R/server.R
In UMMS-Biocore/crisprseekplus: crisprseekplus
#' cspServer
#'
#' Sets up shinyServer to be able to run crisprseekplus interactively.
#'
#' @note \code{cspServer}
#' @param input, input params from UI
#' @param output, output params to UI
#' @return the panel for main plots;
#'
#' @examples
#' cspServer
#'
#' @export
#' @importFrom shiny actionButton actionLink addResourcePath column
#' conditionalPanel downloadButton downloadHandler
#' eventReactive fileInput fluidPage helpText isolate
#' mainPanel need numericInput observe observeEvent
#' outputOptions plotOutput radioButtons
#' reactive reactiveValues renderPlot renderUI runApp
#' selectInput shinyApp shinyServer shinyUI sidebarLayout
#' sidebarPanel sliderInput stopApp tabPanel tabsetPanel
#' textInput textOutput titlePanel uiOutput tags HTML
#' h4 img icon updateTabsetPanel updateTextInput validate
#' wellPanel checkboxInput br checkboxGroupInput a strong
#' renderPrint fluidRow
#' @importFrom shinyjs hide enable disable reset useShinyjs extendShinyjs
#' js inlineCSS toggleState
#' @importFrom DT datatable dataTableOutput renderDataTable
#' @importFrom utils read.csv read.table zip update.packages
#' @importFrom hash hash
#' @import GenomicRanges
#' @import CRISPRseek
#' @importFrom GUIDEseq GUIDEseqAnalysis
#' @importFrom BSgenome getBSgenome
#' @importFrom GenomicRanges GRanges
#' @importFrom GenomicFeatures exons
#' @importFrom BiocInstaller biocLite
#' @importFrom AnnotationDbi get
#' @export
#'
#'
cspServer <- function(input, output) {
if (!interactive()) {
options( shiny.maxRequestSize = 1000 * 1024 ^ 2,
shiny.fullstacktrace = FALSE, shiny.trace=FALSE,
shiny.autoreload=TRUE)
#library(crisprseekplus)
}
output$loading <- renderUI({
getLoadingMsg()
})
output$logo <- renderUI({
getLogo()
})
output$output1 <- renderUI({
#InputFilePath
inputFilePath <- fileInputFunc(input = input$file1$datapath,
sampleFile = system.file("extdata", "inputseq.fa",
package = "CRISPRseek"))
#REpatternFile
REpatternFile <- fileInputFunc(input = input$file2$datapath,
sampleFile = system.file("extdata", "NEBenzymes.fa",
package = "CRISPRseek"))
#Mismatch Activity FIle
mismatch.activity.file <- fileInputFunc(input =
input$mismatchActivityFile$datapath,
sampleFile = system.file("extdata",
"NatureBiot2016SuppTable19DoenchRoot.csv",
package = "CRISPRseek"))
#InputFile1Path for C2S
inputFile1Path <- fileInputFunc(input = input$file3$datapath,
sampleFile = system.file("extdata", "rs362331T.fa",
package = "CRISPRseek"))
#InputFile2Path for C2S
inputFile2Path <- fileInputFunc(input = input$file4$datapath,
sampleFile = system.file("extdata", "rs362331C.fa",
package = "CRISPRseek"))
#UMI File for GSA
umifile <- fileInputFunc(input = input$file5$datapath,
sampleFile = system.file("extdata", "UMI-HEK293_site4_chr13.txt",
package = "GUIDEseq"))
#BAM File for GSA
bamfile <- fileInputFunc(input = input$file6$datapath,
sampleFile = system.file("extdata","bowtie2.HEK293_site4_chr13.sort.bam",
package = "GUIDEseq"))
#gRNA File for GSA
gRNA.file <- fileInputFunc(input = input$file7$datapath,
sampleFile = system.file("extdata","gRNA.fa", package = "GUIDEseq"))
outputDir <- paste0(tempdir(), "/", input$runNum)
isolate(
if(input$fileFormat == 1) {
format <- "fasta"
}
else if(input$fileFormat == 2) {
format <- "fastq"
}
else if(input$fileFormat == 3) {
format <- "bed"
})
isolate(
if(input$chooseAction == 2) {
if(input$fileFormat2 == 1) {
format <- append(format, "fasta")}
else if(input$fileFormat2 == 2) {
format <- append(format, "fastq")}
else {
format <- append(format, "bed")}}
)
isolate(
if(input$gRNAexport == 1) {
exportAllgRNAs <- "fasta"}
else if(input$gRNAexport == 2) {
exportAllgRNAs <- "genbank"}
else if(input$gRNAexport == 3) {
exportAllgRNAs <- "all"}
else {
exportAllgRNAs <- "no"})
isolate(
header <- trueFalseFunc(input$fileHeader)
)
#Find gRNAs With RE Cut Only?
isolate(
if(input$chooseAction == 1) {
findgRNAsWithREcutOnly<- trueFalseFunc(input$radio1)}
else {
findgRNAsWithREcutOnly<- trueFalseFunc(input$radio3)}
)
#Annotate paired?
isolate(
if(input$annPaired1 == 1 || input$annPaired2 == 1) {
annotatePaired <- TRUE}
else {
annotatePaired <- FALSE}
)
#Find Paired gRNA only?
isolate(
if(input$chooseAction == 1) {
findPairedgRNAOnly <- trueFalseFunc(input$radio2)}
else {
findPairedgRNAOnly <- trueFalseFunc(input$radio4)}
)
#Input Organism: updates txdb, BSGenomeName, and orgAnn
isolate(
if(input$organism == "mm10") {
installpack("org.Mm.eg.db")
installpack("BSgenome.Mmusculus.UCSC.mm10")
installpack("TxDb.Mmusculus.UCSC.mm10.knownGene")
BSgenomeName <- getBSgenome("BSgenome.Mmusculus.UCSC.mm10")
txdb <- get("TxDb.Mmusculus.UCSC.mm10.knownGene")
orgAnn <- "org.Mm.egSYMBOL"
}
else if(input$organism == "ce6") {
installpack("BSgenome.Celegans.UCSC.ce6")
installpack("TxDb.Celegans.UCSC.ce6.ensGene")
installpack("org.Ce.eg.db")
BSgenomeName <- getBSgenome("BSgenome.Celegans.UCSC.ce6")
txdb <- get("TxDb.Celegans.UCSC.ce6.ensGene")
orgAnn <- "org.Ce.egSYMBOL"
}
else if(input$organism == "rn5") {
installpack("BSgenome.Rnorvegicus.UCSC.rn5")
installpack("TxDb.Rnorvegicus.UCSC.rn5.refGene")
installpack("org.Rn.eg.db")
BSgenomeName <- getBSgenome("BSgenome.Rnorvegicus.UCSC.rn5")
txdb <- get("TxDb.Rnorvegicus.UCSC.rn5.refGene")
orgAnn <- "org.Rn.egSYMBOL"
}
else if(input$organism == "dm3") {
installpack("BSgenome.Dmelanogaster.UCSC.dm3")
installpack("TxDb.Dmelanogaster.UCSC.dm3.ensGene")
installpack("org.Dm.eg.db")
BSgenomeName <- getBSgenome("BSgenome.Dmelanogaster.UCSC.dm3")
txdb <- get("TxDb.Dmelanogaster.UCSC.dm3.ensGene")
orgAnn <- "org.Dm.egSYMBOL"
}
else {
installpack("BSgenome.Hsapiens.UCSC.hg19")
installpack("TxDb.Hsapiens.UCSC.hg19.knownGene")
installpack("org.Hs.eg.db")
orgAnn <- "org.Hs.egSYMBOL"
BSgenomeName <- getBSgenome("BSgenome.Hsapiens.UCSC.hg19")
txdb <- get("TxDb.Hsapiens.UCSC.hg19.knownGene")
}
)
#Chromosome to Search?
isolate(
chromToSearch <- input$chromSearch
)
#Advanced Settings
#Change default gRNA/RE cut size overlap positions
isolate(
overlap.gRNA.positions <- c(input$overlapgRNA[1],
input$overlapgRNA[2]))
#Change the input size of the gRNA
isolate(
gRNA.size <- input$gRNASize)
#Change number of bases before the gRNA for calculating gRNA efficiency
isolate(
baseBeforegRNA <- input$baseBefore)
#Change number of bases before PAM for calculating gRNA efficiency
isolate(
baseAfterPAM <- input$baseAfter)
#Change min.distance between oppositely orientied gRNA to be paired gRNA
isolate(
min.gap <- input$minGap)
#Change max.distance between oppositely orientied gRNA to bepaired gRNA
isolate(
max.gap <- input$maxGap)
#Choose whether or not to indicate whether the off target is inside
# and exon or not
isolate(
if(input$annExon == 2)
annotateExon <- FALSE
else (
annotateExon <- TRUE))
#Change PAM length
isolate(
PAM.size <- input$pamSize)
#Change temperature
isolate(
temperature <- input$temp)
#Enable multicore processing?
isolate(
if(input$multicore == 1)
enable.multicore <- TRUE
else(
enable.multicore <- FALSE)
)
#Search direction
searchDirection <- c()
isolate(
searchDirection <- append(searchDirection, input$searchDir)
)
#grna name prefix
isolate(
gRNA.name.prefix <- input$gRNAname
)
#Find gRNA from input?
isolate(
if(input$chooseAction == 1) {
if(input$findgRNA1 == 1){
findgRNAs <- TRUE}
else {
findgRNAs <- FALSE}
}
else {
if(input$findgRNA2 == 1) {
findgRNAs <- c(TRUE, TRUE)}
else if(input$findgRNA2 == 2) {
findgRNAs <- c(FALSE, FALSE)
}
else if(input$findgRNA2 == 3) {
findgRNAs <- c(TRUE, FALSE)
}
else if(input$findgRNA2 == 4) {
findgRNAs <- c(FALSE, TRUE)
}
})
#Change default PAM Sequence
isolate(
PAM <- input$PAMSeq
)
#Overwrite existing filesin the output
#directory each time analysis is run?
isolate(
overwrite <- trueFalseFunc(input$overwriteFile))
#Max mapped R1 bp length to be considered
#for downstream analysis
isolate(
min.R1.mapped <- input$minR1)
isolate(
min.R2.mapped <- input$minR2)
#Specify whether the paired reads are required
#to align to the same chromosme
isolate(
same.chromosome <- trueFalseFunc(input$sameChrom))
#Does UMI File contain a header?
isolate(
umi.header <- trueFalseFunc(input$umiheader)
)
#Index of the column containing the read
#identified in UMI file
isolate(
read.ID.col <- input$readID
)
#Index of column containing umi or umi plus the
#first few bases of sequence from R1 reads
isolate(
umi.col <- input$umicol
)
#specify whether R1/R2 should be aligned to the same or
#opposite strands
isolate(
concordant.strand <- trueFalseFunc(input$concordantStrand))
#Maximum distance allowed between R1/R2 reads
isolate(
max.paired.distance <- input$maxPairedDistance
)
#Minimum mapping quality of acceptable alignments
isolate(
min.mapping.quality <- input$minMapQuality
)
#Distance value to be assigned to paired reads
#aligned to different chromosomes
isolate(
distance.inter.chrom <- input$distInterChrom
)
#apply minimum mapped length requirement to both r1/r2 reads
isolate(
apply.both.min.mapped <- trueFalseFunc(input$applyMinMapped)
)
#min number of reads to be considered as a peak
isolate(
min.reads <- input$minReads
)
#maximum pcalue to be considered as significant
isolate(
maxP <- input$max.P
)
#Statistical test
isolate(
if(input$stat == 1) {
stats <- "poisson"
}
else {
stats <- "nbinom"
}
)
#Max gap allowed between the plus strand
#and the negative strand peak
isolate(
distance.threshold <- input$distThreshold
)
#PAM sequenece after the gRNA
isolate(
PAM.pattern <- input$PAMpattern
)
#number of degenerative bases in the PAM sequence
isolate(
allowed.mismatch.PAM <- input$PAMmismatch
)
#weights to be used in SPcas9 system
isolate(
v <- unlist(strsplit(input$weight,","))
)
isolate(
v <- as.numeric(v[!is.na(v)])
)
isolate(
if(length(v) < gRNA.size) {
x <- (gRNA.size - length(v))
padZeros <- vector("numeric", length = x)
weights <- append(padZeros, v)
}
else {
weights <- v
})
#min score of an off target to be included in final output
isolate(
min.score <- input$minScore)
#top N off targets to be included in the final output
isolate(
topN <- input$top.N)
#top N off target used to calculate the total
#off target score
isolate(
topN.OfftargetTotalScore <- input$topNscore)
#Fetch flank sequence of off target or not
isolate(
fetchSequence <- trueFalseFunc(input$fetchSeq))
#upstream offset from the off target start
isolate(
upstream <- input$up.stream
)
#downstream offset from the off target end
isolate(
downstream <- input$down.stream
)
# display in gray scale with the darkness
#indicating the gRNA efficacy
isolate(
useScore <- trueFalseFunc(input$usescore)
)
isolate(
useEfficacyFromInputSeq <- trueFalseFunc(input$efficacyFromIS))
#Indicates which method to use for offtarget
#cleavage rate estimation
isolate(
if(input$scoringmethod == 1) {
scoring.method <- "Hsu-Zhang"
}
else {
scoring.method <- "CFDscore"}
)
#Sub PAM activity
AA = input$AA
AC = input$AC
AG = input$AG
AT = input$AT
CA = input$CA
CC = input$CC
CG = input$CG
CT = input$CT
GA = input$GA
GC = input$GC
GG = input$GG
GT = input$GT
TA = input$TA
TC = input$TC
TG = input$TG
TT = input$TT
isolate(
subPAM.activity <- hash::hash("AA" = AA, "AC" = AC, "AG" = AG,
"AT" = AT, "CA" = CA, "CC" = CC,
"CG" = CG, "CT" = CT, "GA" = GA,
"GC" = GC, "GG" = GG, "GT" = GT,
"TA" = TA, "TC" = TC, "TG" = TG,
"TT" = TT))
#upstream offset from the bed input starts to search for gRNAs
isolate(
upstream.search <- input$upstreamSearch
)
#downstream offset from the bed input ends to search for gRNAs
isolate(
downstream.search <- input$downstreamSearch
)
#The start and end positions of the sub PAM.
isolate(
subPAM.position <- c(input$subPamPos1, input$subPamPos2)
)
#whether to remove the detailed gRNA information such as efficacy
#file/restriction enzyme cut sites
isolate(
if(input$removeDetails == 1) {
removegRNADetails <- c(TRUE, TRUE)
}
else if(input$removeDetails == 2) {
removegRNADetails <- c(FALSE, FALSE)
}
else if(input$removeDetails == 3) {
removegRNADetails <- c(TRUE, FALSE)
}
else if(input$removeDetails == 4) {
removegRNADetails <- c(FALSE, TRUE)
}
)
#window size to calculate coverage
isolate(
window.size <- input$window
)
#step size to calculate coverage
isolate(
step <- input$stepSize
)
#window size to calculate local background
isolate(
bg.window.size <- input$BGWindow
)
#Adjustment method for multiple comparisons
isolate(
if(input$adjustMethods == 1) {
p.adjust.methods <- "none"
}
else if(input$adjustMethods == 2) {
p.adjust.methods <- "BH"
}
else if(input$adjustMethods == 3) {
p.adjust.methods <- "holm"
}
else if(input$adjustMethods == 4) {
p.adjust.methods <- "hochberg"
}
else if(input$adjustMethods == 5) {
p.adjust.methods <- "hommel"
}
else if(input$adjustMethods == 6) {
p.adjust.methods <- "bonferroni"
}
else if(input$adjustMethods == 7) {
p.adjust.methods <- "BY"
}
else if(input$adjustMethods == 8) {
p.adjust.methods <- "fdr"
}
)
#Chromosome to exclude searching in off target analysis
isolate(
chromToExclude <- as.character(unlist(strsplit(
input$chromExclude,",")))
)
#Run off target analysis
resultsOTA <- eventReactive(input$goButton, {
offTargetAnalysis(inputFilePath = inputFilePath,
findgRNAsWithREcutOnly=findgRNAsWithREcutOnly,
REpatternFile = REpatternFile,
findPairedgRNAOnly = findPairedgRNAOnly,
annotatePaired = annotatePaired,
BSgenomeName = BSgenomeName, txdb=txdb,
orgAnn = orgAnn,max.mismatch = input$mismatch,
chromToSearch = chromToSearch, outputDir = outputDir,
overwrite = overwrite, allowed.mismatch.PAM =
allowed.mismatch.PAM,
overlap.gRNA.positions=overlap.gRNA.positions,
gRNA.size = gRNA.size, baseBeforegRNA =baseBeforegRNA,
baseAfterPAM=baseAfterPAM,max.gap = max.gap,
annotateExon = annotateExon, enable.multicore =
enable.multicore,
PAM.size = PAM.size, temperature = temperature,
minREpatternSize = input$REPatSize1,
findgRNAs = findgRNAs,
PAM = PAM, PAM.pattern = PAM.pattern, format = format,
header = header, exportAllgRNAs = exportAllgRNAs,
gRNA.name.prefix = gRNA.name.prefix, min.score =
min.score, topN = topN, topN.OfftargetTotalScore =
topN.OfftargetTotalScore,
fetchSequence = fetchSequence, upstream = upstream,
downstream = downstream, weights = weights,
useScore = useScore,
useEfficacyFromInputSeq = useEfficacyFromInputSeq,
scoring.method = scoring.method, subPAM.activity =
subPAM.activity,
mismatch.activity.file = mismatch.activity.file,
upstream.search = upstream.search,
downstream.search = downstream.search,
subPAM.position = subPAM.position,
chromToExclude = chromToExclude)
})
#run compare 2 sequences
resultsC2S <- eventReactive(input$goButton, {
compare2Sequences(inputFile1Path = inputFile1Path,
inputFile2Path =inputFile2Path,
REpatternFile = REpatternFile, outputDir = outputDir,
overwrite = overwrite, BSgenomeName = BSgenomeName,
findgRNAsWithREcutOnly=findgRNAsWithREcutOnly,
findPairedgRNAOnly=findPairedgRNAOnly, annotatePaired
= annotatePaired, overlap.gRNA.positions=
overlap.gRNA.positions, gRNA.size = gRNA.size,
baseBeforegRNA =baseBeforegRNA, min.gap = min.gap,
baseAfterPAM=baseAfterPAM, max.gap = max.gap,
PAM.size = PAM.size, temperature = temperature,
minREpatternSize = input$REPatSize2,
findgRNAs = findgRNAs,
max.mismatch = input$mismatch,
searchDirection = searchDirection, PAM = PAM,
PAM.pattern = PAM.pattern,allowed.mismatch.PAM
= allowed.mismatch.PAM, scoring.method = scoring.method,
subPAM.activity = subPAM.activity, weights = weights,
mismatch.activity.file = mismatch.activity.file,
subPAM.position = subPAM.position, format = format,
header = header, removegRNADetails = removegRNADetails,
exportAllgRNAs = exportAllgRNAs, gRNA.name.prefix =
gRNA.name.prefix, upstream = upstream,
downstream = downstream
)
})
#run GUIDEseqAnalysis
resultsGSA <- eventReactive(input$goButton, {
GUIDEseqAnalysis(alignment.inputfile = bamfile, umi.inputfile = umifile,
BSgenomeName = BSgenomeName, gRNA.file = gRNA.file,
outputDir = outputDir, overlap.gRNA.positions =
overlap.gRNA.positions, PAM.size = PAM.size, PAM.pattern
= PAM.pattern, gRNA.size = gRNA.size,
PAM = PAM, max.mismatch = input$mismatch,
overwrite = overwrite,
min.R1.mapped = min.R1.mapped,
min.R2.mapped = min.R2.mapped,
same.chromosome = same.chromosome,
umi.header = umi.header,
read.ID.col = read.ID.col, umi.col = umi.col,
concordant.strand = concordant.strand,
max.paired.distance = max.paired.distance,
min.mapping.quality = min.mapping.quality,
distance.inter.chrom = distance.inter.chrom,
apply.both.min.mapped = apply.both.min.mapped,
min.reads = min.reads, maxP = maxP, stats = stats,
distance.threshold = distance.threshold,
weights = weights,
window.size = window.size, step = step,
bg.window.size = bg.window.size,
p.adjust.methods = p.adjust.methods,
allowed.mismatch.PAM = allowed.mismatch.PAM,
upstream = upstream, downstream = downstream)})
if (!file.exists(outputDir)){
dir.create(outputDir)
}
setwd(outputDir)
#Toggle for download button
disableDownload(input$goButton)
#Run Program
if(isolate(input$chooseAction == 1)) {
resultsOTA()
}
else if(isolate(input$chooseAction == 2)) {
resultsC2S()
}
else if(isolate(input$chooseAction == 3)) {
resultsGSA()
}
#Data Tables
output$tables <- DT::renderDataTable(DT::datatable({
if(input$goButton < 1) {
return()
}
else {
#OTA data table example
if(input$chooseAction == 1) {
data <- read.table(paste0(outputDir, "/REcutDetails.xls"),
header = TRUE)
}
#C2S data table example
else if(input$chooseAction == 2) {
data <- read.table(paste0(outputDir,
"/scoresFor2InputSequences.xls"),
colClasses = c(NA, NA, NA, NA, NA, "NULL", "NULL", "NULL",
"NULL", "NULL", "NULL", "NULL", "NULL", "NULL"),
header = TRUE)
}
#GSA data table example
else if(input$chooseAction == 3) {
data <- read.table(paste0(outputDir, "/gRNA-peaks.xls"),
header = TRUE)
}
}
}))#Data Table
#Download output as zip file
output$downloadData <- downloadHandler(
filename = function() {
paste0("crisprSeekPlus_", input$runNum,".zip")
},
content = function(fname) {
crisprOutput <- list.files(path = outputDir)
zip(zipfile = fname, files = crisprOutput)
},
contentType = "application/zip")
return()
})
#Reset all fields
observeEvent(input$resetFields, {
reset("fileFormat")
reset("fileHeader")
reset("gRNAexport")
reset("givenOutputDir")
reset("radio1")
reset("radio2")
reset("radio3")
reset("radio4")
reset("organism")
reset("mismatch")
reset("chromSearch")
reset("overlapgRNA")
reset("gRNASize")
reset("baseBefore")
reset("baseAfter")
reset("minGap")
reset("maxGap")
reset("annExon")
reset("searchDir")
reset("pamSize")
reset("REPatSize1")
reset("REPatSize2")
reset("findgRNA1")
reset("annPaired1")
reset("temp")
reset("findgRNA2")
reset("annPaired2")
reset("multicore")
reset("PAMSeq")
reset("overwriteFile")
reset("minR1")
reset("minR2")
reset("sameChrom")
reset("umiheader")
reset("readID")
reset("umicol")
reset("concordantStrand")
reset("maxPairedDistance")
reset("minMapQuality")
reset("distInterChrom")
reset("applyMinMapped")
reset("minReads")
reset("max.P")
reset("stat")
reset("distThreshold")
reset("PAMpattern")
reset("weight")
reset("gRNAname")
reset("PAMmismatch")
reset("minScore")
reset("top.N")
reset("topNscore")
reset("fetchSeq")
reset("up.stream")
reset("down.stream")
reset("usescore")
reset("efficacyFromIS")
reset("scoringmethod")
reset("AA")
reset("AC")
reset("AG")
reset("AT")
reset("CA")
reset("CC")
reset("CG")
reset("CT")
reset("GA")
reset("GC")
reset("GG")
reset("GT")
reset("TA")
reset("TC")
reset("TG")
reset("TT")
reset("upstreamSearch")
reset("downstreamSearch")
reset("subPamPos1")
reset("subPamPos2")
reset("removeDetails")
reset("window")
reset("BGWindow")
reset("stepSize")
reset("adjustMethods")
reset("PAMlocation")
reset("weight")
reset("chromExlude")})
##################### VIEW INPUT FILES #####################
output$fileInput1 <- renderPrint(
if(is.null(input$file1)) {
print(read.csv(system.file("extdata", "inputseq.fa",
package = "CRISPRseek")))
}
else {
print(read.csv(input$file1$datapath))
})
output$fileInput2 <- DT::renderDataTable(DT::datatable({
if(is.null(input$file2)) {
data <- read.csv(system.file("extdata", "NEBenzymes.fa",
package = "CRISPRseek"), header = TRUE)
}
else {
data <- read.csv(input$file2$datapath)
}
}))
output$mismatchActivityInput <- DT::renderDataTable(DT::datatable({
if(is.null(input$mismatchActivityFile)) {
data <- read.csv(system.file("extdata",
"NatureBiot2016SuppTable19DoenchRoot.csv",
package = "CRISPRseek"))
}
else {
data <- read.csv(input$mismatchActivityFile$datapath)
}
}))
output$fileInput3 <- renderPrint(
if(is.null(input$file3)) {
print(read.csv(system.file("extdata", "rs362331T.fa",
package = "CRISPRseek")))}
else {
print(read.csv(input$file3$datapath))
}
)
output$fileInput4 <- renderPrint(
if(is.null(input$file4)) {
print(read.csv(system.file("extdata", "rs362331C.fa",
package = "CRISPRseek")))
}
else {
print(read.csv(input$file4$datapath))
})
output$fileInput5 <- DT::renderDataTable(DT::datatable({
if(is.null(input$file5)) {
data <- read.csv(system.file("extdata",
"UMI-HEK293_site4_chr13.txt",
package = "GUIDEseq"))
}
else {
data <- read.csv(input$file5$datapath)}
}))
output$fileInput7 <- renderPrint(
if(is.null(input$file7)) {
print(read.csv(system.file("extdata","gRNA.fa",
package = "GUIDEseq")))
}
else {
print(read.csv(input$file7$datapath))})
}
UMMS-Biocore/crisprseekplus documentation built on May 9, 2019, 5:20 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' cspServer
#'
#' Sets up shinyServer to be able to run crisprseekplus interactively.
#'
#' @note \code{cspServer}
#' @param input, input params from UI
#' @param output, output params to UI
#' @return the panel for main plots;
#'
#' @examples
#' cspServer
#'
#' @export
#' @importFrom shiny actionButton actionLink addResourcePath column
#' conditionalPanel downloadButton downloadHandler
#' eventReactive fileInput fluidPage helpText isolate
#' mainPanel need numericInput observe observeEvent
#' outputOptions plotOutput radioButtons
#' reactive reactiveValues renderPlot renderUI runApp
#' selectInput shinyApp shinyServer shinyUI sidebarLayout
#' sidebarPanel sliderInput stopApp tabPanel tabsetPanel
#' textInput textOutput titlePanel uiOutput tags HTML
#' h4 img icon updateTabsetPanel updateTextInput validate
#' wellPanel checkboxInput br checkboxGroupInput a strong
#' renderPrint fluidRow
#' @importFrom shinyjs hide enable disable reset useShinyjs extendShinyjs
#' js inlineCSS toggleState
#' @importFrom DT datatable dataTableOutput renderDataTable
#' @importFrom utils read.csv read.table zip update.packages
#' @importFrom hash hash
#' @import GenomicRanges
#' @import CRISPRseek
#' @importFrom GUIDEseq GUIDEseqAnalysis
#' @importFrom BSgenome getBSgenome
#' @importFrom GenomicRanges GRanges
#' @importFrom GenomicFeatures exons
#' @importFrom BiocInstaller biocLite
#' @importFrom AnnotationDbi get
#' @export
#'
#'
cspServer <- function(input, output) {
if (!interactive()) {
options( shiny.maxRequestSize = 1000 * 1024 ^ 2,
shiny.fullstacktrace = FALSE, shiny.trace=FALSE,
shiny.autoreload=TRUE)
#library(crisprseekplus)
}
output$loading <- renderUI({
getLoadingMsg()
})
output$logo <- renderUI({
getLogo()
})
output$output1 <- renderUI({
#InputFilePath
inputFilePath <- fileInputFunc(input = input$file1$datapath,
sampleFile = system.file("extdata", "inputseq.fa",
package = "CRISPRseek"))
#REpatternFile
REpatternFile <- fileInputFunc(input = input$file2$datapath,
sampleFile = system.file("extdata", "NEBenzymes.fa",
package = "CRISPRseek"))
#Mismatch Activity FIle
mismatch.activity.file <- fileInputFunc(input =
input$mismatchActivityFile$datapath,
sampleFile = system.file("extdata",
"NatureBiot2016SuppTable19DoenchRoot.csv",
package = "CRISPRseek"))
#InputFile1Path for C2S
inputFile1Path <- fileInputFunc(input = input$file3$datapath,
sampleFile = system.file("extdata", "rs362331T.fa",
package = "CRISPRseek"))
#InputFile2Path for C2S
inputFile2Path <- fileInputFunc(input = input$file4$datapath,
sampleFile = system.file("extdata", "rs362331C.fa",
package = "CRISPRseek"))
#UMI File for GSA
umifile <- fileInputFunc(input = input$file5$datapath,
sampleFile = system.file("extdata", "UMI-HEK293_site4_chr13.txt",
package = "GUIDEseq"))
#BAM File for GSA
bamfile <- fileInputFunc(input = input$file6$datapath,
sampleFile = system.file("extdata","bowtie2.HEK293_site4_chr13.sort.bam",
package = "GUIDEseq"))
#gRNA File for GSA
gRNA.file <- fileInputFunc(input = input$file7$datapath,
sampleFile = system.file("extdata","gRNA.fa", package = "GUIDEseq"))
outputDir <- paste0(tempdir(), "/", input$runNum)
isolate(
if(input$fileFormat == 1) {
format <- "fasta"
}
else if(input$fileFormat == 2) {
format <- "fastq"
}
else if(input$fileFormat == 3) {
format <- "bed"
})
isolate(
if(input$chooseAction == 2) {
if(input$fileFormat2 == 1) {
format <- append(format, "fasta")}
else if(input$fileFormat2 == 2) {
format <- append(format, "fastq")}
else {
format <- append(format, "bed")}}
)
isolate(
if(input$gRNAexport == 1) {
exportAllgRNAs <- "fasta"}
else if(input$gRNAexport == 2) {
exportAllgRNAs <- "genbank"}
else if(input$gRNAexport == 3) {
exportAllgRNAs <- "all"}
else {
exportAllgRNAs <- "no"})
isolate(
header <- trueFalseFunc(input$fileHeader)
)
#Find gRNAs With RE Cut Only?
isolate(
if(input$chooseAction == 1) {
findgRNAsWithREcutOnly<- trueFalseFunc(input$radio1)}
else {
findgRNAsWithREcutOnly<- trueFalseFunc(input$radio3)}
)
#Annotate paired?
isolate(
if(input$annPaired1 == 1 || input$annPaired2 == 1) {
annotatePaired <- TRUE}
else {
annotatePaired <- FALSE}
)
#Find Paired gRNA only?
isolate(
if(input$chooseAction == 1) {
findPairedgRNAOnly <- trueFalseFunc(input$radio2)}
else {
findPairedgRNAOnly <- trueFalseFunc(input$radio4)}
)
#Input Organism: updates txdb, BSGenomeName, and orgAnn
isolate(
if(input$organism == "mm10") {
installpack("org.Mm.eg.db")
installpack("BSgenome.Mmusculus.UCSC.mm10")
installpack("TxDb.Mmusculus.UCSC.mm10.knownGene")
BSgenomeName <- getBSgenome("BSgenome.Mmusculus.UCSC.mm10")
txdb <- get("TxDb.Mmusculus.UCSC.mm10.knownGene")
orgAnn <- "org.Mm.egSYMBOL"
}
else if(input$organism == "ce6") {
installpack("BSgenome.Celegans.UCSC.ce6")
installpack("TxDb.Celegans.UCSC.ce6.ensGene")
installpack("org.Ce.eg.db")
BSgenomeName <- getBSgenome("BSgenome.Celegans.UCSC.ce6")
txdb <- get("TxDb.Celegans.UCSC.ce6.ensGene")
orgAnn <- "org.Ce.egSYMBOL"
}
else if(input$organism == "rn5") {
installpack("BSgenome.Rnorvegicus.UCSC.rn5")
installpack("TxDb.Rnorvegicus.UCSC.rn5.refGene")
installpack("org.Rn.eg.db")
BSgenomeName <- getBSgenome("BSgenome.Rnorvegicus.UCSC.rn5")
txdb <- get("TxDb.Rnorvegicus.UCSC.rn5.refGene")
orgAnn <- "org.Rn.egSYMBOL"
}
else if(input$organism == "dm3") {
installpack("BSgenome.Dmelanogaster.UCSC.dm3")
installpack("TxDb.Dmelanogaster.UCSC.dm3.ensGene")
installpack("org.Dm.eg.db")
BSgenomeName <- getBSgenome("BSgenome.Dmelanogaster.UCSC.dm3")
txdb <- get("TxDb.Dmelanogaster.UCSC.dm3.ensGene")
orgAnn <- "org.Dm.egSYMBOL"
}
else {
installpack("BSgenome.Hsapiens.UCSC.hg19")
installpack("TxDb.Hsapiens.UCSC.hg19.knownGene")
installpack("org.Hs.eg.db")
orgAnn <- "org.Hs.egSYMBOL"
BSgenomeName <- getBSgenome("BSgenome.Hsapiens.UCSC.hg19")
txdb <- get("TxDb.Hsapiens.UCSC.hg19.knownGene")
}
)
#Chromosome to Search?
isolate(
chromToSearch <- input$chromSearch
)
#Advanced Settings
#Change default gRNA/RE cut size overlap positions
isolate(
overlap.gRNA.positions <- c(input$overlapgRNA[1],
input$overlapgRNA[2]))
#Change the input size of the gRNA
isolate(
gRNA.size <- input$gRNASize)
#Change number of bases before the gRNA for calculating gRNA efficiency
isolate(
baseBeforegRNA <- input$baseBefore)
#Change number of bases before PAM for calculating gRNA efficiency
isolate(
baseAfterPAM <- input$baseAfter)
#Change min.distance between oppositely orientied gRNA to be paired gRNA
isolate(
min.gap <- input$minGap)
#Change max.distance between oppositely orientied gRNA to bepaired gRNA
isolate(
max.gap <- input$maxGap)
#Choose whether or not to indicate whether the off target is inside
# and exon or not
isolate(
if(input$annExon == 2)
annotateExon <- FALSE
else (
annotateExon <- TRUE))
#Change PAM length
isolate(
PAM.size <- input$pamSize)
#Change temperature
isolate(
temperature <- input$temp)
#Enable multicore processing?
isolate(
if(input$multicore == 1)
enable.multicore <- TRUE
else(
enable.multicore <- FALSE)
)
#Search direction
searchDirection <- c()
isolate(
searchDirection <- append(searchDirection, input$searchDir)
)
#grna name prefix
isolate(
gRNA.name.prefix <- input$gRNAname
)
#Find gRNA from input?
isolate(
if(input$chooseAction == 1) {
if(input$findgRNA1 == 1){
findgRNAs <- TRUE}
else {
findgRNAs <- FALSE}
}
else {
if(input$findgRNA2 == 1) {
findgRNAs <- c(TRUE, TRUE)}
else if(input$findgRNA2 == 2) {
findgRNAs <- c(FALSE, FALSE)
}
else if(input$findgRNA2 == 3) {
findgRNAs <- c(TRUE, FALSE)
}
else if(input$findgRNA2 == 4) {
findgRNAs <- c(FALSE, TRUE)
}
})
#Change default PAM Sequence
isolate(
PAM <- input$PAMSeq
)
#Overwrite existing filesin the output
#directory each time analysis is run?
isolate(
overwrite <- trueFalseFunc(input$overwriteFile))
#Max mapped R1 bp length to be considered
#for downstream analysis
isolate(
min.R1.mapped <- input$minR1)
isolate(
min.R2.mapped <- input$minR2)
#Specify whether the paired reads are required
#to align to the same chromosme
isolate(
same.chromosome <- trueFalseFunc(input$sameChrom))
#Does UMI File contain a header?
isolate(
umi.header <- trueFalseFunc(input$umiheader)
)
#Index of the column containing the read
#identified in UMI file
isolate(
read.ID.col <- input$readID
)
#Index of column containing umi or umi plus the
#first few bases of sequence from R1 reads
isolate(
umi.col <- input$umicol
)
#specify whether R1/R2 should be aligned to the same or
#opposite strands
isolate(
concordant.strand <- trueFalseFunc(input$concordantStrand))
#Maximum distance allowed between R1/R2 reads
isolate(
max.paired.distance <- input$maxPairedDistance
)
#Minimum mapping quality of acceptable alignments
isolate(
min.mapping.quality <- input$minMapQuality
)
#Distance value to be assigned to paired reads
#aligned to different chromosomes
isolate(
distance.inter.chrom <- input$distInterChrom
)
#apply minimum mapped length requirement to both r1/r2 reads
isolate(
apply.both.min.mapped <- trueFalseFunc(input$applyMinMapped)
)
#min number of reads to be considered as a peak
isolate(
min.reads <- input$minReads
)
#maximum pcalue to be considered as significant
isolate(
maxP <- input$max.P
)
#Statistical test
isolate(
if(input$stat == 1) {
stats <- "poisson"
}
else {
stats <- "nbinom"
}
)
#Max gap allowed between the plus strand
#and the negative strand peak
isolate(
distance.threshold <- input$distThreshold
)
#PAM sequenece after the gRNA
isolate(
PAM.pattern <- input$PAMpattern
)
#number of degenerative bases in the PAM sequence
isolate(
allowed.mismatch.PAM <- input$PAMmismatch
)
#weights to be used in SPcas9 system
isolate(
v <- unlist(strsplit(input$weight,","))
)
isolate(
v <- as.numeric(v[!is.na(v)])
)
isolate(
if(length(v) < gRNA.size) {
x <- (gRNA.size - length(v))
padZeros <- vector("numeric", length = x)
weights <- append(padZeros, v)
}
else {
weights <- v
})
#min score of an off target to be included in final output
isolate(
min.score <- input$minScore)
#top N off targets to be included in the final output
isolate(
topN <- input$top.N)
#top N off target used to calculate the total
#off target score
isolate(
topN.OfftargetTotalScore <- input$topNscore)
#Fetch flank sequence of off target or not
isolate(
fetchSequence <- trueFalseFunc(input$fetchSeq))
#upstream offset from the off target start
isolate(
upstream <- input$up.stream
)
#downstream offset from the off target end
isolate(
downstream <- input$down.stream
)
# display in gray scale with the darkness
#indicating the gRNA efficacy
isolate(
useScore <- trueFalseFunc(input$usescore)
)
isolate(
useEfficacyFromInputSeq <- trueFalseFunc(input$efficacyFromIS))
#Indicates which method to use for offtarget
#cleavage rate estimation
isolate(
if(input$scoringmethod == 1) {
scoring.method <- "Hsu-Zhang"
}
else {
scoring.method <- "CFDscore"}
)
#Sub PAM activity
AA = input$AA
AC = input$AC
AG = input$AG
AT = input$AT
CA = input$CA
CC = input$CC
CG = input$CG
CT = input$CT
GA = input$GA
GC = input$GC
GG = input$GG
GT = input$GT
TA = input$TA
TC = input$TC
TG = input$TG
TT = input$TT
isolate(
subPAM.activity <- hash::hash("AA" = AA, "AC" = AC, "AG" = AG,
"AT" = AT, "CA" = CA, "CC" = CC,
"CG" = CG, "CT" = CT, "GA" = GA,
"GC" = GC, "GG" = GG, "GT" = GT,
"TA" = TA, "TC" = TC, "TG" = TG,
"TT" = TT))
#upstream offset from the bed input starts to search for gRNAs
isolate(
upstream.search <- input$upstreamSearch
)
#downstream offset from the bed input ends to search for gRNAs
isolate(
downstream.search <- input$downstreamSearch
)
#The start and end positions of the sub PAM.
isolate(
subPAM.position <- c(input$subPamPos1, input$subPamPos2)
)
#whether to remove the detailed gRNA information such as efficacy
#file/restriction enzyme cut sites
isolate(
if(input$removeDetails == 1) {
removegRNADetails <- c(TRUE, TRUE)
}
else if(input$removeDetails == 2) {
removegRNADetails <- c(FALSE, FALSE)
}
else if(input$removeDetails == 3) {
removegRNADetails <- c(TRUE, FALSE)
}
else if(input$removeDetails == 4) {
removegRNADetails <- c(FALSE, TRUE)
}
)
#window size to calculate coverage
isolate(
window.size <- input$window
)
#step size to calculate coverage
isolate(
step <- input$stepSize
)
#window size to calculate local background
isolate(
bg.window.size <- input$BGWindow
)
#Adjustment method for multiple comparisons
isolate(
if(input$adjustMethods == 1) {
p.adjust.methods <- "none"
}
else if(input$adjustMethods == 2) {
p.adjust.methods <- "BH"
}
else if(input$adjustMethods == 3) {
p.adjust.methods <- "holm"
}
else if(input$adjustMethods == 4) {
p.adjust.methods <- "hochberg"
}
else if(input$adjustMethods == 5) {
p.adjust.methods <- "hommel"
}
else if(input$adjustMethods == 6) {
p.adjust.methods <- "bonferroni"
}
else if(input$adjustMethods == 7) {
p.adjust.methods <- "BY"
}
else if(input$adjustMethods == 8) {
p.adjust.methods <- "fdr"
}
)
#Chromosome to exclude searching in off target analysis
isolate(
chromToExclude <- as.character(unlist(strsplit(
input$chromExclude,",")))
)
#Run off target analysis
resultsOTA <- eventReactive(input$goButton, {
offTargetAnalysis(inputFilePath = inputFilePath,
findgRNAsWithREcutOnly=findgRNAsWithREcutOnly,
REpatternFile = REpatternFile,
findPairedgRNAOnly = findPairedgRNAOnly,
annotatePaired = annotatePaired,
BSgenomeName = BSgenomeName, txdb=txdb,
orgAnn = orgAnn,max.mismatch = input$mismatch,
chromToSearch = chromToSearch, outputDir = outputDir,
overwrite = overwrite, allowed.mismatch.PAM =
allowed.mismatch.PAM,
overlap.gRNA.positions=overlap.gRNA.positions,
gRNA.size = gRNA.size, baseBeforegRNA =baseBeforegRNA,
baseAfterPAM=baseAfterPAM,max.gap = max.gap,
annotateExon = annotateExon, enable.multicore =
enable.multicore,
PAM.size = PAM.size, temperature = temperature,
minREpatternSize = input$REPatSize1,
findgRNAs = findgRNAs,
PAM = PAM, PAM.pattern = PAM.pattern, format = format,
header = header, exportAllgRNAs = exportAllgRNAs,
gRNA.name.prefix = gRNA.name.prefix, min.score =
min.score, topN = topN, topN.OfftargetTotalScore =
topN.OfftargetTotalScore,
fetchSequence = fetchSequence, upstream = upstream,
downstream = downstream, weights = weights,
useScore = useScore,
useEfficacyFromInputSeq = useEfficacyFromInputSeq,
scoring.method = scoring.method, subPAM.activity =
subPAM.activity,
mismatch.activity.file = mismatch.activity.file,
upstream.search = upstream.search,
downstream.search = downstream.search,
subPAM.position = subPAM.position,
chromToExclude = chromToExclude)
})
#run compare 2 sequences
resultsC2S <- eventReactive(input$goButton, {
compare2Sequences(inputFile1Path = inputFile1Path,
inputFile2Path =inputFile2Path,
REpatternFile = REpatternFile, outputDir = outputDir,
overwrite = overwrite, BSgenomeName = BSgenomeName,
findgRNAsWithREcutOnly=findgRNAsWithREcutOnly,
findPairedgRNAOnly=findPairedgRNAOnly, annotatePaired
= annotatePaired, overlap.gRNA.positions=
overlap.gRNA.positions, gRNA.size = gRNA.size,
baseBeforegRNA =baseBeforegRNA, min.gap = min.gap,
baseAfterPAM=baseAfterPAM, max.gap = max.gap,
PAM.size = PAM.size, temperature = temperature,
minREpatternSize = input$REPatSize2,
findgRNAs = findgRNAs,
max.mismatch = input$mismatch,
searchDirection = searchDirection, PAM = PAM,
PAM.pattern = PAM.pattern,allowed.mismatch.PAM
= allowed.mismatch.PAM, scoring.method = scoring.method,
subPAM.activity = subPAM.activity, weights = weights,
mismatch.activity.file = mismatch.activity.file,
subPAM.position = subPAM.position, format = format,
header = header, removegRNADetails = removegRNADetails,
exportAllgRNAs = exportAllgRNAs, gRNA.name.prefix =
gRNA.name.prefix, upstream = upstream,
downstream = downstream
)
})
#run GUIDEseqAnalysis
resultsGSA <- eventReactive(input$goButton, {
GUIDEseqAnalysis(alignment.inputfile = bamfile, umi.inputfile = umifile,
BSgenomeName = BSgenomeName, gRNA.file = gRNA.file,
outputDir = outputDir, overlap.gRNA.positions =
overlap.gRNA.positions, PAM.size = PAM.size, PAM.pattern
= PAM.pattern, gRNA.size = gRNA.size,
PAM = PAM, max.mismatch = input$mismatch,
overwrite = overwrite,
min.R1.mapped = min.R1.mapped,
min.R2.mapped = min.R2.mapped,
same.chromosome = same.chromosome,
umi.header = umi.header,
read.ID.col = read.ID.col, umi.col = umi.col,
concordant.strand = concordant.strand,
max.paired.distance = max.paired.distance,
min.mapping.quality = min.mapping.quality,
distance.inter.chrom = distance.inter.chrom,
apply.both.min.mapped = apply.both.min.mapped,
min.reads = min.reads, maxP = maxP, stats = stats,
distance.threshold = distance.threshold,
weights = weights,
window.size = window.size, step = step,
bg.window.size = bg.window.size,
p.adjust.methods = p.adjust.methods,
allowed.mismatch.PAM = allowed.mismatch.PAM,
upstream = upstream, downstream = downstream)})
if (!file.exists(outputDir)){
dir.create(outputDir)
}
setwd(outputDir)
#Toggle for download button
disableDownload(input$goButton)
#Run Program
if(isolate(input$chooseAction == 1)) {
resultsOTA()
}
else if(isolate(input$chooseAction == 2)) {
resultsC2S()
}
else if(isolate(input$chooseAction == 3)) {
resultsGSA()
}
#Data Tables
output$tables <- DT::renderDataTable(DT::datatable({
if(input$goButton < 1) {
return()
}
else {
#OTA data table example
if(input$chooseAction == 1) {
data <- read.table(paste0(outputDir, "/REcutDetails.xls"),
header = TRUE)
}
#C2S data table example
else if(input$chooseAction == 2) {
data <- read.table(paste0(outputDir,
"/scoresFor2InputSequences.xls"),
colClasses = c(NA, NA, NA, NA, NA, "NULL", "NULL", "NULL",
"NULL", "NULL", "NULL", "NULL", "NULL", "NULL"),
header = TRUE)
}
#GSA data table example
else if(input$chooseAction == 3) {
data <- read.table(paste0(outputDir, "/gRNA-peaks.xls"),
header = TRUE)
}
}
}))#Data Table
#Download output as zip file
output$downloadData <- downloadHandler(
filename = function() {
paste0("crisprSeekPlus_", input$runNum,".zip")
},
content = function(fname) {
crisprOutput <- list.files(path = outputDir)
zip(zipfile = fname, files = crisprOutput)
},
contentType = "application/zip")
return()
})
#Reset all fields
observeEvent(input$resetFields, {
reset("fileFormat")
reset("fileHeader")
reset("gRNAexport")
reset("givenOutputDir")
reset("radio1")
reset("radio2")
reset("radio3")
reset("radio4")
reset("organism")
reset("mismatch")
reset("chromSearch")
reset("overlapgRNA")
reset("gRNASize")
reset("baseBefore")
reset("baseAfter")
reset("minGap")
reset("maxGap")
reset("annExon")
reset("searchDir")
reset("pamSize")
reset("REPatSize1")
reset("REPatSize2")
reset("findgRNA1")
reset("annPaired1")
reset("temp")
reset("findgRNA2")
reset("annPaired2")
reset("multicore")
reset("PAMSeq")
reset("overwriteFile")
reset("minR1")
reset("minR2")
reset("sameChrom")
reset("umiheader")
reset("readID")
reset("umicol")
reset("concordantStrand")
reset("maxPairedDistance")
reset("minMapQuality")
reset("distInterChrom")
reset("applyMinMapped")
reset("minReads")
reset("max.P")
reset("stat")
reset("distThreshold")
reset("PAMpattern")
reset("weight")
reset("gRNAname")
reset("PAMmismatch")
reset("minScore")
reset("top.N")
reset("topNscore")
reset("fetchSeq")
reset("up.stream")
reset("down.stream")
reset("usescore")
reset("efficacyFromIS")
reset("scoringmethod")
reset("AA")
reset("AC")
reset("AG")
reset("AT")
reset("CA")
reset("CC")
reset("CG")
reset("CT")
reset("GA")
reset("GC")
reset("GG")
reset("GT")
reset("TA")
reset("TC")
reset("TG")
reset("TT")
reset("upstreamSearch")
reset("downstreamSearch")
reset("subPamPos1")
reset("subPamPos2")
reset("removeDetails")
reset("window")
reset("BGWindow")
reset("stepSize")
reset("adjustMethods")
reset("PAMlocation")
reset("weight")
reset("chromExlude")})
##################### VIEW INPUT FILES #####################
output$fileInput1 <- renderPrint(
if(is.null(input$file1)) {
print(read.csv(system.file("extdata", "inputseq.fa",
package = "CRISPRseek")))
}
else {
print(read.csv(input$file1$datapath))
})
output$fileInput2 <- DT::renderDataTable(DT::datatable({
if(is.null(input$file2)) {
data <- read.csv(system.file("extdata", "NEBenzymes.fa",
package = "CRISPRseek"), header = TRUE)
}
else {
data <- read.csv(input$file2$datapath)
}
}))
output$mismatchActivityInput <- DT::renderDataTable(DT::datatable({
if(is.null(input$mismatchActivityFile)) {
data <- read.csv(system.file("extdata",
"NatureBiot2016SuppTable19DoenchRoot.csv",
package = "CRISPRseek"))
}
else {
data <- read.csv(input$mismatchActivityFile$datapath)
}
}))
output$fileInput3 <- renderPrint(
if(is.null(input$file3)) {
print(read.csv(system.file("extdata", "rs362331T.fa",
package = "CRISPRseek")))}
else {
print(read.csv(input$file3$datapath))
}
)
output$fileInput4 <- renderPrint(
if(is.null(input$file4)) {
print(read.csv(system.file("extdata", "rs362331C.fa",
package = "CRISPRseek")))
}
else {
print(read.csv(input$file4$datapath))
})
output$fileInput5 <- DT::renderDataTable(DT::datatable({
if(is.null(input$file5)) {
data <- read.csv(system.file("extdata",
"UMI-HEK293_site4_chr13.txt",
package = "GUIDEseq"))
}
else {
data <- read.csv(input$file5$datapath)}
}))
output$fileInput7 <- renderPrint(
if(is.null(input$file7)) {
print(read.csv(system.file("extdata","gRNA.fa",
package = "GUIDEseq")))
}
else {
print(read.csv(input$file7$datapath))})
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.