In protViz/SRMService: Report Quantitative Mass Spectrometry Data

Generate Ion Libraries

Parameters

knitr::opts_chunk$set(echo = FALSE, fig.width=10, fig.height=10, warning = FALSE)

library(prozor)
library(specL)

R.Version()$version.string
library(specL)
packageVersion('specL')

Library generation is run with the following parameters:

SWATH_LIBRARY <- file.path(OUTPUTDIR, SWATH_LIBRARY)
PEPPROTMAPPING <- file.path(OUTPUTDIR, "pepprot.tsv")

FRAGMENTIONMZRANGE <- c(300,1250)
FRAGMENTIONRANGE <- c(MIN_IONS,200)

if(TRUE){
cat(" SWATH_LIBRARY = ", SWATH_LIBRARY, "\n", 
    " PEPPROTMAPPING = ",PEPPROTMAPPING , "\n",
    " NON_REDUNDANT = ",NON_REDUNDANT , "\n",
    " REDUNDANT = ",REDUNDANT, "\n" )
}

cat(" MZ_ERROR = ",MZ_ERROR, "\n",
    " FRAGMENTIONMZRANGE = ", FRAGMENTIONMZRANGE, "\n",
    " FRAGMENTIONRANGE = ",FRAGMENTIONRANGE, "\n",
    " FASTA_FILE = ", FASTA_FILE, "\n",
    " MAX_IONS = ", MAX_IONS, "\n",
    " MIN_IONS = ", MIN_IONS, "\n",
    " MASCOT_MIN_SCORE = ", MASCOT_MIN_SCORE, "\n"
    )

print(NON_REDUNDANT)
system.time( nonRedundantBlib <- read.bibliospec(NON_REDUNDANT) )
system.time( redundantBlib <- read.bibliospec(REDUNDANT) )

redundantBlibF <- plyr::laply(redundantBlib, function(x){x$mascotScore > MASCOT_MIN_SCORE})
redundantBlib <- redundantBlib[redundantBlibF]

nonRedundantBlibF <- plyr::laply(nonRedundantBlib, function(x){x$mascotScore > MASCOT_MIN_SCORE})
nonRedundantBlib <- nonRedundantBlib[nonRedundantBlibF]

Defined filtering function.

fragmentIonFunctionUpTo2 <- function (b, y) {
  Hydrogen <- 1.007825
  Oxygen <- 15.994915
  Nitrogen <- 14.003074
  b1_ <- (b )
  y1_ <- (y )
  b2_ <- (b + Hydrogen) / 2
  y2_ <- (y + Hydrogen) / 2 
  return( cbind(b1_, y1_, b2_, y2_) )
}

rtPep <- bibliospec::CiRTpeptides
colnames(rtPep) <- c("peptide", "rt")
specLibrary <- genSwathIonLib(
  data = nonRedundantBlib,
  data.fit = redundantBlib,
  max.mZ.Da.error = MZ_ERROR,
  topN = MAX_IONS,
  fragmentIonMzRange = FRAGMENTIONMZRANGE,
  fragmentIonRange = FRAGMENTIONRANGE,
  fragmentIonFUN = fragmentIonFunctionUpTo2,
  iRT = IRT_PEPTIDES,
  mascotIonScoreCutOFF = IRT_PEPTIDES
  )

op <- par(mfrow=c(2, 1))
class(specLibrary)

plot(specLibrary)

length(nonRedundantBlib)

Library Generation Summary

length(specLibrary)
slotNames(specLibrary)

length([e-mail])
length([e-mail])
specLibrary@ionlibrary[[1]]

slotNames(specLibrary@ionlibrary[[1]])

if(length(specLibrary@ionlibrary) ==0){
  library(knitr)
  opts_chunk$set(eval=FALSE, message=FALSE, echo=FALSE)
}

summary(specLibrary)

Total Number of PSM's with Mascot e score < 0.05, in your search is r length(redundantBlib). The number of unique precurosors is r length(nonRedundantBlib).

The size of the generated ion library is r length(specLibrary@ionlibrary).

That means that r length(specLibrary@ionlibrary)/length(nonRedundantBlib) *100 % of the unique precursors fullfilled the filtering criteria.

Assigning identified precursors to proteins

protpep = getProteinPeptideTable(specLibrary)

library(prozor)
fasta = read.fasta(file = FASTA_FILE, as.string = TRUE, seqtype="AA")

protpepDF <- data.frame(protpep)


protpepDF <- plyr::rename(protpepDF, c('peptideSequence'='peptideSeq',
                                       'peptideModSequence' = 'peptideModSeq',
                                       'z' = 'precursorCharge' ))

protpepAnnot = annotatePeptides(protpepDF,fasta)

Annotate peptides

library(Matrix)
write.table(protpepAnnot,file=PEPPROTMAPPING,quote = FALSE, row.names = FALSE,sep="\t")

pepProtMatrix = prepareMatrix(protpepAnnot, sep=".")
protPepAssingments = greedy(pepProtMatrix)
xx= cbind(names(protPepAssingments),protPepAssingments)


for(i in 1:length(specLibrary@ionlibrary)){
  specl <- specLibrary@ionlibrary[[i]]
  id <- paste(specl@peptideModSeq, specl@prec_z, sep="." )
  tmp = protPepAssingments[[id]]
  if(!is.null(tmp)){
    specLibrary@ionlibrary[[i]]@proteinInformation = tmp
  }
}


uniq <- rowSums(pepProtMatrix)
names(uniq)<-rownames(pepProtMatrix)

proteotyp<- table(uniq)
plot(proteotyp, ylab="nr peptides", xlab="matching number proteins", main="proteotypic")

tp <- unlist(protPepAssingments)

Protein FDR r length(grep("REV_",tp))/length(tp) * 100

freq.table <- table(table(tp))
plot(freq.table,
    ylab = "number of proteins",  
    xlab="number of precurosor assignments", 
    main="single hit wonders")

freq.table <- table(table(tp))
plot(as.numeric(names(rev(freq.table))), cumsum(rev(freq.table)), 
    type = "h",
    log = "x")

• Least specific peptide precursor : r names(uniq)[which.max(uniq)] matching r max(uniq)

proteins.

• Protein r names(tmp)[which.max(tmp)] with most peptide assignments = r max(tmp)

Number of annotated precursors is :r dim(pepProtMatrix)[1]

There are in total: r dim(protpepAnnot)[1] precursor protein assingments. There are: r sum(uniq == 1)

proteotypic precursors, while r dim(pepProtMatrix)[1] - sum(uniq == 1) where assigned to 2 or more protein sequences.

Compute minimal protein set explaining peptides

The r dim(pepProtMatrix)[1] peptide precursors matched protein sequences assigned to r dim(pepProtMatrix)[2] unique protein identifies.

The minimal protein set explaining precursors has a size of r length(unique(unlist(protPepAssingments))) proteins.

if(file.exists(SWATH_LIBRARY)){
  file.remove(SWATH_LIBRARY)
}
slotNames(specLibrary)
write.spectronaut(specLibrary,file=SWATH_LIBRARY)
assay_library = read.table(file=SWATH_LIBRARY,header=TRUE,sep="\t")

Session Information

sessionInfo()

Summary

The file assay_library.tsv contains a spectronaut compatible assay library. The assays in this file are annotated with razor proteins.

Remarks

This report was generated using the packages:

• http://bioconductor.org/packages/specL

• https://github.com/protViz/prozor

We have invested a lot of time and effort in creating and maintaining this software. Please cite our publication:

• Panse C, Trachsel C, Grossmann J and Schlapbach R (2015). ``specL - An R/Bioconductor package to prepare peptide spectrum matches for use in targeted proteomics.'' Bioinformatics, pp. 2228-2231. DOI:10.1093/bioinformatics/btv105, PMID: 25712692.

For questions and improvements please do contact the authors of the application generateSpecLibrary.

protViz/SRMService documentation built on Nov. 13, 2021, 9:58 a.m.