFilter=1000) ## plot the peaks in "chr18" peaks.subset <- countOverlaps(rowRanges(chr18subset), peaks$chr18)>=1 peaks.run <- rle(peaks.subset) run <- cumsum(peaks.run$lengths) run <- data.frame(x0=c(1, run[-length(run)]), x1=run) #run <- run[peaks.run$values, , drop=FALSE] rect(xleft = run$x0, ybottom = ylim*.75, xright = run$x1, ytop = ylim*.8, col = "black")