| Title: | Nucleosome positioning package for R |
|---|---|
| Description: | Nucleosome positioning for Tiling Arrays and NGS experiments. |
| Authors: | Oscar Flores, Ricard Illa |
| Maintainer: | Alba Sala <[email protected]> |
| License: | LGPL (>= 3) |
| Version: | 2.37.0 |
| Built: | 2024-07-17 12:39:12 UTC |
| Source: | https://github.com/bioc/nucleR |
Nucleosome positioning from Tiling Arrays and High-Troughput Sequencing Experiments
| 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
Oscar Flores Ricard Illa
Maintainer: Ricard Illa [email protected]
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 manipulationlibrary(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
Define FFT by regions to avoid a large amount of memory use and a drop in performance
.fftRegion(data2, pcKeepComp).fftRegion(data2, pcKeepComp)
If threshold is given as a string with percentage, convert it
.getThreshold(threshold, data).getThreshold(threshold, data)
threshold |
threshold given as an absolute value or as a string percentage |
data |
vector with values from which to derive the threshold if it's relative |
a numeric vector
single or paired and with the number of input
filesFile loader
Higher order function to import BAM or Bowtie files.
Deals with wether type is single or paired and with the number of input
files
.loadFiles(singleLoad, pairedLoad).loadFiles(singleLoad, pairedLoad)
Load a paired-end-end BAM
.loadPairedBam(file).loadPairedBam(file)
Load a single-end BAM
.loadSingleBam(exp).loadSingleBam(exp)
Simple function for returning the middle point of a of a GRanges (normal mid doesn't work there)
.mid(x).mid(x)
Process a given strand from a BAM file to read
.processStrand(strand, bam).processStrand(strand, bam)
strand |
either strand "+" or "-" |
bam |
|
GenomicRanges::GRanges object representing the reads in a given strand
Wrapper to internal function from the IRanges package. Avoids use of
::: and thus prevents a NOTE warning about the use of a non-exported
function
.unlist_as_integer(x).unlist_as_integer(x)
H. Pages, P. Aboyoun and M. Lawrence
all
Helper function that behaves as a vectorized version of the function all
.vectorizedAll(...).vectorizedAll(...)
... |
arbitraty amount of |
logical vector
Wrapper to choose between lapply and mclapply accordingly
.xlapply(X, FUN, ..., mc.cores = 1).xlapply(X, FUN, ..., mc.cores = 1)
This function allows the correction of experimental coverage profiles (usually MNase digested nucleosomal DNAs in this library) with control samples (usually naked DNA sample digested with MNase). This is useful to correct MNase biase.
controlCorrection(exp, ctr, ...) ## S4 method for signature 'SimpleRleList' controlCorrection(exp, ctr, mc.cores = 1) ## S4 method for signature 'Rle' controlCorrection(exp, ctr) ## S4 method for signature 'list' controlCorrection(exp, ctr, mc.cores = 1) ## S4 method for signature 'numeric' controlCorrection(exp, ctr)controlCorrection(exp, ctr, ...) ## S4 method for signature 'SimpleRleList' controlCorrection(exp, ctr, mc.cores = 1) ## S4 method for signature 'Rle' controlCorrection(exp, ctr) ## S4 method for signature 'list' controlCorrection(exp, ctr, mc.cores = 1) ## S4 method for signature 'numeric' controlCorrection(exp, ctr)
exp, ctr
|
Comparable experimental and control samples (this means same format and equivalent preprocessment) |
... |
Further arguments to be passed to or from other methods. |
mc.cores |
Number of cores available for parallel list processing |
This substracts the enrichment in the control sample respect it's mean from the experimental profile.
This is useful for examinating the effect of the MNase digestion in nucleosome experiments using a nucleosomal DNA and a genomic (naked) DNA sample. Notice that genomic DNA samples cannot be strand-corrected using single end data, so only paired end controls are useful for this proupose, despite they can be compared against extended nucleosomal DNA single end reads. Furthermore, both datasets must be converted to reads per milion.
This process dificults the nucleosome positioning due the lower sharpness of the peaks, but allows a complementary study of the MNase digestion effect.
Corrected experimental profile
Oscar Flores [email protected]
map = syntheticNucMap(as.ratio=TRUE) exp = coverage.rpm(map$syn.reads) ctr = coverage.rpm(map$ctr.reads) corrected = controlCorrection(exp, ctr)map = syntheticNucMap(as.ratio=TRUE) exp = coverage.rpm(map$syn.reads) ctr = coverage.rpm(map$ctr.reads) corrected = controlCorrection(exp, ctr)
Calculates the coverage values from a GenomicRanges::GRanges or IRanges::IRanges object normalized to reads per million, allowing the comparison of experiments with a different absolut number of reads.
coverage.rpm(data, scale = 1e+06, ...) ## S4 method for signature 'GRanges' coverage.rpm(data, scale = 1e+06, ...) ## S4 method for signature 'CompressedGRangesList' coverage.rpm(data, scale = 1e+06, ...) ## S4 method for signature 'IRanges' coverage.rpm(data, scale = 1e+06, ...)coverage.rpm(data, scale = 1e+06, ...) ## S4 method for signature 'GRanges' coverage.rpm(data, scale = 1e+06, ...) ## S4 method for signature 'CompressedGRangesList' coverage.rpm(data, scale = 1e+06, ...) ## S4 method for signature 'IRanges' coverage.rpm(data, scale = 1e+06, ...)
data |
GenomicRanges::GenomicRanges or IRanges::IRanges with the reads information |
scale |
By default, a million (1e6), but you could change this value for abnormal high or low amount of reads. |
... |
Additional arguments to be passed to |
RleList object with the coverage objects
Oscar Flores [email protected]
processReads(), IRanges::coverage()
# Load the example dataset and get the coverage data(nucleosome_htseq) cov <- coverage.rpm(nucleosome_htseq) print(cov) # Plot it library(ggplot2) cover <- as.vector(cov[["chr1"]]) qplot(seq_along(cover), cover, geom="line", ylab="coverage", xlab="position")# Load the example dataset and get the coverage data(nucleosome_htseq) cov <- coverage.rpm(nucleosome_htseq) print(cov) # Plot it library(ggplot2) cover <- as.vector(cov[["chr1"]]) qplot(seq_along(cover), cover, geom="line", ylab="coverage", xlab="position")
Export ranges in BED format, compatible with UCSC genome browser, IGB, and others.
export.bed( ranges, score = NULL, chrom, name, desc = name, filepath = name, splitByChrom = TRUE ) ## S4 method for signature 'IRanges' export.bed(ranges, score = NULL, chrom, name, desc = name, filepath = name) ## S4 method for signature 'CompressedIRangesList' export.bed( ranges, score = NULL, name, desc = name, filepath = name, splitByChrom = TRUE ) ## S4 method for signature 'GRanges' export.bed( ranges, score = NULL, name, desc = name, filepath = name, splitByChrom = TRUE )export.bed( ranges, score = NULL, chrom, name, desc = name, filepath = name, splitByChrom = TRUE ) ## S4 method for signature 'IRanges' export.bed(ranges, score = NULL, chrom, name, desc = name, filepath = name) ## S4 method for signature 'CompressedIRangesList' export.bed( ranges, score = NULL, name, desc = name, filepath = name, splitByChrom = TRUE ) ## S4 method for signature 'GRanges' export.bed( ranges, score = NULL, name, desc = name, filepath = name, splitByChrom = TRUE )
ranges |
Ranges to export, in IRanges::IRanges, IRanges::IRangesList or GenomicRanges::GRanges format |
score |
Score data if not included in |
chrom |
For single |
name |
Name of the track |
desc |
Description of the track |
filepath |
Path and prefix of the file(s) to write. Chromosome number and "bed" extension will be automatically added. |
splitByChrom |
If multiple chromosomes are given, should they be splitted into one file per chromosome or shall them be saved all together? |
(none)
Oscar Flores [email protected]
BED format specification: http://genome.ucsc.edu/FAQ/FAQformat#format1
# Generate some ranges with scores library(GenomicRanges) ran <- GRanges( seqnames="chrX", ranges=IRanges(start=1:100, end=101:200), score=(1:100)/100 ) # Export as bed file export.bed(ran, name="test_track", desc="Just a test track") # If executed, this would create a file named "test_track.chrX.bed" with: # track name="test_track" description="Just a test track" useScore=0 # chrX 1 101 nucl1 0.01 # chrX 2 102 nucl2 0.02 # chrX 3 103 nucl3 0.03 # ...# Generate some ranges with scores library(GenomicRanges) ran <- GRanges( seqnames="chrX", ranges=IRanges(start=1:100, end=101:200), score=(1:100)/100 ) # Export as bed file export.bed(ran, name="test_track", desc="Just a test track") # If executed, this would create a file named "test_track.chrX.bed" with: # track name="test_track" description="Just a test track" useScore=0 # chrX 1 101 nucl1 0.01 # chrX 2 102 nucl2 0.02 # chrX 3 103 nucl3 0.03 # ...
Export coverage/intensity values in WIG format, compatible with UCSC genome browser, IGB and others.
export.wig(data, name, chrom = "", filepath = name)export.wig(data, name, chrom = "", filepath = name)
data |
Coverage/intensity values (numeric vector) |
name |
Name of the track |
chrom |
Information about chromosome if not inferrable from |
filepath |
Filepath where to save the object. Chromosome name and "wig" extension will be automatically added |
(none)
Oscar Flores [email protected]
WIG format specification: http://genome.ucsc.edu/FAQ/FAQformat#format6
# Load data data(nucleosome_htseq) cover <- coverage.rpm(nucleosome_htseq) # Create wig file export.wig(cover, name="example_track") # This would create the file "example_track.chr1.wig" with: # track type=wiggle_0 name="example_track" # fixedStep chrom=chr1 start=1 step=1 # 55.55247 # 55.55247 # 55.55247 # 277.7623 # 388.8673 # ...# Load data data(nucleosome_htseq) cover <- coverage.rpm(nucleosome_htseq) # Create wig file export.wig(cover, name="example_track") # This would create the file "example_track.chr1.wig" with: # track type=wiggle_0 name="example_track" # fixedStep chrom=chr1 start=1 step=1 # 55.55247 # 55.55247 # 55.55247 # 277.7623 # 388.8673 # ...
Remove noise from genomic data smoothing and cleaning the observed signal. This function doesn't alter the shape or the values of the signal as much as the traditional method of sliding window average does, providing a great correlation within the original and filtered data (>0.99).
filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ... ) ## S4 method for signature 'SimpleRleList' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, mc.cores = 1, ... ) ## S4 method for signature 'Rle' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ... ) ## S4 method for signature 'list' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, mc.cores = 1, ... ) ## S4 method for signature 'numeric' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ... )filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ... ) ## S4 method for signature 'SimpleRleList' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, mc.cores = 1, ... ) ## S4 method for signature 'Rle' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ... ) ## S4 method for signature 'list' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, mc.cores = 1, ... ) ## S4 method for signature 'numeric' filterFFT( data, pcKeepComp = "auto", showPowerSpec = FALSE, useOptim = TRUE, ... )
data |
Coverage or intensities values representing the results of the
NGS of TA experiment. This attribute could be a individual vector
representing a chromosome ( |
pcKeepComp |
Number of components to select, in percentage respect total length of the sample. Allowed values are numeric (in range 0:1) for manual setting or "auto" for automatic detection. See details. |
showPowerSpec |
Plot the Power Spectrum of the Fast Fourier Transform to visually identify the selected components (see details). |
useOptim |
This function implements tweaks to a standard fft call to
improve (dramatically) the performance in large genomic data. These
optimizations can be bypassed by setting this parameter to |
... |
Other parameters to be passed to |
mc.cores |
If multiple cores are available, maximum number of them to
use for parallel processing of |
Fourier-analysis principal components selection is widely used in signal processing theory for an unbiased cleaning of a signal over the time.
Other procedures, as the traditional sliding window average, can change too much the shape of the results in function of the size of the window, and moreover they don't only smooth the noise without removing it.
With a Fourier Transform of the original signal, the input signal is descomposed in diferent wavelets and described as a combination of them. Long frequencies can be explained as a function of two ore more periodical shorter frequecies. This is the reason why long, unperiodic sequences are usually identified as noise, and therefore is desireable to remove them from the signal we have to process.
This procedure here is applied to genomic data, providing a novel method to obtain perfectly clean values wich allow an efficient detection of the peaks which can be used for a direct nucleosome position recognition.
This function select a certain number of components in the original power
spectrum (the result of the Fast Fourier Transform which can be seen with
showPowerSpec=TRUE) and sets the rest of them to 0 (component knock-out).
The amout of components to keep (given as a percentage of the input lenght)
can be set by the pcKeepComp. This will select the first components of
the signal, knock-outing the rest. If this value is close to 1, more
components will be selected and then more noise will be allowed in the
output. For an effective filtering which removes the noise keeping almost
all relevant peaks, a value between 0.01 and 0.05 is usually sufficient.
Lower values can cause merging of adjacent minor peaks.
This library also allows the automatic detection of a fitted value for
pcKeepComp. By default, if uses the pcKeepCompDetect function, which
looks which is the minimum percentage of components than can reproduce
the original signal with a corelation between the filtered and the original
one of 0.99. See the help page of pcKeepCompDetect for further details and
reference of available parameters.
One of the most powerful features of nucleR is the efficient
implementation of the FFT to genomic data. This is achived trought few
tweaks that allow an optimum performance of the Fourier Transform. This
includes a by-range filtering, an automatic detection of uncovered regions,
windowed execution of the filter and padding of the data till nearest power
of 2 (this ensures an optimum case for FFT due the high factorization of
components). Internal testing showed up that in specific datasets, these
optimizations lead to a dramatic improvement of many orders of magnitude
(from 3 days to few seconds) while keeping the correlation between the
native fft call and our filterFFT higher than 0.99. So, the use of these
optimizations is highly recomended.
If for some reason you want to apply the function without any kind of
optimizations you can specify the parameter useOptim=FALSE to bypass them
and get the pure knockout inverse from native FFT call. All other parameters
can be still applyied in this case.
Numeric vector with cleaned/smoothed values
Oscar Flores [email protected], David Rosell [email protected]
Smith, Steven W. (1999), The Scientist and Engineer's Guide to Digital Signal Processing (Second ed.), San Diego, Calif.: California Technical Publishing, ISBN 0-9660176-3-3 (availabe online: http://www.dspguide.com/pdfbook.htm)
# Load example data, raw hybridization values for Tiling Array raw_data <- get(data(nucleosome_tiling)) # Filter data fft_data <- filterFFT(raw_data, pcKeepComp=0.01) # See both profiles library(ggplot2) plot_data <- rbind( data.frame(x=seq_along(raw_data), y=raw_data, intensities="raw"), data.frame(x=seq_along(fft_data), y=fft_data, intensities="filtered") ) qplot(x=x, y=y, data=plot_data, geom="line", xlab="position", ylab="intensities") + facet_grid(intensities~.) # The power spectrum shows a visual representation of the components fft_data <- filterFFT(raw_data, pcKeepComp=0.01, showPowerSpec=TRUE)# Load example data, raw hybridization values for Tiling Array raw_data <- get(data(nucleosome_tiling)) # Filter data fft_data <- filterFFT(raw_data, pcKeepComp=0.01) # See both profiles library(ggplot2) plot_data <- rbind( data.frame(x=seq_along(raw_data), y=raw_data, intensities="raw"), data.frame(x=seq_along(fft_data), y=fft_data, intensities="filtered") ) qplot(x=x, y=y, data=plot_data, geom="line", xlab="position", ylab="intensities") + facet_grid(intensities~.) # The power spectrum shows a visual representation of the components fft_data <- filterFFT(raw_data, pcKeepComp=0.01, showPowerSpec=TRUE)
When using single-ended sequencing, the resulting partial sequences map only
in one strand, causing a bias in the coverage profile if not corrected. The
only way to correct this is knowing the average size of the real fragments.
nucleR uses this information when preprocessing single-ended sequences.
You can provide this information by your own (usually a 147bp length is a
good aproximation) or you can use this method to automatically guess the
size of the inserts.
fragmentLenDetect( reads, samples = 1000, window = 5000, min.shift = 1, max.shift = 100, mc.cores = 1, as.shift = FALSE ) ## S4 method for signature 'AlignedRead' fragmentLenDetect( reads, samples = 1000, window = 1000, min.shift = 1, max.shift = 100, mc.cores = 1, as.shift = FALSE ) ## S4 method for signature 'GRanges' fragmentLenDetect( reads, samples = 1000, window = 1000, min.shift = 1, max.shift = 100, mc.cores = 1, as.shift = FALSE )fragmentLenDetect( reads, samples = 1000, window = 5000, min.shift = 1, max.shift = 100, mc.cores = 1, as.shift = FALSE ) ## S4 method for signature 'AlignedRead' fragmentLenDetect( reads, samples = 1000, window = 1000, min.shift = 1, max.shift = 100, mc.cores = 1, as.shift = FALSE ) ## S4 method for signature 'GRanges' fragmentLenDetect( reads, samples = 1000, window = 1000, min.shift = 1, max.shift = 100, mc.cores = 1, as.shift = FALSE )
reads |
Raw single-end reads ShortRead::AlignedRead or GenomicRanges::GRanges format) |
samples |
Number of samples to perform the analysis (more = slower but more accurate) |
window |
Analysis window. Usually there's no need to touch this parameter. |
min.shift, max.shift
|
Minimum and maximum shift to apply on the strands to detect the optimal fragment size. If the range is too big, the performance decreases. |
mc.cores |
If multicore support, maximum number of cores allowed to use. |
as.shift |
If TRUE, returns the shift needed to align the middle of the reads in opposite strand. If FALSE, returns the mean inferred fragment length. |
This function shifts one strand downstream one base by one from min.shift
to max.shift. In every step, the correlation on a random position of
length window is checked between both strands. The maximum correlation is
returned and averaged for samples repetitions.
The final returned length is the best shift detected plus the width of the
reads. You can increase the performance of this function by reducing the
samples value and/or narrowing the shift range. The window size has
almost no impact on the performance, despite a to small value can give
biased results.
Inferred mean lenght of the inserts by default, or shift needed to
align strands if as.shift=TRUE.
Oscar Flores [email protected]
library(GenomicRanges) library(IRanges) # Create a sinthetic dataset, simulating single-end reads, for positive and # negative strands # Positive strand reads pos <- syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads # Negative strand (shifted 147bp) neg <- IRanges(end=start(pos)+147, width=40) sim <- GRanges( seqnames="chr1", ranges=c(pos, neg), strand=c(rep("+", length(pos)), rep("-", length(neg))) ) # Detect fragment lenght (we know by construction it is really 147) fragmentLenDetect(sim, samples=50) # The function restricts the sampling to speed up the examplelibrary(GenomicRanges) library(IRanges) # Create a sinthetic dataset, simulating single-end reads, for positive and # negative strands # Positive strand reads pos <- syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads # Negative strand (shifted 147bp) neg <- IRanges(end=start(pos)+147, width=40) sim <- GRanges( seqnames="chr1", ranges=c(pos, neg), strand=c(rep("+", length(pos)), rep("-", length(neg))) ) # Detect fragment lenght (we know by construction it is really 147) fragmentLenDetect(sim, samples=50) # The function restricts the sampling to speed up the example
This function joints close nucleosome calls into one larger, fuzzy nucleosome.
mergeCalls( calls, min.overlap = 50, discard.low = 0.2, mc.cores = 1, verbose = TRUE ) ## S4 method for signature 'GRanges' mergeCalls(calls, min.overlap = 50, discard.low = 0.2, verbose = TRUE)mergeCalls( calls, min.overlap = 50, discard.low = 0.2, mc.cores = 1, verbose = TRUE ) ## S4 method for signature 'GRanges' mergeCalls(calls, min.overlap = 50, discard.low = 0.2, verbose = TRUE)
calls |
GenomicRanges::GRanges with scored and ranged nucleosome
calls from |
min.overlap |
Minimum overlap between two reads for merge them |
discard.low |
Discard low covered calls (i.e. calls with |
mc.cores |
Number of cores available to parallel data processing. |
verbose |
Show progress info? |
This functions looks for overlapped calls and join those with more than
min.overlap bases overlapped. More than two reads can be joined in
one single call if all of them are overlapped at least that distance with
almost another read in the range.
Joining is performed in chain, so if nucleosome call A is close to B and B is close to C, the final call will comprise the range A-B-C. The resulting scores (mixed, width, height) of the final joined call will be the average value of the individual scores.
The parameter discard.low allows to ignore the small peaks that could be
merged with larger ones, originating large calls. In the case that all of
the overlapped reads in a given position have score_h less than
discard.low, all of them will be selected instead of deleting that call.
GenomicRanges::GRanges with merged calls and the additional data
column nmerge, with the count of how many original ranges are merged in
the resulting range.
Oscar Flores [email protected]
# Generate a synthetic coverage map (assuming reads of 40bp and fragments # of 130) map <- syntheticNucMap( wp.num=20, fuz.num=20, nuc.len=40, lin.len=130, rnd.seed=1 ) cover <- filterFFT(coverage.rpm(map$syn.reads)) # Find peaks over FFT filtered coverage calls <- peakDetection(filterFFT( cover, pcKeepComp=0.02), width=130, score=TRUE ) # Merge overlapped calls merged_calls <- mergeCalls(calls) plotPeaks(merged_calls, cover)# Generate a synthetic coverage map (assuming reads of 40bp and fragments # of 130) map <- syntheticNucMap( wp.num=20, fuz.num=20, nuc.len=40, lin.len=130, rnd.seed=1 ) cover <- filterFFT(coverage.rpm(map$syn.reads)) # Find peaks over FFT filtered coverage calls <- peakDetection(filterFFT( cover, pcKeepComp=0.02), width=130, score=TRUE ) # Merge overlapped calls merged_calls <- mergeCalls(calls) plotPeaks(merged_calls, cover)
Few reads from paired-ended MNase-seq experiment in S.cerevisiae where mononucleosomes were sequenced
GRanges with the range of the reads and a data column with the
strand information.
This data is obtained from MNase digested nucleosomal DNA and sequenced with
Illumina platform. Paired-ended reads where mapped to SacCer1 genome using
Bowtie, and imported to R using the package ShortRead and paired ends
where merged into a single range.
Reads were sorted by chromosome and starting position and only a few reads from the starting positions of chromosome 1 are presented.
Publication pending
Some bases from S.cerevisiae tiling microarray where mononucleosomes were sequenced and hybridizated with histone-free naked DNA. The intensity is the normalized ratio between the intensities from nucleosomic and naked DNA.
numeric vector with the intensities.
Due to the difficulty of providing a raw file, this file has been preprocessed. See details.
The raw .CEL files from Affymetrix S.Cerevisiae Tilling 1.0R Array (3
nucleosomal + 3 naked DNA) has been merged using package Starr and the
resulting ExpressionSet object has been passed to processTilingArray
function from this package as follows:
processTilingArray(data, exprName, chrPAttern="Sc:Oct_2003;chr1", closeGaps=50)
The first 8000bp of the chr1 have been saved as this example dataset.
Publication pending
pcKeepComp param for filterFFT functionThis function tries to obtain the minimum number of components needed in a
FFT filter to achieve or get as close as possible to a given correlation
value. Usually you don't need to call directly this function, is used in
filterFFT by default.
pcKeepCompDetect( data, pc.min = 0.01, pc.max = 0.1, max.iter = 20, verbose = FALSE, cor.target = 0.98, cor.tol = 0.001, smpl.num = 25, smpl.min.size = 2^10, smpl.max.size = 2^14 )pcKeepCompDetect( data, pc.min = 0.01, pc.max = 0.1, max.iter = 20, verbose = FALSE, cor.target = 0.98, cor.tol = 0.001, smpl.num = 25, smpl.min.size = 2^10, smpl.max.size = 2^14 )
data |
Numeric vector to be filtered |
pc.min, pc.max
|
Range of allowed values for |
max.iter |
Maximum number of iterations |
verbose |
Extra information (debug) |
cor.target |
Target correlation between the filtered and the original profiles. A value around 0.99 is recommeded for Next Generation Sequencing data and around 0.7 for Tiling Arrays. |
cor.tol |
Tolerance allowed between the obtained correlation an the target one. |
smpl.num |
If |
smpl.min.size, smpl.max.size
|
Minimum and maximum size of the samples. This is used for selection and sub-selection of ranges with meaningful values (i,e, different from 0 and NA). Power of 2 values are recommended, despite non-mandatory. |
... |
Parameters to be pass to |
This function predicts a suitable pcKeepComp value for filterFFT
function. This is the recommended amount of components (in percentage) to
keep in the filterFFT function to obtain a correlation of (or near of)
cor.target.
The search starts from two given values pc.min, pc.max and uses linial
interpolation to quickly reach a value that gives a corelation between the
filtered and the original near cor.target within the specified tolerance
cor.tol.
To allow a quick detection without an exhaustive search, this function uses
a subset of the data by randomly sampling those regions with meaningful
coverage values (i,e, different from 0 or NA) larger than smpl.min.size.
If it's not possible to obtain smpl.max.size from this region (this could
be due to flanking 0's, for example) at least smpl.min.size will be used
to check correlation. Mean correlation between all sampled regions is used
to test the performance of the pcKeepComp parameter.
If the number of meaningful bases in data is less than smpl.min.size * (smpl.num/2) all the data vector will be used instead of using sampling.
Fitted pcKeepComp value
Oscar Flores [email protected], David Rosell [email protected]
# Load dataset data(nucleosome_htseq) data <- as.vector(coverage.rpm(nucleosome_htseq)[[1]]) # Get recommended pcKeepComp value pckeepcomp <- pcKeepCompDetect(data, cor.target=0.99) print(pckeepcomp) # Call filterFFT f1 <- filterFFT(data, pcKeepComp=pckeepcomp) # Also this can be called directly f2 <- filterFFT(data, pcKeepComp="auto", cor.target=0.99) # Plot library(ggplot2) i <- 1:2000 plot_data <- rbind( data.frame(x=i, y=data[i], coverage="original"), data.frame(x=i, y=f1[i], coverage="two calls"), data.frame(x=i, y=f2[i], coverage="one call") ) qplot(x=x, y=y, color=coverage, data=plot_data, geom="line", xlab="position", ylab="coverage")# Load dataset data(nucleosome_htseq) data <- as.vector(coverage.rpm(nucleosome_htseq)[[1]]) # Get recommended pcKeepComp value pckeepcomp <- pcKeepCompDetect(data, cor.target=0.99) print(pckeepcomp) # Call filterFFT f1 <- filterFFT(data, pcKeepComp=pckeepcomp) # Also this can be called directly f2 <- filterFFT(data, pcKeepComp="auto", cor.target=0.99) # Plot library(ggplot2) i <- 1:2000 plot_data <- rbind( data.frame(x=i, y=data[i], coverage="original"), data.frame(x=i, y=f1[i], coverage="two calls"), data.frame(x=i, y=f2[i], coverage="one call") ) qplot(x=x, y=y, color=coverage, data=plot_data, geom="line", xlab="position", ylab="coverage")
This function allows a efficient recognition of the local maximums (peaks) in a given numeric vector.
peakDetection( data, threshold = 0.25, chromosome = NULL, width = 1, score = TRUE, min.cov = 2, mc.cores = 1 ) ## S4 method for signature 'list' peakDetection( data, threshold = "25%", width = 1, score = TRUE, min.cov = 2, mc.cores = 1 ) ## S4 method for signature 'numeric' peakDetection( data, threshold = "25%", chromosome = NULL, width = 1, score = TRUE, min.cov = 2, mc.cores = 1 )peakDetection( data, threshold = 0.25, chromosome = NULL, width = 1, score = TRUE, min.cov = 2, mc.cores = 1 ) ## S4 method for signature 'list' peakDetection( data, threshold = "25%", width = 1, score = TRUE, min.cov = 2, mc.cores = 1 ) ## S4 method for signature 'numeric' peakDetection( data, threshold = "25%", chromosome = NULL, width = 1, score = TRUE, min.cov = 2, mc.cores = 1 )
data |
Input numeric values, or a list of them |
threshold |
Threshold value from which the peaks will be selected. Can
be given as a percentage string (i.e., |
chromosome |
Optionally specify the name of the chromosome for input data that doesn't specify it. |
width |
If a positive integer > 1 is given, the peaks are returned as a range of the given width centered in the local maximum. Useful for nucleosome calling from a coverage peak in the dyad. |
score |
If TRUE, the results will be scored using |
min.cov |
Minimum coverage that a peak needs in order to be considered as a nucleosome call. |
mc.cores |
The number of cores to use, i.e. at most how many child processes will be run simultaneously. Parallelization requires at least two cores. |
It's recommended to smooth the input with filterFFT prior the detection.
The type of the return depends on the input parameters:
numeric (or a list of them) if width==1 & score==FALSE containing
the position of the peaks.
data.frame (or list of them) if width==1 & score==TRUE containing a
'peak' column with the position of the peak plus a 'score' column with
its score.
IRanges (or IRangesList) if width>1 & score==FALSE containing the
ranges of the peaks.
GRanges if width>1 & score==TRUE containing the ranges of the
peaks and the assigned score.
If width > 1, those ranges outside the range 1:length(data) will
be skipped.
Oscar Flores [email protected]
# Generate a random peaks profile reads <- syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads cover <- coverage.rpm(reads) # Filter them cover_fft <- filterFFT(cover) # Detect and plot peaks (up a bit the threshold for accounting synthetic # data) peaks <- peakDetection(cover_fft, threshold="40%", score=TRUE) plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000) # Now use ranges version, which accounts for fuzziness when scoring peaks <- peakDetection(cover_fft, threshold="40%", score=TRUE, width=147) plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000)# Generate a random peaks profile reads <- syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads cover <- coverage.rpm(reads) # Filter them cover_fft <- filterFFT(cover) # Detect and plot peaks (up a bit the threshold for accounting synthetic # data) peaks <- peakDetection(cover_fft, threshold="40%", score=TRUE) plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000) # Now use ranges version, which accounts for fuzziness when scoring peaks <- peakDetection(cover_fft, threshold="40%", score=TRUE, width=147) plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000)
Scores peaks detected with function peakDetection according the height and
the sharpness (width) of the peak. This function can be called automatically
from peakDetection if score=TRUE.
peakScoring(peaks, data, threshold = 0.25, ...) ## S4 method for signature 'list' peakScoring(peaks, data, threshold = "25%", mc.cores = 1) ## S4 method for signature 'IRangesList' peakScoring( peaks, data, threshold = "25%", weight.width = 1, weight.height = 1, dyad.length = 38, mc.cores = 1 ) ## S4 method for signature 'numeric' peakScoring(peaks, data, chromosome = NULL, threshold = "25%") ## S4 method for signature 'IRanges' peakScoring( peaks, data, chromosome = NULL, threshold = "25%", weight.width = 1, weight.height = 1, dyad.length = 38 )peakScoring(peaks, data, threshold = 0.25, ...) ## S4 method for signature 'list' peakScoring(peaks, data, threshold = "25%", mc.cores = 1) ## S4 method for signature 'IRangesList' peakScoring( peaks, data, threshold = "25%", weight.width = 1, weight.height = 1, dyad.length = 38, mc.cores = 1 ) ## S4 method for signature 'numeric' peakScoring(peaks, data, chromosome = NULL, threshold = "25%") ## S4 method for signature 'IRanges' peakScoring( peaks, data, chromosome = NULL, threshold = "25%", weight.width = 1, weight.height = 1, dyad.length = 38 )
peaks |
The identified peaks resulting from |
data |
Data of nucleosome coverage or intensites. |
threshold |
The non-default |
... |
Further arguments to be passed to or from other methods. |
mc.cores |
If input is a |
weight.height, weight.width
|
If the score is a range, the height and the widht score (coverage and fuzzynes) can be defined with different weigths with these parameters. See details. |
dyad.length |
How many bases account in the nucleosome dyad for
sharpness description. If working with NGS data, works best with the reads
width value for single-ended data or the |
chromosome |
Optionally specify the name of the chromosome for input data that doesn't specify it. |
This function scores each previously identified peak according its height and sharpness.
The height score (score_h) tells how large is a peak, higher means more
coverage or intensity, so better positioned nucleosome. This score is
obtained by checking the observed peak value in a Normal distribution with
the mean and sd of data. This value is between 0 and 1.
The width score (score_w) is a mesure of how sharp is a peak. With a NGS
coverage in mind, a perfect phased (well-positioned) nucleosome is this that
starts and ends exactly in the same place many times. The shape of this
ideal peak will be a rectangular shape of the lenght of the read. A wider
top of a peak could indicate fuzzyness. The parameter dyad.length tells
how long should be the "flat" region of an ideal peak. The optimum value for
this parameter is the lenght of the read in single-ended data or the trim
value of the function processReads. For Tiling Arrays, the default value
should be fine.
This score is obtained calculating the ratio between the mean of the
nucleosome scope (the one provided by range in the elements of peaks) and
the dyad.length central bases. This value is normalized between 0 and 1.
For punctual, single points peaks (provided by numeric vector or list as
peaks attribute) the score returned is the height score.
For range peaks the weighted sum of the heigth and width scores is used.
This is: ((score_h * weigth.height) / sum.wei) + ((score_w * weigth.width) / sum.wei). Note that you can query for only one score by setting its
weight to 1 and the other to 0.
In the case of numeric input, the value returned is a data.frame
containing a 'peak' and a 'score' column. If the input is a list, the
result will be a list of data.frame.
If input is a IRanges or IRangesList, the result will be a data.frame
or GenomicRanges::GRanges object with one or multiple spaces respectively
and a 3 data column with the mixed, width and heigh score.
Oscar Flores [email protected]
peakDetection(), processReads(),
# Generate a synthetic map # Trimmed length nucleosome map map <- syntheticNucMap(nuc.len=40, lin.len=130) # Get the information of dyads and the coverage peaks <- c(map$wp.starts, map$fz.starts) cover <- filterFFT(coverage.rpm(map$syn.reads)) # Calculate the scores scores <- peakScoring(peaks, cover) plotPeaks(scores$peak, cover, scores=scores$score, start=5000, end=10000)# Generate a synthetic map # Trimmed length nucleosome map map <- syntheticNucMap(nuc.len=40, lin.len=130) # Get the information of dyads and the coverage peaks <- c(map$wp.starts, map$fz.starts) cover <- filterFFT(coverage.rpm(map$syn.reads)) # Calculate the scores scores <- peakScoring(peaks, cover) plotPeaks(scores$peak, cover, scores=scores$score, start=5000, end=10000)
Helper function for a quick and convenient overview of nucleosome calling data.
plotPeaks(peaks, data, ...) ## S4 method for signature 'numeric' plotPeaks( peaks, data, threshold = 0, scores = NULL, start = 1, end = length(data), xlab = "position", ylab = "coverage", type = 1, col.points = "red", thr.lty = 1, thr.lwd = 1, thr.col = "darkred", scor.col = col.points, scor.cex = 2.5, scor.digits = 2, scor.nudge = 2000 ) ## S4 method for signature 'data.frame' plotPeaks(peaks, data, ...) ## S4 method for signature 'GRanges' plotPeaks(peaks, data, ...) ## S4 method for signature 'IRanges' plotPeaks( peaks, data, threshold = 0, scores = NULL, start = 1, end = length(data), dyn.pos = TRUE, xlab = "position", ylab = "coverage", type = 1, col.points = "red", thr.lty = 1, thr.lwd = 1, thr.col = "darkred", rect.thick = 2, rect.lwd = 0.5, rect.border = "black", scor.col = col.points, scor.cex = 2.5, scor.digits = 2, indiv.scores = FALSE, scor.nudge = 2000 )plotPeaks(peaks, data, ...) ## S4 method for signature 'numeric' plotPeaks( peaks, data, threshold = 0, scores = NULL, start = 1, end = length(data), xlab = "position", ylab = "coverage", type = 1, col.points = "red", thr.lty = 1, thr.lwd = 1, thr.col = "darkred", scor.col = col.points, scor.cex = 2.5, scor.digits = 2, scor.nudge = 2000 ) ## S4 method for signature 'data.frame' plotPeaks(peaks, data, ...) ## S4 method for signature 'GRanges' plotPeaks(peaks, data, ...) ## S4 method for signature 'IRanges' plotPeaks( peaks, data, threshold = 0, scores = NULL, start = 1, end = length(data), dyn.pos = TRUE, xlab = "position", ylab = "coverage", type = 1, col.points = "red", thr.lty = 1, thr.lwd = 1, thr.col = "darkred", rect.thick = 2, rect.lwd = 0.5, rect.border = "black", scor.col = col.points, scor.cex = 2.5, scor.digits = 2, indiv.scores = FALSE, scor.nudge = 2000 )
peaks |
|
data |
Coverage or Tiling Array intensities |
... |
Arguments to be passed to other methods. |
threshold |
Threshold applied in |
scores |
If |
start, end
|
Start and end points defining a subset in the range of
|
xlab, ylab, type, col.points
|
Default values with general properties of the plot |
thr.lty, thr.lwd, thr.col
|
Default values with general properties for threshold representation |
scor.col, scor.nudge, scor.cex, scor.digits
|
Default values for
|
dyn.pos |
If peaks are ranges, should they be positioned dynamicaly on
top of the peaks or staticaly at |
rect.thick, rect.lwd, rect.border
|
Default values for
|
indiv.scores |
Show or hide individual scores for width and height in brakets besides the mixed score. |
This function is intended to plot data previously processed with nucleR
pipeline. It shows a coverage/intensity profile toghether with the
identified peaks. If available, score of each peak is also shown.
(none)
Ricard Illa [email protected]
peakDetection(), peakScoring(), ggplot2::ggplot(),
# Generate a random peaks profile reads <- syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads cover <- coverage.rpm(reads) # Filter them cover_fft <- filterFFT(cover) # Detect peaks peaks <- peakDetection(cover_fft, threshold="40%", score=TRUE, width=140) # Plot peaks and coverage profile (show only a window) plotPeaks(peaks, cover_fft, threshold="40%", start=1000, end=6000)# Generate a random peaks profile reads <- syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads cover <- coverage.rpm(reads) # Filter them cover_fft <- filterFFT(cover) # Detect peaks peaks <- peakDetection(cover_fft, threshold="40%", score=TRUE, width=140) # Plot peaks and coverage profile (show only a window) plotPeaks(peaks, cover_fft, threshold="40%", start=1000, end=6000)
This method allows the processment of NGS nucleosome reads from different sources and a basic manipulation of them. The tasks includes the correction of strand-specific single-end reads and the trimming of reads to a given length.
processReads(data, type = "single", fragmentLen, trim, ...) ## S4 method for signature 'AlignedRead' processReads(data, type = "single", fragmentLen, trim, ...) ## S4 method for signature 'CompressedGRangesList' processReads(data, type = "single", fragmentLen, trim, ...) ## S4 method for signature 'GRanges' processReads(data, type = "single", fragmentLen, trim, ...)processReads(data, type = "single", fragmentLen, trim, ...) ## S4 method for signature 'AlignedRead' processReads(data, type = "single", fragmentLen, trim, ...) ## S4 method for signature 'CompressedGRangesList' processReads(data, type = "single", fragmentLen, trim, ...) ## S4 method for signature 'GRanges' processReads(data, type = "single", fragmentLen, trim, ...)
data |
Sequence reads objects, probably imported using other packages
as |
type |
Describes the type of reads. Values allowed are |
fragmentLen |
Expected original length of the sequenced fragments. See details. |
trim |
Length to trim the reads (or extend them if |
... |
Other parameters passed to |
This function reads a ShortRead::AlignedRead or a GenomicRanges::GRanges object containing the position, length and strand of the sequence reads.
It allows the processment of both paired and single ended reads. In the case
of single end reads this function corrects the strand-specific mapping by
shifting plus strand reads and minus strand reads towards a middle position
where both strands are overlaped. This is done by accounting the expected
fragment length (fragmentLen).
For paired end reads, mononucleosomal reads could extend more than expected
length due to mapping issues or experimental conditions. In this case, the
fragmentLen variable sets the threshold from which reads longer than it
should be ignored.
If no value is supplied for fragmentLen it will be calculated
automatically (increasing the computing time) using fragmentLenDetect with
default parameters. Performance can be increased by tunning
fragmentLenDetect parameteres in a separated call and passing its result
as fragmentLen parameter.
In some cases, could be useful trim the reads to a shorter length to improve
the detection of nucleosome dyads, easing its detection and automatic
positioning. The parameter trim allows the selection of how many
nucleotides select from each read.
A special case for single-ended data is setting the trim to the same
value as fragmentLen, so the reads will be extended strand-wise towards
the 3' direction, creating an artificial map comparable with
paired-ended data. The same but opposite can be performed with paired-end
data, setting a trim value equal to the read length from paired ended, so
paired-ended data will look like single-ended.
GenomicRanges::GRanges containing the aligned/trimmed individual reads.
IMPORTANT: this information is only used to correct possible strand-specific mapping, this package doesn't link the two ends of paired reads.
Oscar Flores [email protected]
ShortRead::AlignedRead, GenomicRanges::GRanges,
fragmentLenDetect()
# Load data data(nucleosome_htseq) # Process nucleosome reads, select only those shorter than 200bp pr1 <- processReads(nucleosome_htseq, fragmentLen=200) # Now process them, but picking only the 40 bases surrounding the dyad pr2 <- processReads(nucleosome_htseq, fragmentLen=200, trim=40) # Compare the results: library(ggplot2) cov1 <- as.vector(coverage.rpm(pr1)[["chr1"]]) cov2 <- as.vector(coverage.rpm(pr2)[["chr1"]]) plot_data <- rbind( data.frame(x=seq_along(cov1), y=cov1, coverage="original"), data.frame(x=seq_along(cov2), y=cov2, coverage="trimmed") ) qplot(x=x, y=y, geom="line", data=plot_data, xlab="position", ylab="coverage") + facet_grid(coverage~.)# Load data data(nucleosome_htseq) # Process nucleosome reads, select only those shorter than 200bp pr1 <- processReads(nucleosome_htseq, fragmentLen=200) # Now process them, but picking only the 40 bases surrounding the dyad pr2 <- processReads(nucleosome_htseq, fragmentLen=200, trim=40) # Compare the results: library(ggplot2) cov1 <- as.vector(coverage.rpm(pr1)[["chr1"]]) cov2 <- as.vector(coverage.rpm(pr2)[["chr1"]]) plot_data <- rbind( data.frame(x=seq_along(cov1), y=cov1, coverage="original"), data.frame(x=seq_along(cov2), y=cov2, coverage="trimmed") ) qplot(x=x, y=y, geom="line", data=plot_data, xlab="position", ylab="coverage") + facet_grid(coverage~.)
Process and transform the microarray data coming from tiling array nucleosome positioning experiments.
processTilingArray( data, exprName, chrPattern, inferLen = 50, mc.cores = 1, quiet = FALSE )processTilingArray( data, exprName, chrPattern, inferLen = 50, mc.cores = 1, quiet = FALSE )
data |
|
exprName |
Name of the |
chrPattern |
Only chromosomes that contain |
inferLen |
Maximum length (in basepairs) for allowing data gaps
inference. See |
mc.cores |
Number of cores available to parallel data processing. |
quiet |
Avoid printing on going information (TRUE | FALSE) |
The processing of tiling arrays could be complicated as many types exists on the market. This function deals ok with Affymetrix Tiling Arrays in yeast, but hasn't been tested on other species or platforms.
The main aim is convert the output of preprocessing steps (supplied by third-parties packages) to a clean genome wide nucleosome occupancy profile.
Tiling arrays doesn't use to provide a one-basepair resolution data, so one gets one value per probe in the array, covering X basepairs and shifted (tiled) Y basepairs respect the surrounding ones. So, one gets a piece of information every Y basepairs.
This function tries to convert this noisy, low resolution data, to a one-basepair signal, which allows a fast recognition of nucleosomes without using large and artificious statistical machinery as Hidden Markov Models using posterionr noise cleaning process.
As example, imagine your array has probes of 20mers and a tiling between probes of 10bp. Starting at position 1 (covering coordinates from 1 to 20), the next probe will be in position 10 (covering the coordinates 10 to 29). This can be represented as two hybridization intensity values on coordinates 1 and 10. This function will try to infer (using a lineal distribution) the values from 2 to 9 using the existing values of probes in coordinate 1 and coordinate 10.
The tiling space between adjacent array probes could be not constant, or
could be also there are regions not covered in the used microarray. With the
function argument inferLen you can specify wich amout of space (in
basepairs) you allow to infer the non-present values.
If at some point the range not covered (gap) between two adjacent probes of
the array is greater than inferLen value, then the coordinates between
these probes will be setted to NA.
RleList with the observed/inferred values for each coordinate.
This function could not cover all kind of arrays in
the market. This package assumes the data is processed and normalized
prior this processing, using standard microarray packages existing for R,
like Starr.
This function should be suitable for all data objects of kind
ExpressionSet coding the annotations "chr" for chromosome and "pos"
for position (acccessible by pData(data@featureData)) and a expression
value (accessible by exprs(data)).
Oscar Flores [email protected]
Biobase::ExpressionSet, Starr::getRatio()
## Not run: # Dataset cannot be provided for size restrictions # This is the code used to get the hybridization ratio with Starr from # CEL files library("Starr") TA_parsed <- readCelFile( BPMap, CELfiles, CELnames, CELtype, featureData=TRUE, log.it=TRUE ) TA_loess <- normalize.Probes(TA_parsed, method="loess") TA_ratio <- getRatio( TA_loess, TA_loess$type=="IP", TA_loess$type=="CONTROL", "myRatio" ) # From here, we use nucleR: # Preprocess the array, using the calculated ratio feature we named # "myRatio". # This will also select only those chromosomes with the pattern # "Sc:Oct_2003;chr", removing control data present in that tiling # array. # Finally, we allow that loci not covered by a prove being inferred # from adjacent ones, as far as they are separated by 50bp or less arr <- processTilingArray( TA_ratio, "myRatio", chrPattern="Sc:Oct_2003;chr", inferLen=50 ) # From here we can proceed with the analysis: arr_fft <- filterFFT(arr) arr_pea <- peakDetection(arr_fft) plotPeaks(arr_pea, arr_fft) ## End(Not run)## Not run: # Dataset cannot be provided for size restrictions # This is the code used to get the hybridization ratio with Starr from # CEL files library("Starr") TA_parsed <- readCelFile( BPMap, CELfiles, CELnames, CELtype, featureData=TRUE, log.it=TRUE ) TA_loess <- normalize.Probes(TA_parsed, method="loess") TA_ratio <- getRatio( TA_loess, TA_loess$type=="IP", TA_loess$type=="CONTROL", "myRatio" ) # From here, we use nucleR: # Preprocess the array, using the calculated ratio feature we named # "myRatio". # This will also select only those chromosomes with the pattern # "Sc:Oct_2003;chr", removing control data present in that tiling # array. # Finally, we allow that loci not covered by a prove being inferred # from adjacent ones, as far as they are separated by 50bp or less arr <- processTilingArray( TA_ratio, "myRatio", chrPattern="Sc:Oct_2003;chr", inferLen=50 ) # From here we can proceed with the analysis: arr_fft <- filterFFT(arr) arr_pea <- peakDetection(arr_fft) plotPeaks(arr_pea, arr_fft) ## End(Not run)
This function allows to load reads from BAM files from both single and paired-end commming from Next Generation Sequencing nucleosome mapping experiments.
readBAM(files, type = "paired")readBAM(files, type = "paired")
files |
List of input BAM files. |
type |
Describes the type of reads. Values allowed are |
GenomicRanges::GRangesList containing the reads of each input BAM file.
Ricard Illa [email protected]
infile <- system.file( "extdata", "cellCycleM_chrII_5000-25000.bam", package="nucleR" ) reads <- readBAM(infile, type="paired")infile <- system.file( "extdata", "cellCycleM_chrII_5000-25000.bam", package="nucleR" ) reads <- readBAM(infile, type="paired")
This function allows to load reads from Bowtie files from both single and paired-end commming from Next Generation Sequencing nucleosome mapping experiments.
readBowtie(files, type = "paired")readBowtie(files, type = "paired")
files |
List of input Bowtie files. |
type |
Describes the type of reads. Values allowed are |
GenomicRanges::GRangesList containing the reads of each input BAM file.
Ricard Illa [email protected]
This function generates a synthetic nucleosome map using the parameters given by the user and returns the coverage (like NGS experiments) or a pseudo-hybdridization ratio (like Tiling Arrays) toghether with the perfect information about the well positioned and fuzzy nucleosome positions.
syntheticNucMap( wp.num = 100, wp.del = 10, wp.var = 20, fuz.num = 50, fuz.var = 50, max.cover = 20, nuc.len = 147, lin.len = 20, rnd.seed = NULL, as.ratio = FALSE, show.plot = FALSE )syntheticNucMap( wp.num = 100, wp.del = 10, wp.var = 20, fuz.num = 50, fuz.var = 50, max.cover = 20, nuc.len = 147, lin.len = 20, rnd.seed = NULL, as.ratio = FALSE, show.plot = FALSE )
wp.num |
Number of well-positioned (non overlapped) nucleosomes. They
are placed uniformly every |
wp.del |
Number of well-positioned nucleosomes (the ones generated by
|
wp.var |
Maximum variance in basepairs of the well-positioned nucleosomes. This will create some variation in the position of the reads describing the same nucleosome. |
fuz.num |
Number of fuzzy nucleosomes. They are distributed randomly over all the region. They could be overlapped with other well-positioned or fuzzy nucleosomes. |
fuz.var |
Maximum variance of the fuzzy nucleosomes. This allow to set
different variance in well-positioned and fuzzy nucleosome reads (using
|
max.cover |
Maximum coverage of a nucleosome, i.e., how many times a nucleosome read can be repeated. The final coverage probably will be higher by the addition of overlapping nucleosomes. |
nuc.len |
Nucleosome length. It's not recomended change the default 147bp value. |
lin.len |
Linker DNA length. Usually around 20 bp. |
rnd.seed |
As this model uses random distributions for the placement, by setting the rnd.seed to a known value allows to reproduce maps in different executions or computers. If you don't need this, just left it in default value. |
as.ratio |
If |
show.plot |
If |
A list with the following elements:
wp.starts Start points of well-positioned nucleosomes
wp.nreads Number of repetitions of each well positioned read
wp.reads Well positioned nucleosome reads (IRanges format),
containing the repetitions
fuz.starts Start points of the fuzzy nucleosomes
fuz.nreads Number of repetitions of each fuzzy nucleosome read
fuz.reads Fuzzy nucleosome reads (IRanges format), containing all
the repetitions
syn.reads All synthetic nucleosome reads togheter (IRanges format)
The following elements will be only returned if as.ratio=TRUE:
ctr.reads The pseudo-naked DNA (control) reads (IRanges format)
syn.ratio The calculated ratio nucleosomal/control (Rle format)
Oscar Flores [email protected]
# Generate a synthetic map with 50wp + 20fuzzy nucleosomes using fixed # random seed=1 res <- syntheticNucMap(wp.num=50, fuz.num=20, show.plot=TRUE, rnd.seed=1) # Increase the fuzzyness res <- syntheticNucMap( wp.num=50, fuz.num=20, wp.var=70, fuz.var=150, show.plot=TRUE, rnd.seed=1 ) # Calculate also a random map and get the ratio between random and # nucleosomal res <- syntheticNucMap( wp.num=50, wp.del=0, fuz.num=20, as.ratio=TRUE, show.plot=TRUE, rnd.seed=1 ) print(res) # Different reads can be accessed separately from results # Let's use this to plot the nucleosomal + the random map library(ggplot2) as <- as.vector(coverage.rpm(res$syn.reads)) bs <- as.vector(coverage.rpm(res$ctr.reads)) cs <- as.vector(res$syn.ratio) plot_data <- rbind( data.frame(x=seq_along(as), y=as, lab="nucleosomal"), data.frame(x=seq_along(bs), y=bs, lab="random"), data.frame(x=seq_along(cs), y=cs, lab="ratio") ) qplot(x=x, y=y, data=plot_data, geom="area", xlab="position", ylab="") + facet_grid(lab~., scales="free_y")# Generate a synthetic map with 50wp + 20fuzzy nucleosomes using fixed # random seed=1 res <- syntheticNucMap(wp.num=50, fuz.num=20, show.plot=TRUE, rnd.seed=1) # Increase the fuzzyness res <- syntheticNucMap( wp.num=50, fuz.num=20, wp.var=70, fuz.var=150, show.plot=TRUE, rnd.seed=1 ) # Calculate also a random map and get the ratio between random and # nucleosomal res <- syntheticNucMap( wp.num=50, wp.del=0, fuz.num=20, as.ratio=TRUE, show.plot=TRUE, rnd.seed=1 ) print(res) # Different reads can be accessed separately from results # Let's use this to plot the nucleosomal + the random map library(ggplot2) as <- as.vector(coverage.rpm(res$syn.reads)) bs <- as.vector(coverage.rpm(res$ctr.reads)) cs <- as.vector(res$syn.ratio) plot_data <- rbind( data.frame(x=seq_along(as), y=as, lab="nucleosomal"), data.frame(x=seq_along(bs), y=bs, lab="random"), data.frame(x=seq_along(cs), y=cs, lab="ratio") ) qplot(x=x, y=y, data=plot_data, geom="area", xlab="position", ylab="") + facet_grid(lab~., scales="free_y")