The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as “peaks”), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC is a method for joint analysis of weak peaks. Given a set of peaks from (biological or technical) replicates, MSPC combines the p-values of overlapping enriched regions, and allows the “rescue” of weak peaks occurring in more than one replicate. In other words, MSPC comparatively evaluates ChIP-seq peaks and combines the statistical significance of repeated evidences, with the aim of lowering the minimum significance required to “rescue” weak peaks; hence reducing false-negatives.
The MSPC method is implemented as a C# .NET library with two interfaces: A cross-platform command line interface, and an R package. Both interfaces are built on the same library, hence they provide the same functionality and produce the same output.
The documentation for running the package from a terminal on Linux,
macOS, and Windows is available at
this page.
The present package, rmspc, is developed to run MSPC from R.
The package facilitates usage and integration of MSPC with
pipelines/scripts written in R. This document explores how
to use MSPC program from Rstudio, using the rmspc package.
A prerequisite for the rmspc package is .NET 5.0 (since it is developed
on program implemented in C# .NET). You can check if .NET is installed
using the command dotnet --info run in a terminal.
If .NET is installed, the output of the command shows the version
of the installed framework (should be 5.0 or newer). If not installed,
you may install following
these instructions.
The installation of the package goes as follows :
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("rmspc")If the package BiocManager is not installed in the user’s computer, it will be installed as well.
A package only needs to be installed once. We load the rmspc package
into an R session :
library(rmspc)The package has one external function: mspc. This is the main function
of the package that we use to run the MSPC program in R.
There are 4 required arguments that the user needs to specify to run the
mspc function.
These arguments are:
input: Character vector (file path of BED files) or a GRanges list.
replicateType: Character string. This argument defines the replicate
type. Possible values : ‘Bio’, ‘Biological’, ‘Tec’, ‘Technical’.
stringencyThreshold: A real number of type Double. A threshold
on p-values, where peaks with p-value lower than this threshold are
considered stringent peaks.
weakThreshold: A real number of type Double. A threshold on
p-values, such that peaks with p-value between this and the stringency
threshold are considered weak peaks.
More information about the arguments of the mspc function can be
found in the package documentation.
It is important to note that the input argument can be given in
two possible formats.
In this first scenario, we suppose the samples we want to use as
input data for the mspc function are in a BED file format. We
will use for this example the external data available in the package.
path <- system.file("extdata", package = "rmspc")We have two sample files available in the directory inst/extdata of the package :
list.files(path)## [1] "rep1.bed" "rep2.bed"More information about these sample files is available in the data documentation file.
We specify the input argument. In this first scenario, the input argument is a character vector. Each element of the vector is a file path of a BED file.
input1 <- paste0(path, "/rep1.bed")
input2 <- paste0(path, "/rep2.bed")
input <- c(input1, input2)
input## [1] "/tmp/RtmpSFLJGq/Rinst1d1b2c2e3fd19c/rmspc/extdata/rep1.bed"
## [2] "/tmp/RtmpSFLJGq/Rinst1d1b2c2e3fd19c/rmspc/extdata/rep2.bed"When the mspc function is called, it creates a number of files in
the user’s computer. If the user wishes to keep all the files
generated in their computer, they can set the argument keep to TRUE.
More information regarding this argument is available in the documentation.
We run the mspc function as follows :
results <- mspc(
input = input, replicateType = "Technical",
stringencyThreshold = 1e-8,
weakThreshold = 1e-4, gamma = 1e-8,
keep = FALSE,GRanges = TRUE,
multipleIntersections = "Lowest",
c = 2,alpha = 0.05)## Export Directory: /tmp/RtmpxfsZf3_0
## Degree of parallelism is set to 72.
##
## .::...Parsing Samples....::.
## # Filename Read peaks# Min p-value Mean p-value Max p-value
## ---- -------------------- ----------- ----------- ------------ -----------
## 1/2 rep1 5,458 5.012E-071 1.215E-003 1.000E-002
## 2/2 rep2 4,119 6.607E-239 1.778E-004 9.550E-003
##
## .::..Analyzing Samples...::.
## [1/4] Initializing
## [2/4] Processing samples
##
└── 0/6,045 (0.000%) peaks
└── 2,657/6,045 (43.954%) peaks processed
└── 6,045/6,045 (100.000%) peaks processed
## [3/4] Performing Multiple testing correction
## [4/4] Creating consensus peaks set
##
## .::....Saving Results....::.
##
## .::..Summary Statistics..::.
## # Filename Read peaks# Background Weak Stringent Confirmed Discarded TruePositive FalsePositive
## ---- -------------------- ----------- ---------- -------- --------- --------- --------- ------------ -------------
## 1/2 rep1 5,458 51.319% 39.080% 9.601% 27.318% 21.363% 27.318% 0.000%
## 2/2 rep2 4,119 17.747% 48.216% 34.037% 30.104% 52.149% 30.104% 0.000%
##
## .::.Consensus Peaks Count.::.
## 1,239
##
##
## Elapsed time: 00:00:16.7525580
## All processes successfully finished.The mspc function prints the results of the MSPC program.
The first line of the output printed gives the exported directory,
which is the directory where the files generated by the
mspc function are created.
The function returns the following:
status: Integer. The exit status of running the mspc function.
A 0 exit status means the function ran successfully.filesCreated: List of character vectors. It lists the names of
the files generated while running the mspc function.GrangesObjects: GRanges list. All the files generated while
running the mspc function are imported as GRanges objects,
and are combined into a GRanges list.It is important to note that the mspc function does not
always return these three elements. The output of the function
depends on the arguments keep and GRanges given to
the mspc function.
In this example, we chose to set the argument keep to FALSE,
and GRanges to TRUE.
By doing so, we chose to ask the function to return all the
files generated as GRanges objects, but to not keep them in
the computer.
The objects returned by the mspc function in this example
are therefore :
results$status## [1] 0head(results$GRangesObjects,3)## $ConsensusPeaks
## GRanges object with 1239 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 32565-33040 * | MSPC_Peak_1239 61.843
## [2] chr1 34688-34887 * | MSPC_Peak_1238 14.490
## [3] chr1 34973-35206 * | MSPC_Peak_1237 10.042
## [4] chr1 38569-38849 * | MSPC_Peak_1236 24.609
## [5] chr1 437581-437800 * | MSPC_Peak_1235 11.529
## ... ... ... ... . ... ...
## [1235] chr1 248100316-248100550 * | MSPC_Peak_5 8.706
## [1236] chr1 249152183-249153033 * | MSPC_Peak_4 44.835
## [1237] chr1 249153179-249153549 * | MSPC_Peak_3 10.678
## [1238] chr1 249168024-249168194 * | MSPC_Peak_2 11.693
## [1239] chr1 249232854-249233102 * | MSPC_Peak_1 23.832
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $`rep1/Background`
## GRanges object with 2801 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 10000-10039 * | MACS_peak_1 2.42
## [2] chr1 10102-10190 * | MACS_peak_2 3.23
## [3] chr1 29304-29382 * | MACS_peak_3 2.44
## [4] chr1 132296-132335 * | MACS_peak_9 3.58
## [5] chr1 227521-227560 * | MACS_peak_11 2.70
## ... ... ... ... . ... ...
## [2797] chr1 248610772-248610811 * | MACS_peak_5442 2.52
## [2798] chr1 249104439-249104478 * | MACS_peak_5446 2.02
## [2799] chr1 249162600-249162639 * | MACS_peak_5452 3.67
## [2800] chr1 249219095-249219134 * | MACS_peak_5455 3.58
## [2801] chr1 249230903-249230943 * | MACS_peak_5456 3.50
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $`rep1/Confirmed`
## GRanges object with 1491 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 32601-32680 * | MACS_peak_4 4.08
## [2] chr1 32727-32936 * | MACS_peak_5 17.50
## [3] chr1 34690-34797 * | MACS_peak_6 5.82
## [4] chr1 35084-35124 * | MACS_peak_7 4.59
## [5] chr1 38594-38836 * | MACS_peak_8 10.70
## ... ... ... ... . ... ...
## [1487] chr1 249152329-249152446 * | MACS_peak_5449 10.70
## [1488] chr1 249152889-249152949 * | MACS_peak_5450 5.65
## [1489] chr1 249153389-249153472 * | MACS_peak_5451 6.28
## [1490] chr1 249168083-249168158 * | MACS_peak_5453 4.75
## [1491] chr1 249232876-249233088 * | MACS_peak_5458 7.13
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsEach element of the GRangesObjects of the output can be
accessed as such:
results$GRangesObjects$ConsensusPeaks## GRanges object with 1239 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 32565-33040 * | MSPC_Peak_1239 61.843
## [2] chr1 34688-34887 * | MSPC_Peak_1238 14.490
## [3] chr1 34973-35206 * | MSPC_Peak_1237 10.042
## [4] chr1 38569-38849 * | MSPC_Peak_1236 24.609
## [5] chr1 437581-437800 * | MSPC_Peak_1235 11.529
## ... ... ... ... . ... ...
## [1235] chr1 248100316-248100550 * | MSPC_Peak_5 8.706
## [1236] chr1 249152183-249153033 * | MSPC_Peak_4 44.835
## [1237] chr1 249153179-249153549 * | MSPC_Peak_3 10.678
## [1238] chr1 249168024-249168194 * | MSPC_Peak_2 11.693
## [1239] chr1 249232854-249233102 * | MSPC_Peak_1 23.832
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsresults$GRangesObjects$`rep1/Background`## GRanges object with 2801 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 10000-10039 * | MACS_peak_1 2.42
## [2] chr1 10102-10190 * | MACS_peak_2 3.23
## [3] chr1 29304-29382 * | MACS_peak_3 2.44
## [4] chr1 132296-132335 * | MACS_peak_9 3.58
## [5] chr1 227521-227560 * | MACS_peak_11 2.70
## ... ... ... ... . ... ...
## [2797] chr1 248610772-248610811 * | MACS_peak_5442 2.52
## [2798] chr1 249104439-249104478 * | MACS_peak_5446 2.02
## [2799] chr1 249162600-249162639 * | MACS_peak_5452 3.67
## [2800] chr1 249219095-249219134 * | MACS_peak_5455 3.58
## [2801] chr1 249230903-249230943 * | MACS_peak_5456 3.50
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsIn order to understand better the output of the mspc
function, let’s go over the files generated while
running the mspc function.
These files are listed by the filesCreated object,
returned by the mspc function :
A log file that contains the execution log : this file contains the information, debugging messages, and exceptions that occurred during the execution.
Consensus peaks in standard BED and MSPC format.
One folder per each replicates that contains BED files, containing stringent, weak, background, confirmed, discarded, true-positive, and false-positive peaks.
Each category of peaks is defined as such :
Peaks with p-value >= weakThreshold.
Peaks with stringencyThreshold <= p-value < weakThreshold.
Peaks with p-value < stringencyThreshold.
Peaks that:
c peaks from replicates, andgamma andPeaks that:
c) supporting evidence, orThe confirmed peaks that pass the Benjamini-Hochberg multiple testing
correction at level alpha.
The confirmed peaks that fail Benjamini-Hochberg multiple testing
correction at level alpha.
More information about the files generated by the mspc function can be found here.
In this second scenario, we suppose the samples we want to use
as input data for the mspc function are GRanges objects,
loaded in the R environment the user is working on.
To exemplify this scenario, we will import the BED files, included in the package, as GRanges objects into our R environment.
In order to do so, we need to install and load the two following
Bioconductor packages: GenomicRanges and rtracklayer.
BiocManager::install("GenomicRanges",dependencies = TRUE)
BiocManager::install("rtracklayer",dependencies = TRUE)We load these packages to our R session as follows:
library(GenomicRanges)
library(rtracklayer)We now import the two BED files, that are available in the folder inst/extdata of the package, as GRanges objects.
path <- system.file("extdata", package = "rmspc")
input1 <- paste0(path, "/rep1.bed")
input2 <- paste0(path, "/rep2.bed")
GR1 <- rtracklayer::import(con = input1, format = "bed")
GR2 <- rtracklayer::import(con = input2, format = "bed")We have now created 2 GRanges objects : GR1 and GR2. Here’s what the GR1 object is like:
GR1## UCSC track 'R Track'
## UCSCData object with 5458 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 10000-10039 * | MACS_peak_1 2.42
## [2] chr1 10102-10190 * | MACS_peak_2 3.23
## [3] chr1 29304-29382 * | MACS_peak_3 2.44
## [4] chr1 32601-32680 * | MACS_peak_4 4.08
## [5] chr1 32727-32936 * | MACS_peak_5 17.50
## ... ... ... ... . ... ...
## [5454] chr1 249185288-249185343 * | MACS_peak_5454 5.05
## [5455] chr1 249219095-249219134 * | MACS_peak_5455 3.58
## [5456] chr1 249230903-249230943 * | MACS_peak_5456 3.50
## [5457] chr1 249231135-249231205 * | MACS_peak_5457 5.00
## [5458] chr1 249232876-249233088 * | MACS_peak_5458 7.13
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsWe can now combine the GRanges objects, GR1 and GR2, into a GRanges list. A Granges list is a list of several GRanges objects. It is defined as such:
GR <- GenomicRanges::GRangesList("GR1" = GR1, "GR2" = GR2)
GR## GRangesList object of length 2:
## $GR1
## UCSC track 'R Track'
## UCSCData object with 5458 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 10000-10039 * | MACS_peak_1 2.42
## [2] chr1 10102-10190 * | MACS_peak_2 3.23
## [3] chr1 29304-29382 * | MACS_peak_3 2.44
## [4] chr1 32601-32680 * | MACS_peak_4 4.08
## [5] chr1 32727-32936 * | MACS_peak_5 17.50
## ... ... ... ... . ... ...
## [5454] chr1 249185288-249185343 * | MACS_peak_5454 5.05
## [5455] chr1 249219095-249219134 * | MACS_peak_5455 3.58
## [5456] chr1 249230903-249230943 * | MACS_peak_5456 3.50
## [5457] chr1 249231135-249231205 * | MACS_peak_5457 5.00
## [5458] chr1 249232876-249233088 * | MACS_peak_5458 7.13
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $GR2
## UCSC track 'R Track'
## UCSCData object with 4119 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 9933-10069 * | MACS_peak_1 7.99
## [2] chr1 11143-11340 * | MACS_peak_2 5.37
## [3] chr1 29363-29488 * | MACS_peak_3 4.42
## [4] chr1 32565-33040 * | MACS_peak_4 44.33
## [5] chr1 34688-34887 * | MACS_peak_5 10.25
## ... ... ... ... . ... ...
## [4115] chr1 249168024-249168194 * | MACS_peak_4115 8.44
## [4116] chr1 249200277-249200407 * | MACS_peak_4116 3.62
## [4117] chr1 249208247-249208409 * | MACS_peak_4117 9.33
## [4118] chr1 249221136-249221304 * | MACS_peak_4118 11.34
## [4119] chr1 249232854-249233102 * | MACS_peak_4119 18.48
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe GRanges list GR created is the input we will give
to the mspc function.
When we give a Granges list to the mspc function as input,
each GRanges object of the GRanges list is exported as a BED
file into the folder specified by the argument directoryGRangesInput.
More information about the directoryGRangesInput argument in
the documentation.
We now will call the mspc function, as follows:
results <- mspc(
input = GR, replicateType = "Biological",
stringencyThreshold = 1e-8, weakThreshold = 1e-4,
gamma = 1e-8, GRanges = TRUE, keep = FALSE,
multipleIntersections = "Highest",
c = 2,alpha = 0.05)## Export Directory: /tmp/RtmpxfsZf3_1
## Degree of parallelism is set to 72.
##
## .::...Parsing Samples....::.
## # Filename Read peaks# Min p-value Mean p-value Max p-value
## ---- -------------------- ----------- ----------- ------------ -----------
## 1/2 gr1 5,458 5.012E-071 1.215E-003 1.000E-002
## 2/2 gr2 4,119 6.607E-239 1.778E-004 9.550E-003
##
## .::..Analyzing Samples...::.
## [1/4] Initializing
## [2/4] Processing samples
##
└── 0/6,045 (0.000%) peaks
└── 2,657/6,045 (43.954%) peaks processed
└── 6,045/6,045 (100.000%) peaks processed
## [3/4] Performing Multiple testing correction
## [4/4] Creating consensus peaks set
##
## .::....Saving Results....::.
##
## .::..Summary Statistics..::.
## # Filename Read peaks# Background Weak Stringent Confirmed Discarded TruePositive FalsePositive
## ---- -------------------- ----------- ---------- -------- --------- --------- --------- ------------ -------------
## 1/2 gr1 5,458 51.319% 39.080% 9.601% 27.318% 21.363% 27.318% 0.000%
## 2/2 gr2 4,119 17.747% 48.216% 34.037% 30.129% 52.124% 30.129% 0.000%
##
## .::.Consensus Peaks Count.::.
## 1,239
##
##
## Elapsed time: 00:00:16.6375392
## All processes successfully finished.The objects returned by the mspc function in this example are:
results$status## [1] 0tail(results$GRangesObjects)## $`gr2/Confirmed`
## GRanges object with 1241 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 32565-33040 * | MACS_peak_4 44.33
## [2] chr1 34688-34887 * | MACS_peak_5 10.25
## [3] chr1 34973-35206 * | MACS_peak_6 6.89
## [4] chr1 38569-38849 * | MACS_peak_7 15.70
## [5] chr1 437581-437800 * | MACS_peak_19 6.95
## ... ... ... ... . ... ...
## [1237] chr1 248100316-248100550 * | MACS_peak_4103 4.83
## [1238] chr1 249152183-249153033 * | MACS_peak_4110 32.29
## [1239] chr1 249153179-249153549 * | MACS_peak_4111 5.86
## [1240] chr1 249168024-249168194 * | MACS_peak_4115 8.44
## [1241] chr1 249232854-249233102 * | MACS_peak_4119 18.48
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $`gr2/Discarded`
## GRanges object with 2147 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 9933-10069 * | MACS_peak_1 7.99
## [2] chr1 11143-11340 * | MACS_peak_2 5.37
## [3] chr1 29363-29488 * | MACS_peak_3 4.42
## [4] chr1 132229-132405 * | MACS_peak_8 7.45
## [5] chr1 136294-136419 * | MACS_peak_9 4.29
## ... ... ... ... . ... ...
## [2143] chr1 249132100-249132234 * | MACS_peak_4108 4.99
## [2144] chr1 249132816-249133041 * | MACS_peak_4109 5.08
## [2145] chr1 249167198-249167371 * | MACS_peak_4113 9.63
## [2146] chr1 249208247-249208409 * | MACS_peak_4117 9.33
## [2147] chr1 249221136-249221304 * | MACS_peak_4118 11.34
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $`gr2/FalsePositive`
## GRanges object with 0 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## -------
## seqinfo: no sequences
##
## $`gr2/Stringent`
## GRanges object with 1402 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 32565-33040 * | MACS_peak_4 44.33
## [2] chr1 34688-34887 * | MACS_peak_5 10.25
## [3] chr1 38569-38849 * | MACS_peak_7 15.70
## [4] chr1 432555-433057 * | MACS_peak_18 14.06
## [5] chr1 544675-545070 * | MACS_peak_25 10.93
## ... ... ... ... . ... ...
## [1398] chr1 249167198-249167371 * | MACS_peak_4113 9.63
## [1399] chr1 249168024-249168194 * | MACS_peak_4115 8.44
## [1400] chr1 249208247-249208409 * | MACS_peak_4117 9.33
## [1401] chr1 249221136-249221304 * | MACS_peak_4118 11.34
## [1402] chr1 249232854-249233102 * | MACS_peak_4119 18.48
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $`gr2/TruePositive`
## GRanges object with 1241 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 32565-33040 * | MACS_peak_4 44.33
## [2] chr1 34688-34887 * | MACS_peak_5 10.25
## [3] chr1 34973-35206 * | MACS_peak_6 6.89
## [4] chr1 38569-38849 * | MACS_peak_7 15.70
## [5] chr1 437581-437800 * | MACS_peak_19 6.95
## ... ... ... ... . ... ...
## [1237] chr1 248100316-248100550 * | MACS_peak_4103 4.83
## [1238] chr1 249152183-249153033 * | MACS_peak_4110 32.29
## [1239] chr1 249153179-249153549 * | MACS_peak_4111 5.86
## [1240] chr1 249168024-249168194 * | MACS_peak_4115 8.44
## [1241] chr1 249232854-249233102 * | MACS_peak_4119 18.48
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $`gr2/Weak`
## GRanges object with 1986 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 9933-10069 * | MACS_peak_1 7.99
## [2] chr1 11143-11340 * | MACS_peak_2 5.37
## [3] chr1 29363-29488 * | MACS_peak_3 4.42
## [4] chr1 34973-35206 * | MACS_peak_6 6.89
## [5] chr1 132229-132405 * | MACS_peak_8 7.45
## ... ... ... ... . ... ...
## [1982] chr1 248100316-248100550 * | MACS_peak_4103 4.83
## [1983] chr1 249120099-249120329 * | MACS_peak_4105 4.08
## [1984] chr1 249132100-249132234 * | MACS_peak_4108 4.99
## [1985] chr1 249132816-249133041 * | MACS_peak_4109 5.08
## [1986] chr1 249153179-249153549 * | MACS_peak_4111 5.86
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe output in this vignette was produced under the following conditions:
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] rtracklayer_1.54.0 GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [4] IRanges_2.28.0 S4Vectors_0.32.0 BiocGenerics_0.40.0
## [7] rmspc_1.0.0 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] bslib_0.3.1 compiler_4.1.1
## [3] BiocManager_1.30.16 restfulr_0.0.13
## [5] jquerylib_0.1.4 XVector_0.34.0
## [7] MatrixGenerics_1.6.0 bitops_1.0-7
## [9] tools_4.1.1 zlibbioc_1.40.0
## [11] digest_0.6.28 lattice_0.20-45
## [13] jsonlite_1.7.2 evaluate_0.14
## [15] rlang_0.4.12 Matrix_1.3-4
## [17] DelayedArray_0.20.0 parallel_4.1.1
## [19] yaml_2.2.1 xfun_0.27
## [21] fastmap_1.1.0 GenomeInfoDbData_1.2.7
## [23] stringr_1.4.0 knitr_1.36
## [25] Biostrings_2.62.0 sass_0.4.0
## [27] grid_4.1.1 Biobase_2.54.0
## [29] R6_2.5.1 processx_3.5.2
## [31] BiocParallel_1.28.0 XML_3.99-0.8
## [33] rmarkdown_2.11 bookdown_0.24
## [35] magrittr_2.0.1 matrixStats_0.61.0
## [37] GenomicAlignments_1.30.0 Rsamtools_2.10.0
## [39] ps_1.6.0 htmltools_0.5.2
## [41] SummarizedExperiment_1.24.0 stringi_1.7.5
## [43] RCurl_1.98-1.5 crayon_1.4.1
## [45] rjson_0.2.20 BiocIO_1.4.0Jalili, V., Matteucci, M., Masseroli, M., & Morelli, M. J. (2015). Using combined evidence from replicates to evaluate ChIP-seq peaks. Bioinformatics, 31(17), 2761-2769. Link to the article