| Title: | Quality control for target capture experiments |
|---|---|
| Description: | Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. |
| Authors: | M. Hummel, S. Bonnin, E. Lowy, G. Roma |
| Maintainer: | Sarah Bonnin <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 4.27.0 |
| Built: | 2024-07-03 06:03:06 UTC |
| Source: | https://github.com/bioc/TEQC |
Barplot of numbers (or fractions) of reads (and targets) falling on each chromosome
chrom.barplot(reads, targets, plotchroms, col = c("darkgreen", "orange"), ylab, legendpos = "topright", ...)chrom.barplot(reads, targets, plotchroms, col = c("darkgreen", "orange"), ylab, legendpos = "topright", ...)
reads |
|
targets |
Optional |
plotchroms |
character vector specifying the chromosomes that shall be included in the plot (and their desired order) |
col |
color(s) of the bars |
ylab |
y-axis label |
legendpos |
Position of the legend. String from the list "bottomright", "bottom", "bottomleft",
"left", "topleft", "top", "topright", "right" and "center". Ignored if |
... |
graphical parameters passed to |
If targets is not specified, absolute read counts per chromosome are shown in the barplot.
If targets is provided, fractions of reads and targets are shown. For reads, this is the
fraction within the total number of reads (since reads are expected to have all the same length).
In contrast, for the targets, the fraction of targeted bases on each chromosome is calculated.
Since targets might vary in length it is reasonable to account for the actual target sizes instead
of considering merely numbers of targets per chromosome.
Barplot of reads and optionally targets per chromosome.
Manuela Hummel [email protected]
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) chrom.barplot(reads, targets)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) chrom.barplot(reads, targets)
Visualization of target coverage correlations between pairs of samples.
coverage.correlation(coveragelist, normalized = TRUE, plotfrac = 0.001, seed = 123, labels, main, pch = ".", cex.labels, cex.pch = 2, cex.main = 1.2, cex.corr, font.labels = 1, font.main = 2, ...)coverage.correlation(coveragelist, normalized = TRUE, plotfrac = 0.001, seed = 123, labels, main, pch = ".", cex.labels, cex.pch = 2, cex.main = 1.2, cex.corr, font.labels = 1, font.main = 2, ...)
coveragelist |
List where each element is the output of function |
normalized |
if |
plotfrac |
numeric value between 0 and 1. Coverages for a fraction of |
seed |
seed for random selection of |
labels |
sample names that are written in the diagonal panels; if missing, names of |
main |
main title |
pch |
plot symbol for the scatter plots |
cex.labels, cex.pch, cex.main
|
sizes of sample labels, plot symbols, main title |
cex.corr |
size of the correlation values; if missing, sizes are made proportionally to the values of (positive) correlation. |
font.labels, font.main
|
fonts for sample labels and main title |
... |
further graphical parameters, e.g. limits and symbol color for the scatter plots |
If normalized = TRUE, the function calculates normalized coverages: per-base coverages divided by
average coverage over all targeted bases. Normalized coverages are not dependent
on the absolute quantity of reads and are hence better comparable between different samples
or even different experiments.
'pairs'-style plot where upper panels show scatter plot of (a randomly chosen fraction of) coverage values for pairs of samples. The lower panels show the respective Pearson correlation coefficients, calculated using all coverage values (even if not all of them are shown in the scatter plot).
Manuela Hummel [email protected]
coverage.target, covered.k, coverage.hist,
coverage.density, coverage.uniformity, coverage.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## simulate another sample r <- sample(length(reads), 0.1 * length(reads)) reads2 <- reads[-r,,drop=TRUE] Coverage2 <- coverage.target(reads2, targets, perBase=TRUE) ## coverage uniformity plot covlist <- list(Coverage, Coverage2) coverage.correlation(covlist, plotfrac=0.1)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## simulate another sample r <- sample(length(reads), 0.1 * length(reads)) reads2 <- reads[-r,,drop=TRUE] Coverage2 <- coverage.target(reads2, targets, perBase=TRUE) ## coverage uniformity plot covlist <- list(Coverage, Coverage2) coverage.correlation(covlist, plotfrac=0.1)
Visualization of target coverage density for one or more samples.
coverage.density(coveragelist, normalized = TRUE, legend, main, xlab, col, lwd, lty, xlim, ylim, ...)coverage.density(coveragelist, normalized = TRUE, legend, main, xlab, col, lwd, lty, xlim, ylim, ...)
coveragelist |
Output of function |
normalized |
if |
legend |
legend text. If missing, names of |
main |
main title |
xlab |
x-axis label |
col |
line color(s) |
lwd |
line width(s) |
lty |
line style(s) |
xlim, ylim
|
x- and y-axis coordinate ranges |
... |
further graphical parameters passed to |
If normalized = TRUE, the function calculates normalized coverages: per-base coverages divided by
average coverage over all targeted bases. Normalized coverages are not dependent
on the absolute quantity of reads and are hence better comparable between different samples
or even different experiments.
Line plot(s) showing densities.
Manuela Hummel [email protected]
coverage.target, covered.k, coverage.hist,
coverage.uniformity, coverage.correlation, coverage.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage density coverage.density(Coverage)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage density coverage.density(Coverage)
Calculates and plots average normalized coverage per hybridization probe versus GC content of the respective probe. A smoothing spline is added to the scatter plot.
coverage.GC(coverageAll, baits, returnBaitValues = FALSE, linecol = "darkred", lwd, xlab, ylab, pch, col, cex, ...)coverage.GC(coverageAll, baits, returnBaitValues = FALSE, linecol = "darkred", lwd, xlab, ylab, pch, col, cex, ...)
coverageAll |
|
baits |
A |
returnBaitValues |
if |
linecol, lwd
|
color and width of spline curve |
xlab, ylab
|
x- and y-axis labels |
pch |
plotting character |
col, cex
|
color and size of plotting character |
... |
further graphical parameters passed to |
The function calculates average normalized coverages for each bait: the average coverage over all bases within a bait is divided by the average coverage over all bait-covered bases. Normalized coverages are not dependent on the absolute quantity of reads and are hence better comparable between different samples or even different experiments.
A scatterplot with normalized per-bait coverages on the y-axis and GC content of respective baits on the x-axis. A smoothing spline is added to the plot.
If returnBaitValues = TRUE average coverage, average normalized coverage and GC content per bait are returned
as 'values' columns of the baits input RangedData table
Manuela Hummel [email protected]
Tewhey R, Nakano M, Wang X, Pabon-Pena C, Novak B, Giuffre A, Lin E, Happe S, Roberts DN, LeProust EM, Topol EJ, Harismendy O, Frazer KA. Enrichment of sequencing targets from the human genome by solution hybridization. Genome Biol. 2009; 10(10): R116.
coverage.target, covered.k, coverage.hist, coverage.plot,
coverage.uniformity, coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## get bait positions and sequences baitsfile <- file.path(exptPath, "ExampleSet_Baits.txt") baits <- get.baits(baitsfile, chrcol=3, startcol=4, endcol=5, seqcol=2) ## do coverage vs GC plot coverage.GC(Coverage$coverageAll, baits)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## get bait positions and sequences baitsfile <- file.path(exptPath, "ExampleSet_Baits.txt") baits <- get.baits(baitsfile, chrcol=3, startcol=4, endcol=5, seqcol=2) ## do coverage vs GC plot coverage.GC(Coverage$coverageAll, baits)
Histogram and cumulative density of target base coverages
coverage.hist(coverageTarget, col.hist = "lightblue", col.line = "orange", covthreshold, outline = FALSE, breaks = "Sturges", xlab, ylab, main, lwd, ...)coverage.hist(coverageTarget, col.hist = "lightblue", col.line = "orange", covthreshold, outline = FALSE, breaks = "Sturges", xlab, ylab, main, lwd, ...)
coverageTarget |
|
col.hist |
histogram color |
col.line |
color of the cumulative density line |
covthreshold |
indicates with dashed vertical and horizontal lines, which fraction of bases
has a coverage of at least |
outline |
if |
breaks |
number of cells for the histogram, or string naming an algorithm to compute
the number of cells, or function to compute the number of cells,
or vector giving the breakpoints between histogram cells (see |
xlab, ylab
|
x- and y-axis labels |
main |
plot title |
lwd |
line width |
... |
further graphical parameters, passed to |
Histogram of read coverages for bases within the target. Additionally, a line and the right
axis indicate the cumulative fraction of target bases with coverage of at least x.
If option covthreshold is specified, red dashed lines highlight the cumulative fraction
of target bases with at least the specified coverage.
Manuela Hummel [email protected]
coverage.target, coverage.uniformity, coverage.density, coverage.plot,
coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage histogram coverage.hist(Coverage$coverageTarget, covthreshold=8)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage histogram coverage.hist(Coverage$coverageTarget, covthreshold=8)
Line plot of per-base coverages along a genomic region. Position of target regions can be shown.
coverage.plot(coverageAll, targets, chr, Start, End, Offset = 0, add = FALSE, col.line = 1, col.target = "orange", col.offset = "yellow", xlab, ylab, ylim, ...)coverage.plot(coverageAll, targets, chr, Start, End, Offset = 0, add = FALSE, col.line = 1, col.target = "orange", col.offset = "yellow", xlab, ylab, ylim, ...)
coverageAll |
|
targets |
optional; |
chr |
on which chromosome the region to plot is located (string, e.g. "chr1") |
Start |
genomic position where to start the plot |
End |
genomic position where to end the plot |
Offset |
integer; highlight |
add |
if |
col.line |
color of the coverage line |
col.target |
color of the bar indicating target regions |
col.offset |
color for highlighting |
xlab, ylab
|
x- and y-axis labels |
ylim |
y-axis coordinate ranges |
... |
further graphical parameters, passed to |
If coverage of a new sample is added to an existing plot with add = TRUE, parameters
chr, Start, End still have to be specified and should be the same as
in the previous call in order to make sense. Parameters targets and Offset can but
do not have to be given again. They can also differ from the previous ones, if for the additional sample
a different target was captured.
Line plot showing per-base read coverages for a specified genomic region. When positions of target regions are provided, a bar on the bottom indicates their location such that coverage can be related to the captured targets.
Manuela Hummel [email protected]
coverage.target, make.wigfiles, covered.k,
coverage.hist, coverage.uniformity, coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage plot coverage.plot(Coverage$coverageAll, targets, Offset=100, chr="chr1", Start=11157524, End=11158764)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage plot coverage.plot(Coverage$coverageAll, targets, Offset=100, chr="chr1", Start=11157524, End=11158764)
Calculates average coverage over all target bases, average coverage for each target separately, and per-base coverage for all and for targeted bases
coverage.target(reads, targets, Offset = 0, perTarget = TRUE, perBase = TRUE)coverage.target(reads, targets, Offset = 0, perTarget = TRUE, perBase = TRUE)
reads |
|
targets |
|
Offset |
integer; add |
perTarget |
if TRUE, coverage average and standard deviation per target are calculated and returned |
perBase |
if TRUE, the per-base coverages i) only for targeted bases and ii) for all sequenced and/or targeted bases, are returned |
A list is returned with elements
avgTargetCoverage |
average coverage over all target bases |
targetCoverageSD |
standard deviation of coverage of all target bases |
targetCoverageQuantiles |
0% (minium), 25%, 50% (median), 75% and 100% (maximum) quantiles of coverage of all target bases |
targetCoverages |
Input |
coverageAll |
|
coverageTarget |
|
Manuela Hummel [email protected]
covered.k, coverage.hist, coverage.uniformity, coverage.plot,
coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## total average, per-base and per-target coverages Coverage <- coverage.target(reads, targets)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## total average, per-base and per-target coverages Coverage <- coverage.target(reads, targets)
Plots either numbers of on-target reads or average per-target coverage (or potentially other per-target values) against respective target lengths. A smoothing spline is added to the scatter plot.
coverage.targetlength.plot(targets, plotcolumn, linecol = 2, xlab, ylab, lwd, pch, cex, ...)coverage.targetlength.plot(targets, plotcolumn, linecol = 2, xlab, ylab, lwd, pch, cex, ...)
targets |
|
plotcolumn |
name or index of column to plot (of the 'values' DataFrame within |
linecol |
color of spline curve |
xlab, ylab
|
x- and y-axis labels |
lwd |
line width of spline curve |
pch |
plotting character |
cex |
size of plotting character |
... |
further graphical parameters, passed to |
coverage.target and readsPerTarget can be used to calculate
average per-target coverages and numbers of reads overlapping each target. The values are
added to the RangedData table containing the target positions. Such RangedData
table can then be used for plotting the calculated values against the respecitve target lengths.
A scatterplot with the given per-target values on the y-axis and corresponding target lengths on the x-axis. A smoothing spline is added to the plot.
Manuela Hummel [email protected]
coverage.target, readsPerTarget, covered.k, coverage.hist,
coverage.uniformity, coverage.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## get average per-target coverage Coverage <- coverage.target(reads, targets, perTarget=TRUE) targets2 <- Coverage$targetCoverages ## get numbers of reads per target targets2 <- readsPerTarget(reads, targets2) ## coverage vs target length coverage.targetlength.plot(targets2, plotcolumn="avgCoverage", pch="o") ## coverage vs number of reads per target coverage.targetlength.plot(targets2, plotcolumn="nReads", pch="o")## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## get average per-target coverage Coverage <- coverage.target(reads, targets, perTarget=TRUE) targets2 <- Coverage$targetCoverages ## get numbers of reads per target targets2 <- readsPerTarget(reads, targets2) ## coverage vs target length coverage.targetlength.plot(targets2, plotcolumn="avgCoverage", pch="o") ## coverage vs number of reads per target coverage.targetlength.plot(targets2, plotcolumn="nReads", pch="o")
Visualization of target coverage uniformity. A line shows the cumulative fraction of targeted bases that reach at least a certain normalized coverage.
coverage.uniformity(coveragelist, addlines = TRUE, add = FALSE, xlab, ylab, xlim, ylim, col, lwd, ...)coverage.uniformity(coveragelist, addlines = TRUE, add = FALSE, xlab, ylab, xlim, ylim, col, lwd, ...)
coveragelist |
output of function |
addlines |
if |
add |
if |
xlab, ylab
|
x- and y-axis labels |
xlim, ylim
|
x- and y-axis coordinate ranges |
col |
line color |
lwd |
line width |
... |
further graphical parameters passed to |
The function calculates normalized coverages: per-base coverages divided by average coverage over all targeted bases. Normalized coverages are not dependent on the absolute quantity of reads and are hence better comparable between different samples or even different experiments.
Line plot showing the fraction of targeted bases (y-axis) achieving a normalized
coverage of at least x. The x-axis by default is truncated at 1, which corresponds to the average
normalized coverage. The steeper the curve is falling, the less uniform is the coverage.
If addlines = TRUE, dashed lines indicate the fractions of bases achieving at
least the average (=1) or at least half (=0.5) the average coverage.
Manuela Hummel [email protected]
Gnirke A, Melnikov A, Maguire J, Rogov P, LeProust EM, Brockman W, Fennell T, Giannoukos G, Fisher S, Russ C, Gabriel S, Jaffe DB, Lander ES, Nusbaum C. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol. 2009; 27(2): 182-9.
coverage.target, covered.k, coverage.hist, coverage.density,
coverage.plot, coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage uniformity plot coverage.uniformity(Coverage)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## coverage uniformity plot coverage.uniformity(Coverage)
Calculates fraction of target bases covered by at least k reads
covered.k(coverageTarget, k = c(1, 2, 3, 5, 10, 20))covered.k(coverageTarget, k = c(1, 2, 3, 5, 10, 20))
coverageTarget |
|
k |
integer vector of |
Named vector of same length as k giving the corresponding fractions of target bases
achieving coverages >= k
Manuela Hummel [email protected]
coverage.target, coverage.hist, coverage.uniformity,
coverage.plot, coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) covered.k(Coverage$coverageTarget, k=c(1,10,20))## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) covered.k(Coverage$coverageTarget, k=c(1,10,20))
Barplot showing fractions of reads / read pairs which are unique and for which there are two, three, ... copies. Separate bars are made for on- and off-target reads / read pairs
duplicates.barplot(reads, targets, returnDups=FALSE, truncateX, col=c("red","lightblue"), xlab, ylab, ylim, ...)duplicates.barplot(reads, targets, returnDups=FALSE, truncateX, col=c("red","lightblue"), xlab, ylab, ylim, ...)
reads |
|
targets |
|
returnDups |
if |
truncateX |
integer; show bars only up to a read / read pair multiplicity of |
col |
vector specifying the two colors of bars and legend for on- and off-target read multiplicities |
xlab, ylab
|
x- and y-axis labels |
ylim |
y-axis coordinate ranges |
... |
further graphical parameters passed to |
Single-end reads are considered as duplicates if they have same start end end position. Paired-end read pairs are considered as duplicates if start and end positions of both reads of the pairs are identical. Usually, duplicates are removed before further analyses (e.g. SNP detection), because they could represent PCR artefacts. However, in target capture experiments it is likely to have also many "real" duplicates (actual different molecules that happen to start at same position) due to the "enrichment" of the target regions. The separation in the barplot between on- and off-target reads / read pairs gives an impression on whether on-target there are more reads with higher multiplicites, which hence might indicate a reasonable amount of "real" duplication. A paired-end read pair is considered on-target if at least one of its reads overlaps with a target.
Barplot where the bar heights correspond to fractions of reads / read pairs which are present in the data with the respective number of copies (x-axis). Fractions are calculated separately for on- and off-target reads / read pairs. A read pair is considered on-target if at least one of its reads overlaps with a target. Absolute numbers (in millions) are additionally written on top of the bars.
If returnDups equals TRUE, a list with two elements absolute and
relative is returned. The former is a matrix that contains the absolute numbers of reads / read pairs
for each multiplicity (columns), for both on- and off-target reads / read pairs (rows).
The latter gives row-based fractions which correspond to the bar heights.
Manuela Hummel [email protected]
get.reads, reads2pairs, get.targets
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## duplicates barplot for single reads duplicates.barplot(reads, targets, returnDups=TRUE) ## duplicates barplot for read pairs readpairs <- reads2pairs(reads) duplicates.barplot(readpairs, targets, returnDups=TRUE)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## duplicates barplot for single reads duplicates.barplot(reads, targets, returnDups=TRUE) ## duplicates barplot for read pairs readpairs <- reads2pairs(reads) duplicates.barplot(readpairs, targets, returnDups=TRUE)
Calculates the fraction of reads that align to target regions. Can also be used to retrieve those reads mapping to targets.
fraction.reads.target(reads, targets, Offset = 0, mappingReads = FALSE)fraction.reads.target(reads, targets, Offset = 0, mappingReads = FALSE)
reads |
|
targets |
|
Offset |
integer; add |
mappingReads |
if |
If mappingReads equals FALSE, just the fraction of reads / read pairs mapping to targets is returned.
When reads contains all single reads (i.e. is output of get.reads), this is the number of target-overlapping reads,
divided by the number of all single reads. When reads contains read pairs (i.e. is output of reads2pairs),
it is the number of read pairs with at least one target-overlapping read, divided by the
number of read pairs (= half the number of reads). In case of small targets and large insert sizes
the two reads of a pair could be located on both sides of the target without overlap, respectively.
Still, the read pair will be counted as on-target, since the corresponding DNA molecule was covering the target.
If mappingReads equals TRUE, a list is returned with elements
onTargetFraction |
fraction of reads / read pairs mapping to targets |
mappingReads |
|
With the output from fraction.target and fraction.reads.target
the 'enrichment' of the target capture experiment can be calculated as
'fraction of on-target reads / fraction of target within genome'
Manuela Hummel [email protected]
fraction.target, get.reads, reads2pairs, get.targets
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## fraction of on-target reads fraction.reads.target(reads, targets)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## fraction of on-target reads fraction.reads.target(reads, targets)
Calculates the fraction of the reference genome that is targeted
fraction.target(targets, Offset = 0, genome = c(NA, "hg38", "hg19", "hg18"), genomesize)fraction.target(targets, Offset = 0, genome = c(NA, "hg38", "hg19", "hg18"), genomesize)
targets |
|
Offset |
integer; add |
genome |
genome version targets were designed and reads aligned to. For the given options the total genome size is set automatically. For other genomes or versions, leave this option empty ('NA') and specify the genome size with option 'genomesize' |
genomesize |
integer: specify the total genome size manually. If 'genomesize' is given, option 'genome' will be ignored. |
Returns the fraction of nucleotides within the genome that were targeted.
With the output from fraction.target and fraction.reads.target
the 'enrichment' of the target capture experiment can be calculated as
'fraction of on-target reads / fraction of target within genome'
Manuela Hummel [email protected]
fraction.reads.target, get.targets
exptPath <- system.file("extdata", package="TEQC") targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) fraction.target(targets, genome="hg19")exptPath <- system.file("extdata", package="TEQC") targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) fraction.target(targets, genome="hg19")
Reads a file containing positions and sequences of the capture hybridization probes
and creates a RangedData object.
get.baits(baitsfile, chrcol = 1, startcol = 2, endcol = 3, seqcol = 4, zerobased = TRUE, sep = "\t", header = TRUE, ...)get.baits(baitsfile, chrcol = 1, startcol = 2, endcol = 3, seqcol = 4, zerobased = TRUE, sep = "\t", header = TRUE, ...)
baitsfile |
name of file giving the positions and sequences of each hybridization probe ("bait") |
chrcol |
in which column in |
startcol |
in which column there are the starting positions of the baits |
endcol |
in which column there are the end positions of the baits |
seqcol |
in which column there are the sequences of the baits |
zerobased |
if |
sep |
column separator character, defaults to tabs |
header |
a logical value indicating whether the file contains the names of the variables as its first line; defaults to FALSE |
... |
further arguments passed to |
The baitsfile containing positions and sequences of hybridization probes
has to be created beforehand, in many cases manually. (The function was made like this in order to
keep things as general and platform independent as possible.) E.g. with baits designed by
Agilent's eArray tool, the baitsfile can be created by merging the files
'..._D_BED_...bed' and '..._D_DNAFront_BCBottom_...txt'.
A RangedData table holding the hybridization probe
("bait") positions and sequences. Overlapping or adjacent baits are not collapsed.
Manuela Hummel [email protected]
exptPath <- system.file("extdata", package="TEQC") baitsfile <- file.path(exptPath, "ExampleSet_Baits.txt") baits <- get.baits(baitsfile, chrcol=3, startcol=4, endcol=5, seqcol=2)exptPath <- system.file("extdata", package="TEQC") baitsfile <- file.path(exptPath, "ExampleSet_Baits.txt") baits <- get.baits(baitsfile, chrcol=3, startcol=4, endcol=5, seqcol=2)
Reads a bedfile containing positions of sequenced read aligned to a reference genome
and creates a RangedData object.
get.reads(readsfile, filetype = c("bed", "bam"), chrcol = 1, startcol = 2, endcol = 3, idcol, zerobased = TRUE, sep = "\t", skip = 1, header = FALSE, ...)get.reads(readsfile, filetype = c("bed", "bam"), chrcol = 1, startcol = 2, endcol = 3, idcol, zerobased = TRUE, sep = "\t", skip = 1, header = FALSE, ...)
readsfile |
name of bedfile giving the positions of aligned reads |
#!!
filetype |
Input file type. If |
# !!
chrcol |
In which column in the reads bedfile there is the chromosome information
(chromosome information in the file should be in string format, e.g. "chrX").
Ignored if |
startcol |
In which column there are the starting positions of the reads.
Ignored if |
endcol |
In which column there are the end positions of the reads.
Ignored if |
idcol |
In which column there are read identifiers. For single-end data it is optionally.
For paired-end data it is required for some functionalities. The two reads of one pair need to have the same ID.
Ignored if |
zerobased |
if |
sep |
Column separator character, defaults to tabs. Ignored if |
skip |
Number of lines of the bedfile to skip before beginning to read data; defaults to 1.
Ignored if |
header |
A logical value indicating whether the file contains the names of the variables as its first line;
defaults to FALSE. Ignored if |
... |
Further arguments passed to |
A RangedData table holding the read positions
Manuela Hummel [email protected]
exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0)exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0)
Reads a bedfile containing positions of the capture targets and creates a RangedData object.
get.targets(targetsfile, chrcol = 1, startcol = 2, endcol = 3, zerobased = TRUE, sep = "\t", skip = 1, header = FALSE, ...)get.targets(targetsfile, chrcol = 1, startcol = 2, endcol = 3, zerobased = TRUE, sep = "\t", skip = 1, header = FALSE, ...)
targetsfile |
name of bedfile giving the positions of each target region |
chrcol |
in which column in the targets bedfile there is the chromosome information (chromosome information in the file should be in string format, e.g. "chrX") |
startcol |
in which column there are the starting positions of the targeted regions |
endcol |
in which column there are the end positions of the targeted regions |
zerobased |
if |
sep |
column separator character, defaults to tabs |
skip |
number of lines of the bedfile to skip before beginning to read data; defaults to 1 |
header |
a logical value indicating whether the file contains the names of the variables as its first line; defaults to FALSE |
... |
further arguments passed to |
A RangedData table holding the target region positions. Note that overlapping or
adjacent regions are collapsed to one region.
Since overlapping regions are collapsed, the input bedfile can also contain positions of the (in most cases overlapping) hybridization probes used for the target capture.
Manuela Hummel [email protected]
exptPath <- system.file("extdata", package="TEQC") targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0)exptPath <- system.file("extdata", package="TEQC") targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0)
Computes read pair insert sizes, i.e. distance from first base of first read to last base of second read of a read pair, and plots a histogram for all insert sizes.
insert.size.hist(readpairs, returnInserts = FALSE, legendpos="topleft", outline=FALSE, main, xlab, ylab, breaks, col, ...)insert.size.hist(readpairs, returnInserts = FALSE, legendpos="topleft", outline=FALSE, main, xlab, ylab, breaks, col, ...)
readpairs |
|
returnInserts |
if |
legendpos |
position of the legend, e.g. 'topleft' or 'topright' |
outline |
if |
main |
plot title |
xlab, ylab
|
x- and y-axis labels |
breaks |
e.g. integer specifying the number of cells for the histogram, see |
col |
histogram color |
... |
further graphical parameters passed to |
Histogram of read pair insert sizes. Average, standard deviation and median insert size are given in the legend and indicated by lines.
If returnInserts = TRUE, a named vector of insert sizes is returned.
Manuela Hummel [email protected]
get.reads, reads2pairs, duplicates.barplot
## get reads exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) ## merge to read pairs readpairs <- reads2pairs(reads) ## insert size histogram insert.size.hist(readpairs, breaks=10)## get reads exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) ## merge to read pairs readpairs <- reads2pairs(reads) ## insert size histogram insert.size.hist(readpairs, breaks=10)
Prepares wiggle files with (non-zero) per-base coverages for the upload and visualization with genome browsers
make.wigfiles(coverageAll, chroms, trackname = "Coverage", filename = "Coverage")make.wigfiles(coverageAll, chroms, trackname = "Coverage", filename = "Coverage")
coverageAll |
|
chroms |
vector of chromosome names for which to produce wiggle files; if missing wiggle files will be produced for all chromosomes on which there are reads |
trackname |
trackname for wiggle file header |
filename |
part of output wiggle file name. Respective chromosome number and '.wig' will be added |
Only non-zero coverages will be listed
One or more wiggle files listing per-base (non-zero) read coverages
Manuela Hummel [email protected]
coverage.target, coverage.plot, covered.k,
coverage.hist, coverage.uniformity, coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## create wiggle files for read coverages on chromsomes 13 and 17 make.wigfiles(Coverage$coverageAll, chroms=c("chr13", "chr17"))## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## calculate per-base coverages Coverage <- coverage.target(reads, targets, perBase=TRUE) ## create wiggle files for read coverages on chromsomes 13 and 17 make.wigfiles(Coverage$coverageAll, chroms=c("chr13", "chr17"))
Creates an automated html report comparing TEQC analysis results of several samples
multiTEQCreport(singleReportDirs, samplenames, projectName = "", targetsName = "", referenceName = "", destDir = "multiTEQCreport", k = c(1, 2, 3, 5, 10, 20), figureFormat = c("jpeg", "png", "tiff"))multiTEQCreport(singleReportDirs, samplenames, projectName = "", targetsName = "", referenceName = "", destDir = "multiTEQCreport", k = c(1, 2, 3, 5, 10, 20), figureFormat = c("jpeg", "png", "tiff"))
singleReportDirs |
string of directory names; output directories of function TEQCreport(), launched beforehand for each single sample |
samplenames |
names of the samples that will be used in tables and figures |
projectName |
descriptive name for the project / collection of samples; will be written on top of the html report |
targetsName |
descriptive name of the captured target; will be written on top of the html report |
referenceName |
descriptive name of the reference genome the reads were aligned against; will be written on top of the html report |
destDir |
directory where results and html documents shall be saved |
k |
integer vector of |
figureFormat |
format of the figures produced for the html report (besides pdf graphs) |
Before creating the html report for multiple samples, TEQCreport has to be run for each of the samples separately.
The output directories of those analyses are the input for multiTEQCreport. While the creation of single-sample reports is time and memory intensive, multiTEQCreport finishes quickly, since it just collects and summarizes the results from the single analyses.
The files for the multiple sample html report are created in destDir.
The report can be viewed by opening destDir/index.html in a web browser. Images are saved in
destDir/image.
The function is invoked for its side effect
Manuela Hummel [email protected]
Hummel M, Bonnin S, Lowy E, Roma G. TEQC: an R-package for quality control in target capture experiments. Bioinformatics 2011; 27(9):1316-7.
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, skip=0, idcol=4) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## simulated 2nd sample r <- sample(length(reads), 0.1 * length(reads)) reads2 <- reads[-r,,drop=TRUE] ## Not run: ## create single-sample reports TEQCreport(sampleName="Test Sample A", targetsName="Human Exome", referenceName="Human Genome", destDir="./reportA", reads=reads, targets=targets, genome="hg19") TEQCreport(sampleName="Test Sample B", targetsName="Human Exome", referenceName="Human Genome", destDir="./reportB", reads=reads2, targets=targets, genome="hg19") ## create multi-sample report multiTEQCreport(singleReportDirs=c("./reportA", "./reportB"), samplenames=c("Sample A","Sample B"), projectName="Test Project", targetsName="Human Exome", referenceName="Human Genome", destDir="./multiTEQCreport") ## End(Not run)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, skip=0, idcol=4) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## simulated 2nd sample r <- sample(length(reads), 0.1 * length(reads)) reads2 <- reads[-r,,drop=TRUE] ## Not run: ## create single-sample reports TEQCreport(sampleName="Test Sample A", targetsName="Human Exome", referenceName="Human Genome", destDir="./reportA", reads=reads, targets=targets, genome="hg19") TEQCreport(sampleName="Test Sample B", targetsName="Human Exome", referenceName="Human Genome", destDir="./reportB", reads=reads2, targets=targets, genome="hg19") ## create multi-sample report multiTEQCreport(singleReportDirs=c("./reportA", "./reportB"), samplenames=c("Sample A","Sample B"), projectName="Test Project", targetsName="Human Exome", referenceName="Human Genome", destDir="./multiTEQCreport") ## End(Not run)
Combines the two reads of a read pair (in case of paired-end data) to a new 'range' starting at the first reads's start position and ending at the second read's end position.
reads2pairs(reads, max.distance)reads2pairs(reads, max.distance)
reads |
|
max.distance |
Integer value defining the maximum allowed distance between two reads of a pair
(from start position of first read to end position of second read). Reads exceeding this
distance will be returned in the separate table |
The function puts together the two reads of each pair and creates new ranges spanning both
reads and everything in between. Those ranges correspond to the extent of the actual DNA molecules
for which both ends were sequenced. The output of the function can be used by several other functions,
whenever calculations should be based on read pairs rather than on single reads, e.g.
fraction.reads.target, readsPerTarget, duplicates.barplot
If reads only contains complete read pairs and for all pairs the respective reads
align to the same chromosome and their distances do not exceed max.distance (if specified),
a RangedData
object is returned containing positions of the merged reads per pair, ranging from start
position of the first read to end position of the second read.
If reads also contains single reads, or if reads within a pair are further apart than
max.distance (if specified) or align to different chromosome, a list is returned with elements
singleReads |
|
readpairs |
|
Manuela Hummel [email protected]
get.reads, fraction.reads.target,
readsPerTarget, duplicates.barplot, insert.size.hist
exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) readpairs <- reads2pairs(reads)exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) readpairs <- reads2pairs(reads)
Counts the numbers of reads overlapping each target region
readsPerTarget(reads, targets, Offset = 0)readsPerTarget(reads, targets, Offset = 0)
reads |
|
targets |
|
Offset |
integer; add |
The input RangedData table targets with an additional 'values'
column containing numbers of reads overlapping each target
As reads input also the mappingReads output of function fraction.reads.target
can be used to speed up calculation. In this case, make sure that targets and Offset parameters were the
same in fraction.reads.target as then specified in readsPerTarget.
Manuela Hummel [email protected]
coverage.target, fraction.reads.target, covered.k, coverage.hist,
coverage.uniformity, coverage.plot, coverage.targetlength.plot
## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## number of reads per target readsPerTarget(reads, targets)## get reads and targets exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") reads <- get.reads(readsfile, idcol=4, skip=0) targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") targets <- get.targets(targetsfile, skip=0) ## number of reads per target readsPerTarget(reads, targets)
Creates an automated html report for the complete TEQC analysis of one sample
TEQCreport(sampleName = "", targetsName = "", referenceName = "", destDir = "TEQCreport", reads = get.reads(), targets = get.targets(), Offset = 0, pairedend = FALSE, genome = c(NA, "hg38", "hg19", "hg18"), genomesize, k=c(1, 2, 3, 5, 10, 20), covthreshold = 8, CovUniformityPlot = FALSE, CovTargetLengthPlot = FALSE, CovGCPlot = FALSE, duplicatesPlot = FALSE, baits = get.baits(), WigFiles = FALSE, saveWorkspace = FALSE, figureFormat = c("jpeg", "png", "tiff"))TEQCreport(sampleName = "", targetsName = "", referenceName = "", destDir = "TEQCreport", reads = get.reads(), targets = get.targets(), Offset = 0, pairedend = FALSE, genome = c(NA, "hg38", "hg19", "hg18"), genomesize, k=c(1, 2, 3, 5, 10, 20), covthreshold = 8, CovUniformityPlot = FALSE, CovTargetLengthPlot = FALSE, CovGCPlot = FALSE, duplicatesPlot = FALSE, baits = get.baits(), WigFiles = FALSE, saveWorkspace = FALSE, figureFormat = c("jpeg", "png", "tiff"))
sampleName |
descriptive sample name; will be written on top of the html report |
targetsName |
descriptive name of the captured target; will be written on top of the html report |
referenceName |
descriptive name of the reference genome the reads were aligned against; will be written on top of the html report |
destDir |
directory where results and html documents shall be saved |
reads |
|
targets |
|
Offset |
integer; add |
pairedend |
if |
genome |
genome version targets were designed and reads aligned to. For the given options the total genome size is set automatically. For other genomes or versions, leave this option empty ('NA') and specify the genome size with option 'genomesize' |
genomesize |
integer: specify the total genome size manually. If 'genomesize' is given, option 'genome' will be ignored. |
k |
integer vector of |
covthreshold |
integer indicating which coverage value shall be highlighted by dashed lines in the coverage histogram. Passed to |
CovUniformityPlot |
if |
CovTargetLengthPlot |
if |
CovGCPlot |
if |
duplicatesPlot |
if |
baits |
A |
WigFiles |
if |
saveWorkspace |
if |
figureFormat |
format of the figures produced for the html report (besides pdf graphs) |
TEQC analysis is performed and files for an html report are created in destDir.
The report can be viewed by opening destDir/index.html in a web browser. Images are saved in
destDir/image. Wiggle files (in case WigFiles = TRUE) are saved in
destDir/wiggle. A table with general target coverage statistics, a table with average coverage values per target, a table with cumulative fractions of targeted bases with certain coverage and the R workspace
containing R objects for potential further analysis (in case saveWorkspace = TRUE)
are saved in destDir.
The function is invoked for its side effect
Manuela Hummel [email protected]
Hummel M, Bonnin S, Lowy E, Roma G. TEQC: an R-package for quality control in target capture experiments. Bioinformatics 2011; 27(9):1316-1317
get.reads, get.targets, fraction.target, fraction.reads.target,
coverage.target, readsPerTarget, reads2pairs,
covered.k, coverage.hist, coverage.uniformity,
coverage.targetlength.plot, coverage.GC, get.baits,
make.wigfiles
## get reads and targets files exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") ## create report ## Not run: TEQCreport(sampleName="Test Sample", targetsName="Human Exome", referenceName="Human Genome", destDir="report", reads=get.reads(readsfile, skip=0, idcol=4), targets=get.targets(targetsfile, skip=0), genome="hg19") ## End(Not run)## get reads and targets files exptPath <- system.file("extdata", package="TEQC") readsfile <- file.path(exptPath, "ExampleSet_Reads.bed") targetsfile <- file.path(exptPath, "ExampleSet_Targets.bed") ## create report ## Not run: TEQCreport(sampleName="Test Sample", targetsName="Human Exome", referenceName="Human Genome", destDir="report", reads=get.reads(readsfile, skip=0, idcol=4), targets=get.targets(targetsfile, skip=0), genome="hg19") ## End(Not run)