nucleR-package {nucleR} | R Documentation

## Nucleosome positioning package for R

### Description

Nucleosome positioning from Tiling Arrays and High-Troughput Sequencing Experiments

### Details

Package: | nucleR
Type: | Package
License: | LGPL (>= 3)
LazyLoad: | yes

This package provides a convenient pipeline to process and analize nucleosome positioning experiments from High-Troughtput Sequencing or Tiling Arrays.

Despite it's use is intended to nucleosome experiments, it can be also useful for general ChIP experiments, such as ChIP-on-ChIP or ChIP-Seq.

See following example for a brief introduction to the available functions

### Author(s)

Oscar Flores Ricard Illa

Maintainer: Ricard Illa ricard.illa@irbbarcelona.org

### Examples

    library(ggplot2)
    # Load example dataset:
    # some NGS paired-end reads, mapped with Bowtie and processed with R
    # it is a GRanges object with the start/end coordinates for each read.
    reads <- get(data(nucleosome_htseq))

    # Process the paired end reads, but discard those with length > 200
    preads_orig <- processReads(reads, type="paired", fragmentLen=200)

    # Process the reads, but now trim each read to 40bp around the dyad
    preads_trim <- processReads(reads, type="paired", fragmentLen=200, trim=40)

    # Calculate the coverage, directly in reads per million (r.p.m.)
    cover_orig <- coverage.rpm(preads_orig)
    cover_trim <- coverage.rpm(preads_trim)

    # Compare both coverages, the dyad is much more clear in trimmed version
    t1 <- as.vector(cover_orig[[1]])[1:2000]
    t2 <- as.vector(cover_trim[[1]])[1:2000]
    t1 <- (t1-min(t1)) / max(t1-min(t1))  # Normalization
    t2 <- (t2-min(t2)) / max(t2-min(t2))  # Normalization

    plot_data <- rbind(
        data.frame(
            x=seq_along(t1),
            y=t1,
            coverage="original"
        ),
        data.frame(
            x=seq_along(t2),
            y=t2,
            coverage="trimmed"
        )
    )
    qplot(x=x, y=y, color=coverage, data=plot_data, geom="line",
      xlab="position", ylab="coverage")

    # Let's try to call nucleosomes from the trimmed version
    # First of all, let's remove some noise with FFT
    # Power spectrum will be plotted, look how with a 2%
    # of the components we capture almost all the signal
    cover_clean <- filterFFT(cover_trim, pcKeepComp=0.02, showPowerSpec=TRUE)

    # How clean is it now?
    plot_data <- rbind(
        data.frame(
            x=1:4000,
            y=as.vector(cover_trim[[1]])[1:4000],
            coverage="noisy"
        ),
        data.frame(
            x=1:4000,
            y=as.vector(cover_clean[[1]])[1:4000],
            coverage="filtered"
        )
    )
    qplot(x=x, y=y, color=coverage, data=plot_data, geom="line",
      xlab="position", ylab="coverage")

    # And how similar? Let's see the correlation
    cor(cover_clean[[1]], as.vector(cover_trim[[1]]))

    # Now it's time to call for peaks, first just as points
    # See that the score is only a measure of the height of the peak
    peaks <- peakDetection(cover_clean, threshold="25%", score=TRUE)
    plotPeaks(peaks[[1]], cover_clean[[1]], threshold="25%")

    # Do the same as previously, but now we will create the nucleosome calls:
    peaks <- peakDetection(cover_clean, width=147, threshold="25%", score=TRUE)
    plotPeaks(peaks, cover_clean[[1]], threshold="25%")

    #This is all. From here, you can filter, merge or work with the nucleosome
    #calls using standard IRanges functions and R/Bioconductor manipulation

[Package nucleR version 2.16.0 Index]