Componentized Pipeline Framework
Zheng Wei and Shining Ma
2024-10-30
1 What is pipeFrame
pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in this framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save time for users on pipeline tuning or such issues as power off or interrupted process.
2 Download and Installation
The package pipeFrame is part of Bioconductor project starting from Bioc 3.9 built on R 3.6. To install the latest version of pipeFrame, please check your current Bioconductor version and R version first. The latest version of R is recommended, then you can download and install pipeFrame and all its dependencies as follows:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("pipeFrame")Similar with other R packages, please load pipeFrame as follows:
library(pipeFrame)3 Building the pipeline
3.1 Initialize
Before building your pipeline, please initialize the configuration.
For general users, calling the function initPipeFrame is
required after loading the package.
initPipeFrame(availableGenome = c("hg19", "hg38", "mm9", "mm10",
"danRer10", "galGal5", "galGal4",
"rheMac3", "rheMac8", "panTro4",
"rn5", "rn6", "sacCer2","sacCer3",
"susScr3", "testgenome"),
defaultJobName = "test-pipeline"
)In this function, several parameters need to be defined and
configured, including genome (All of the genomes available for this
pacakge are shown in the code above. This is also the default value for
availableGenome. User can use a subset of them.), job name,
reference directory, temporary directory, check and install function,
threads number, reference list, etc. In the following section, we will
go through them in more details.
3.2 Configuration
For the default configuration, the directory of files is organized as follows:
- Working Directory/ (Temporary Directory)
- pkgname-pipeline/ (Job name)
- Step_00_FirstStepName_pipe/
- Step_01_SecondStepName_pipe/
- Step_02_ThirdStepName_pipe/
- …
- refdir (Reference genome directory, which could be set by calling
the function ‘setGenome’)
- hg19/ (Human reference genome files (e.g. hg19.fa) are stored here)
- hg38/
- mm10/
- mm9/
- …
- pkgname-pipeline/ (Job name)
3.2.1 Temporary Directory
By default, the temporary directory is set as the current working
directory. It could be modified by the function
initPipeFrame with argument defaultTmpDir.
Typically, all intermediate results for one job will be stored at the
sub-directory named after this job. Users can get the full pathname by
calling the function:
# display current temporary directory
getTmpDir()## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes"Users can customize the temporary directory in this way:
dir.create("./testdir")
# set a new temporary directory
setTmpDir("./testdir")
# display the new temporary directory
getTmpDir()## [1] "./testdir"3.2.2 Reference Directory
The default reference directory is under the temporary directory. It
could be modified by the function initPipeFrame with
argument defaultRefDir. All of the reference data will be
stored at the sub-directory named after the reference genome version,
respectively. Users can get the full pathname by calling the
function:
# display current reference directory
getRefDir()## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes/refdir"Users can customize the reference directory in this way:
# set a new reference directory
setRefDir("./refdir")
# display the new reference directory
getRefDir()## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes/refdir"3.2.3 Genome Annotation
Usually, only several genome assemblies are available for the
pipeline. The pipeline builders need to specify which genome assemblies
are available. It can be set by the argument
availableGenome in the initPipeFrame function.
Users can obtain the available genome assemblies:
getValidGenome()## [1] "hg19" "hg38" "mm9" "mm10" "danRer10"
## [6] "galGal5" "galGal4" "rheMac3" "rheMac8" "panTro4"
## [11] "rn5" "rn6" "sacCer2" "sacCer3" "susScr3"
## [16] "testgenome"Users can configure currently genome assembly by:
setGenome("hg19")
#display the current configured genome
getGenome()## [1] "hg19"If the genome is not available, a stop message will prompt.
3.2.4 Reference Data Generation
Users usually select only one specific genome assembly, so it would be more efficient to generate the reference data for this specific genome rather than all available genome assemblies.
There are two options for generating reference data. 1) For any R
objects reference data shared by all genome assemblies (e.g. motif PWMs
for vertebrate), they can be set by the argument
defaultReference in the initPipeFrame function
as a list member. Users can use getRef("itemName") to
obtain the reference data. 2) In other cases, please implement a
function and pass it to the argument
defaultCheckAndInstallFunc in the function
initPipeFrame. There are two steps:
First, implement a function for argument
defaultReference. Here we show how to install BSgenome
package for a specific genome assembly and generate its FASTA file.
checkAndInstall needs to call several installation
functions (e.g. checkAndInstallBSgenome and
checkAndInstallGenomeFa) by
runWithFinishCheck. Users assign a reference name
(refName) and file/folder name (refFilePath)
in the reference directory such as “path/to/refdir/hg19/”. This function
can detect break point if the installation is not complete and skip the
item that has already been generated or installed.
checkAndInstall <- function(){
runWithFinishCheck(func = checkAndInstallBSgenome,refName = "bsgenome")
runWithFinishCheck(func = checkAndInstallGenomeFa,refName = "fasta",
refFilePath = "genome.fa")
}Second, implement functions for installation or data generation with
the argument refFilePath.
For BSgenome installation:
library(BSgenome)
checkAndInstallBSgenome <- function(refFilePath){
genome <- getGenome()
bsgenomename<- BSgenome::available.genomes()[
grepl(paste0(genome,"$"),BSgenome::available.genomes())]
if(length(bsgenomename)==0){
stop("BSgenome does not support this genome")
}
bsgenomeinstall <- BSgenome::installed.genomes()[
grepl(paste0(genome,"$"),BSgenome::installed.genomes())]
if(length(bsgenomeinstall)==0){
message(paste("BSgenome for ",genome,"has not been installed,"))
message("begin to install ...")
BiocManager::install(bsgenomename)
}
return(getBSgenome(bsgenomename))
}In this example, the code will check if BSgenome is available for
this genome and install the corresponding BSgenome package.
refFilePath can be ignored because no files will be
generated. The function returns the BSgenome object as the resource of
the reference name. So in the next example, use
getRefRc("bsgenome") to obtain this object.
For genome sequence FASTA file generation:
checkAndInstallGenomeFa <- function(refFilePath){
outFile <- refFilePath
bsgenome<-getRefRc("bsgenome")
if(!is(bsgenome, "BSgenome")){
stop("The variable 'bsgenome' is not a BSgenome")
}
append <- FALSE
for(chrT in seqnames(bsgenome)){
if(is.null(masks(bsgenome[[chrT]])))
chrSeq <- DNAStringSet(bsgenome[[chrT]])
else
chrSeq <- DNAStringSet(injectHardMask(bsgenome[[chrT]],
letter="N"))
names(chrSeq) <- chrT
writeXStringSet(chrSeq, filepath=outFile, format="fasta",
append=append)
append <- TRUE
}
return(NULL)
}In this example, the FASTA file can be generated based on BSgenome
object. It needs to be stored at refFilePath. So the steps
in the pipeline can obtain the reference directory by
getRefFile('fasta'). It does not return any value because
there is no R object generated.
Finally, the function can be passed to the initialization function:
initPipeFrame(availableGenome = c("hg19", "hg38", "mm9", "mm10"),
defaultJobName = "test-pipeline",
defaultCheckAndInstallFunc = checkAndInstall
)When users call setGenome("hg19") to configure genome,
this function will be called and the corresponding reference data will
be installed.
3.2.5 Threads
For multi-core processors, multi-thread program could make full use
of CPUs. It can be set in the function initPipeFrame with
argument defaultThreads. User can get the currently
available max threads as follows:
# check the max user thread limit
getThreads()## [1] 2Users can customize the max threads number as follows:
# customize the max threads number
setThreads(4)
# check the max user thread limit
getThreads()## [1] 43.2.6 Job Name
Intermediate results will be stored in a folder with the job name under the temporary directory.
It is set in the function initPipeFrame with argument
defaultJobName. Users can get the current job name as
follows:
# display the current job name
getJobName()## [1] "test-pipeline"Users can customize the job name as follows:
# set a new job name
setJobName("testJobName")
# display the new job name
getJobName()## [1] "testJobName"getJobDir()## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes/testdir/testJobName"3.3 Step Restriction and Graph Management
The step relations managed are restricted to directed acyclic graph. The direction of data flow is from upstream to downstream. So when users create a new step object, restricting its relation with existing steps is necessary.
For example, if a two-step pipeline is under development, it should be restricted as follows:
addEdges(edges = c("RandomRegionOnGenome","OverlappedRandomRegion"),argOrder = 1)The first parameter is the edge character vector, which consists of
the upstream step and downstream step name. The second parameter
argOrder will tell the order of upstream step in the graph
when it transfers to the downstream step. This will be important and
informative when there are more than one upstream steps for a downstream
object, which helps the Step method distinguish them.
For package development, this function should be called in the
.onLoad function
After the steps are restricted, they are organized in a graph. Users can query this graph by:
printMap()3.4 Step Componentization
Each step should be properly wrapped in the pipeline framework to
make the connections among steps seamless. The framework is specified in
the Step base class. The Step objects contain
the basic organized information for each step such as input directories,
output directories, other parameters etc. All other customized steps
must inherit from this class which is also the wrapper. Creating a new
step needs to obey the rules outlined in the following section.
3.4.1 Non-object Function Wrapper
As many users may not be familiar with the definition of class or object, a function wrapper for the object generator is necessary. In the above example, the two-step pipeline randomly generates regions on the whole genome in the first step and finds out regions overlapped with known regions. Each step can be used individually or as the first step for downstream steps. So the two functions are shown as below.
For step RandomRegionOnGenome class, only sampleNumb is
required. All other arguments could be set by default, as
genome can be obtained from getGenome and
outputBed can be generated automatically. In this wrapper,
all the parameters from the function are put in one list to
generate the RandomRegionOnGenome class. Finally, the step
object should be returned.
randomRegionOnGenome <- function(sampleNumb, regionLen = 1000,
genome = NULL, outputBed = NULL, ...){
allpara <- c(list(Class = "RandomRegionOnGenome"),
as.list(environment()),list(...))
step <- do.call(new,allpara)
invisible(step)
}For the OverlappedRandomRegion class, it is very
similar. Only inputBed and randomBed are
required.
overlappedRandomRegion <- function(inputBed, randomBed, outputBed = NULL, ...){
allpara <- c(list(Class = "OverlappedRandomRegion"),
as.list(environment()),list(...))
step <- do.call(new,allpara)
invisible(step)
}As OverlappedRandomRegion can be the next step of
RandomRegionOnGenome, another function wrapper is needed
for seamless data transfer. First, a generic interface should be
declared. All of the parameters are the same except for the prevStep.
Besides, the randomBed is no longer necessary as it can be obtained from
prevStep object.
setGeneric("runOverlappedRandomRegion",function(prevStep,
inputBed,
randomBed = NULL,
outputBed = NULL,
...)
standardGeneric("runOverlappedRandomRegion"))## [1] "runOverlappedRandomRegion"Second, a Step method should be declared. One more
parameter prevStep needs to be passed to
OverlappedRandomRegion generator.
setMethod(
f = "runOverlappedRandomRegion",
signature = "Step",
definition = function(prevStep,
inputBed,
randomBed = NULL,
outputBed = NULL,
...){
allpara <- c(list(Class = "OverlappedRandomRegion",
prevSteps = list(prevStep)),
as.list(environment()),list(...))
step <- do.call(new,allpara)
invisible(step)
}
)3.4.2 Class Implementation
First, to declare classes, they are required to be inherit from
Step.
Two step classes are shown below as an example
# generate new Step : RandomRegionOnGenome
setClass(Class = "RandomRegionOnGenome",
contains = "Step"
)
# generate another new Step : OverlappedRandomRegion
setClass(Class = "OverlappedRandomRegion",
contains = "Step"
)Second, to initialize parameters, init of
Step method is required to be override. It includes three
arguments, of which .Object is the object itself,
prevSteps is the prior Step object that is
required, and ... contains all parameters passed from
wrapper function. In this function, the pipeline developers need to fill
the three list objects including .Object@inputList (all
input directories or R objects), .Object@outputList (all
output directories or R objects) and .Object@paramList
(other parameters) based on the given arguments.
We recomend to fill in these list with function
input(.Object)$itemname <- value,
output(.Object)$itemname <- value and
param(.Object)$itemname <- value rather than
.Object@inputList[[itemname]] <- value,
.Object@outputList[[itemname]] <- value and
.Object@paramList[[itemname]] <- value. Because it is
more safer to access the slot member of the class.
Here are some tips:
- Use
list(...)to obtain all parameters passed from the wrapper - If there are prevSteps for this step, use
getParam(prevStep,"outputListKey")to obtain the output to fill inputList object - All output file directories should be generated by default in the step intermediate result directory, which can be obtained by getStepWorkDir(.Object,“defaultOutputFileName”)
- If the name of output file is based on the name of input file, use
getAutoPath(.Object,getParam(.Object,"theInputKey"), "suffixToBeRemoved","newSuffixToBeReplacedc") - Input, output and other parameters should be filled in correct formats.
- The dependent input reference files or objects should be filled with
getRef,getRefRc,getRefFilesorgetGenomewhen the arguments are set withNULLor other default values. So when users configure the genome assembly, these arguments are actually not required. - Return .Object at the end of the method
All tips are illustrated in the following two examples.
setMethod(
f = "init",
signature = "RandomRegionOnGenome",
definition = function(.Object,prevSteps = list(),...){
# All arguments in function randomRegionOnGenome
# will be passed from "..."
# so get the arguments from "..." first.
allparam <- list(...)
sampleNumb <- allparam[["sampleNumb"]]
regionLen <- allparam[["regionLen"]]
genome <- allparam[["genome"]]
outputBed <- allparam[["outputBed"]]
# no previous steps for this step so ingnore the "prevSteps"
# begin to set input parameters
# no input for this step
# begin to set output parameters
if(is.null(outputBed)){
output(.Object)$outputBed <-
getStepWorkDir(.Object,"random.bed")
}else{
output(.Object)$outputBed <- outputBed
}
# begin to set other parameters
param(.Object)$regionLen <- regionLen
param(.Object)$sampleNumb <- sampleNumb
if(is.null(genome)){
param(.Object)$bsgenome <- getBSgenome(getGenome())
}else{
param(.Object)$bsgenome <- getBSgenome(genome)
}
# don't forget to return .Object
.Object
}
)
setMethod(
f = "init",
signature = "OverlappedRandomRegion",
definition = function(.Object,prevSteps = list(),...){
# All arguments in function overlappedRandomRegion and
# runOerlappedRandomRegion will be passed from "..."
# so get the arguments from "..." first.
allparam <- list(...)
inputBed <- allparam[["inputBed"]]
randomBed <- allparam[["randomBed"]]
outputBed <- allparam[["outputBed"]]
# inputBed can obtain from previous step object when running
# runOerlappedRandomRegion
if(length(prevSteps)>0){
prevStep <- prevSteps[[1]]
input(.Object)$randomBed <- getParam(prevStep,"outputBed")
}
# begin to set input parameters
if(!is.null(inputBed)){
input(.Object)$inputBed <- inputBed
}
if(!is.null(randomBed)){
input(.Object)$randomBed <- randomBed
}
# begin to set output parameters
# the output is recemended to set under the step work directory
if(!is.null(outputBed)){
output(.Object)$outputBed <- outputBed
}else{
output(.Object)$outputBed <-
getAutoPath(.Object, getParam(.Object, "inputBed"),
"bed", suffix = "bed")
# the path can also be generate in this way
# ib <- getParam(.Object,"inputBed")
# output(.Object)$outputBed <-
# file.path(getStepWorkDir(.Object),
# paste0(substring(ib,1,nchar(ib)-3), "bed"))
}
# begin to set other parameters
# no other parameters
# don't forget to return .Object
.Object
}
)Third, to process the data, processing of
Step method is required to be override. It contains two
arguments, .Object and ....
.Object is the object itself, and ... is
currently unused and for future extension. In this function, the
pipeline developers need to implement the core calculation algorithm and
save the result objects or files to configure the output directory.
Here are some tips:
- Display the parameters including input, output and others by
getParam(.Object, "the key name") - Return .Object at the end of the method
setMethod(
f = "processing",
signature = "RandomRegionOnGenome",
definition = function(.Object,...){
# All arguments are set in .Object
# so we can get them from .Object
sampleNumb <- getParam(.Object,"sampleNumb")
regionLen <- getParam(.Object,"regionLen")
bsgenome <- getParam(.Object,"bsgenome")
outputBed <- getParam(.Object,"outputBed")
# begin the calculation
chrlens <-seqlengths(bsgenome)
selchr <- grep("_|M",names(chrlens),invert=TRUE)
chrlens <- chrlens[selchr]
startchrlens <- chrlens - regionLen
spchrs <- sample(x = names(startchrlens),
size = sampleNumb, replace = TRUE,
prob = startchrlens / sum(startchrlens))
gr <- GRanges()
for(chr in names(startchrlens)){
startpt <- sample(x = 1:startchrlens[chr],
size = sum(spchrs == chr),replace = FALSE)
gr <- c(gr,
GRanges(seqnames = chr,
ranges = IRanges(start = startpt, width = 1000)))
}
result <- sort(gr,ignore.strand=TRUE)
rtracklayer::export.bed(object = result, con = outputBed)
# don't forget to return .Object
.Object
}
)
setMethod(
f = "genReport",
signature = "RandomRegionOnGenome",
definition = function(.Object, ...){
.Object
}
)
setMethod(
f = "processing",
signature = "OverlappedRandomRegion",
definition = function(.Object,...){
# All arguments are set in .Object
# so we can get them from .Object
allparam <- list(...)
inputBed <- getParam(.Object,"inputBed")
randomBed <- getParam(.Object,"randomBed")
outputBed <- getParam(.Object,"outputBed")
# begin the calculation
gr1 <- import.bed(con = inputBed)
gr2 <- import.bed(con = randomBed)
gr <- second(findOverlapPairs(gr1,gr2))
export.bed(gr,con = outputBed)
# don't forget to return .Object
.Object
}
)
setMethod(
f = "genReport",
signature = "OverlappedRandomRegion",
definition = function(.Object, ...){
.Object
}
)3.4.3 Step Components Usage
In this way, users do not need to get familiar with the definition of classes or objects, and could build the pipeline easily:
library(magrittr)
testInputBedFilePath <- file.path(tempdir(),"test.bed")
library(rtracklayer)
export.bed(GRanges("chr7:1-127473000"),testInputBedFilePath)
result <- randomRegionOnGenome(1000) %>%
runOverlappedRandomRegion(inputBed = testInputBedFilePath)## [1] "2024-10-30 19:36:00 EDT"
## [1] "2024-10-30 19:36:00 EDT"
## [1] "2024-10-30 19:36:00 EDT"
## [1] "2024-10-30 19:36:00 EDT"
## [1] "2024-10-30 19:36:00 EDT"
## [1] "2024-10-30 19:36:00 EDT"## [1] "2024-10-30 19:36:01 EDT"
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## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"Or use the function seperately:
result1 <- randomRegionOnGenome(1000)## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"randombed <- getParam(result1,"outputBed")
randombed## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes/testdir/testJobName/Step_00_pipe_RandomRegionOnGenome/random.bed"result2 <- overlappedRandomRegion(inputBed = testInputBedFilePath,
randomBed = randombed)## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"## [1] "2024-10-30 19:36:01 EDT"
## [1] "2024-10-30 19:36:01 EDT"
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## [1] "2024-10-30 19:36:01 EDT"3.5 Combine into Whole Pipeline
The steps can be combined into a whole pipeline with a function wrapper:
library(magrittr)
examplePipe <- function(sampleNumb, inputBed, genome, threads = 2,...){
setThreads(threads = threads)
setGenome(genome = genome)
result <- randomRegionOnGenome(sampleNumb = sampleNumb, ...) %>%
runOverlappedRandomRegion(inputBed = inputBed,...)
return(result)
}Developers can select important and frequently used arguments of the
steps as the arguments of the whole pipeline. Other arguments can be
passed through ... with
StepName.argumentsName = value like:
examplePipe(sampleNumb = 1000,inputBed = testInputBedFilePath,genome = "hg19",
RandomRegionOnGenome_pipe.regionLen = 10000)## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"## [1] "2024-10-30 19:36:04 EDT"
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## [1] "2024-10-30 19:36:04 EDT"## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"
## [1] "2024-10-30 19:36:04 EDT"## An object of class "OverlappedRandomRegion"
## Slot "argv":
## $stepDefName
## NULL
##
## $stepPipeName
## NULL
##
## $isReportStep
## [1] FALSE
##
## $outputBed
## NULL
##
## $randomBed
## NULL
##
## $inputBed
## [1] "/tmp/Rtmp8AvY6u/test.bed"
##
## $prevStep
## An object of class "RandomRegionOnGenome"
## Slot "argv":
## $stepDefName
## NULL
##
## $stepPipeName
## NULL
##
## $isReportStep
## [1] FALSE
##
## $sampleNumb
## [1] 1000
##
## $regionLen
## [1] 1000
##
## $genome
## NULL
##
## $outputBed
## NULL
##
##
## Slot "paramList":
## $regionLen
## [1] 1000
##
## $sampleNumb
## [1] 1000
##
## $bsgenome
## | BSgenome object for Human
## | - organism: Homo sapiens
## | - provider: UCSC
## | - genome: hg19
## | - release date: June 2013
## | - 298 sequence(s):
## | chr1 chr2 chr3
## | chr4 chr5 chr6
## | chr7 chr8 chr9
## | chr10 chr11 chr12
## | chr13 chr14 chr15
## | ... ... ...
## | chr19_gl949749_alt chr19_gl949750_alt chr19_gl949751_alt
## | chr19_gl949752_alt chr19_gl949753_alt chr20_gl383577_alt
## | chr21_gl383578_alt chr21_gl383579_alt chr21_gl383580_alt
## | chr21_gl383581_alt chr22_gl383582_alt chr22_gl383583_alt
## | chr22_kb663609_alt
## |
## | Tips: call 'seqnames()' on the object to get all the sequence names, call
## | 'seqinfo()' to get the full sequence info, use the '$' or '[[' operator to
## | access a given sequence, see '?BSgenome' for more information.
##
##
## Slot "inputList":
## list()
##
## Slot "outputList":
## $outputBed
## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes/testdir/testJobName/Step_00_pipe_RandomRegionOnGenome/random.bed"
##
##
## Slot "propList":
## $pipe
## list()
##
##
## Slot "reportList":
## $timeStampStart
## [1] "2024-10-30 19:35:59 EDT"
##
## $timeStampEnd
## [1] "2024-10-30 19:36:00 EDT"
##
##
## Slot "stepName":
## [1] "pipe_RandomRegionOnGenome"
##
## Slot "stepBaseClass":
## [1] "RandomRegionOnGenome"
##
## Slot "finish":
## [1] TRUE
##
## Slot "timeStampStart":
## [1] "2024-10-30 19:35:59 EDT"
##
## Slot "timeStampEnd":
## [1] "2024-10-30 19:36:00 EDT"
##
## Slot "id":
## [1] 0
##
## Slot "pipeName":
## [1] "pipe"
##
## Slot "loaded":
## [1] TRUE
##
## Slot "isReportStep":
## [1] FALSE
##
## Slot "initParam":
## [[1]]
## [1] "test"
##
##
## Slot "processingParam":
## [[1]]
## [1] "test"
##
##
##
## $RandomRegionOnGenome_pipe.regionLen
## [1] 10000
##
##
## Slot "paramList":
## list()
##
## Slot "inputList":
## $randomBed
## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes/testdir/testJobName/Step_00_pipe_RandomRegionOnGenome/random.bed"
##
## $inputBed
## [1] "/tmp/Rtmp8AvY6u/test.bed"
##
##
## Slot "outputList":
## $outputBed
## [1] "/tmp/RtmpxY7Ru9/Rbuild24cb4774e1bac2/pipeFrame/vignettes/testdir/testJobName/Step_01_pipe_OverlappedRandomRegion/test.bed"
##
##
## Slot "propList":
## $pipe
## list()
##
##
## Slot "reportList":
## $timeStampStart
## [1] "2024-10-30 19:36:04 EDT"
##
## $timeStampEnd
## [1] "2024-10-30 19:36:04 EDT"
##
##
## Slot "stepName":
## [1] "pipe_OverlappedRandomRegion"
##
## Slot "stepBaseClass":
## [1] "OverlappedRandomRegion"
##
## Slot "finish":
## [1] TRUE
##
## Slot "timeStampStart":
## [1] "2024-10-30 19:36:04 EDT"
##
## Slot "timeStampEnd":
## [1] "2024-10-30 19:36:04 EDT"
##
## Slot "id":
## [1] 1
##
## Slot "pipeName":
## [1] "pipe"
##
## Slot "loaded":
## [1] FALSE
##
## Slot "isReportStep":
## [1] FALSE
##
## Slot "initParam":
## [[1]]
## [1] "test"
##
##
## Slot "processingParam":
## [[1]]
## [1] "test"4 Session Information
sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg19_1.4.3 magrittr_2.0.3
## [3] BSgenome_1.75.0 rtracklayer_1.67.0
## [5] BiocIO_1.17.0 Biostrings_2.75.0
## [7] XVector_0.47.0 GenomicRanges_1.59.0
## [9] GenomeInfoDb_1.43.0 IRanges_2.41.0
## [11] S4Vectors_0.45.0 BiocGenerics_0.53.0
## [13] pipeFrame_1.23.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.9 SparseArray_1.7.0
## [3] bitops_1.0-9 lattice_0.22-6
## [5] digest_0.6.37 evaluate_1.0.1
## [7] grid_4.5.0 fastmap_1.2.0
## [9] jsonlite_1.8.9 Matrix_1.7-1
## [11] restfulr_0.0.15 BiocManager_1.30.25
## [13] httr_1.4.7 UCSC.utils_1.3.0
## [15] XML_3.99-0.17 codetools_0.2-20
## [17] jquerylib_0.1.4 abind_1.4-8
## [19] cli_3.6.3 rlang_1.1.4
## [21] crayon_1.5.3 Biobase_2.67.0
## [23] visNetwork_2.1.2 cachem_1.1.0
## [25] DelayedArray_0.33.1 yaml_2.3.10
## [27] S4Arrays_1.7.1 tools_4.5.0
## [29] parallel_4.5.0 BiocParallel_1.41.0
## [31] GenomeInfoDbData_1.2.13 Rsamtools_2.23.0
## [33] SummarizedExperiment_1.37.0 curl_5.2.3
## [35] R6_2.5.1 matrixStats_1.4.1
## [37] lifecycle_1.0.4 zlibbioc_1.53.0
## [39] htmlwidgets_1.6.4 bslib_0.8.0
## [41] xfun_0.48 GenomicAlignments_1.43.0
## [43] MatrixGenerics_1.19.0 knitr_1.48
## [45] rjson_0.2.23 htmltools_0.5.8.1
## [47] rmarkdown_2.28 compiler_4.5.0
## [49] RCurl_1.98-1.16