Prologue - How to get the input for the specL package?

Since peptide identification (using, e.g., Mascot, Sequest, xTandem!, Omssa, ProteinPilot) usually creates result files which are heavily redundant and therefore unsuited for spectral library building, the search results must first be filtered. To create non-redundant input files, we use the BiblioSpec (Frewen and MacCoss 2007) algorithm implemented in Skyline (MacLean et al. 2010). A given search result (e.g.  Mascot result file) is loaded into the software Skyline and is redundancy filtered. The ‘Skyline workflow step’ provides two sqlite readable files as output named *.blib and *.redundant.blib. These files are used as ideal input for this packages. Note here, that Skyline is very flexible when it comes to peptide identification results. It means with Skyline you can build the spectrum library files for almost all search engines (even from other spectrum library files such as spectraST (Lam et al. 2008)).

The first step which has to be performed on the R shell is loading specL library.

library(specL)
packageVersion('specL')

## [1] '1.41.0'

Read from redundant plus non-redundant blib files

for demonstration, specL contains the two data sets, namely peptideStd and peptideStd.redundant. This data set comes from two standard-run experiments routinely used to check if the liquid chromatographic system is still working appropriately. The sample consists of a digest of the Fetuin protein (Bos taurus, uniprot id: P12763). 40 femtomole are loaded on the column. Mascot was used to search and identify the respective peptides.

summary(peptideStd)

## Summary of a "psmSet" object.
## Number of precursor:
##  137
## Number of precursors in Filename(s)
##  0140910_01_fetuin_400amol_1.raw 21
##  0140910_07_fetuin_400amol_2.raw 116
## Number of annotated precursor:
##  0

For both peptideStd, peptideStd.redandant data sets the Skyline software was used to generate the bibliospec files which contain the peptide sequences with the respective peptide spectrum match (PSM). The specL::read.bibliospec function was used to read the blib files into R.

The from read.bibliospec generated object has its own plot functions. The LC-MS map graphs peptide mass versus retention time.

# plot(peptideStd)
plot(0,0, main='MISSING')

The individual peptide spectrum match (psm) is displayed by using the protViz peakplot function.

demoIdx <- 40
# str(peptideStd[[demoIdx]])
#res <- plot(peptideStd[[demoIdx]], ion.axes=TRUE)
plot(0,0, main='MISSING')

Read from Mascot result files

Alternatively, Mascot search result files (dat) can be used by applying protViz perl script protViz\-\_mascotDat2RData.pl.

The Perl script can be found in the exec directory of the protViz package. The mascot mod_file can be found in the configurations of the mascot server. An example on our Linux shell looks as follows:

$ /usr/local/lib/R/site-library/protViz/exec/protViz_mascotDat2RData.pl \
-d=/usr/local/mascot/data/20130116/F178287.dat \
-m=mod_file

mascotDat2RData.pl requires the Mascot server mod\_file keeping all the configured modification.

Once the {erl script is finished, the resulting RData file can be read into the R session using load.

Next, the variable modifications, and the S3 psmSet object has to be generated. This can be done by using specL:::.mascot2psmSet

specL:::.mascot2psmSet

## function (dat, mod, mascotScoreCutOff = 40) 
## {
##     res <- lapply(dat, function(x) {
##         x$MonoisotopicAAmass <- protViz::aa2mass(x$peptideSequence)[[1]]
##         modString <- as.numeric(strsplit(x$modification, "")[[1]])
##         modIdx <- which(modString > 0) - 1
##         modString.length <- length(modString)
##         x$varModification <- mod[modString[c(-1, -modString.length)] + 
##             1]
##         if (length(modIdx) > 0) {
##             warning("modified varModification caused.")
##             x$varModification[modIdx] <- x$varModification[modIdx] - 
##                 x$MonoisotopicAAmass[modIdx]
##         }
##         rt <- x$rtinseconds
##         x <- c(x, rt = rt, fileName = "mascot")
##         class(x) <- "psm"
##         return(x)
##     })
##     res <- res[which(unlist(lapply(dat, function(x) {
##         x$mascotScore > mascotScoreCutOff && length(x$mZ) > 10
##     })))]
##     class(res) <- "psmSet"
##     return(res)
## }
## <bytecode: 0x55d6d280e020>
## <environment: namespace:specL>

If you are processing Mascot result files, you can continue reading in the section genSwathIonLib.

However, please note due do the high potential redundancy of peptide spectrum matches in a database search approach, it might not result in useful ion library for targeted data extraction unless redundancy filtering is handled. However, in a future release, a redundancy filter algorithm might be proposed to resolve this problem.

Annotate protein IDs using FASTA

The information to which protein a peptide-spectrum-match belongs (PSM) is not stored by BiblioSpec. Therefore specL provides the annotate.protein\_id function which uses R’s internal grep to ‘reassign’ the protein information. Therefore a fasta object has to be loaded into the R system using read.fasta of the seqinr package. For this, not necessarily, the same fasta file needs to be provided as in the original database search.

The following lines demonstrate a simple sanity check with a single FASTA style formatted protein entry. Also it demonstrates the use case how to identify entries in the R-object which are from one or a few proteins of interest.

irtFASTAseq <- paste(">zz|ZZ_FGCZCont0260|",
"iRT_Protein_with_AAAAK_spacers concatenated Biognosys\n",
"LGGNEQVTRAAAAKGAGSSEPVTGLDAKAAAAKVEATFGVDESNAKAAAAKYILAGVENS",
"KAAAAKTPVISGGPYEYRAAAAKTPVITGAPYEYRAAAAKDGLDAASYYAPVRAAAAKAD",
"VTPADFSEWSKAAAAKGTFIIDPGGVIRAAAAKGTFIIDPAAVIRAAAAKLFLQFGAQGS",
"PFLK\n")

Tfile <- file();  cat(irtFASTAseq, file = Tfile);
fasta.irtFASTAseq <- read.fasta(Tfile, as.string=TRUE, seqtype="AA")
close(Tfile)

As expected, the peptideStd data, e.g., our demo object, does not contain any protein information yet.

peptideStd[[demoIdx]]$proteinInformation

## [1] ""

The protein information can be added as follow:

peptideStd <- annotate.protein_id(peptideStd, 
    fasta=fasta.irtFASTAseq)

## start protein annotation ...

## time taken:  0.000100874900817871 minutes

The following lines now show the object indices of those entries which do have protein information now.

(idx <- which(unlist(lapply(peptideStd, 
    function(x){nchar(x$proteinInformation)>0}))))

## [1] 1 2 3 4 5 6

As expected, there are now a number of peptide sequences annotated with the protein ID.

peptideStd[[demoIdx]]$proteinInformation

## [1] "zz|ZZ_FGCZCont0260|"

Of note, that the default digest pattern is defined as

digestPattern = "(([RK])|(^)|(^M))"

for tryptic peptides. For other enzymes, the pattern has to be adapted. For example, for semi-tryptic identifications, use digestPattern = "".

Generate the spectral library (assay)

genSwathIonLib is the main contribution of the specL package. It generates the spectral library used in a targeted data extraction workflow from a mass spectrometric measurement. Generating the ion library using iRT peptides is highly recommended as described. However if you have no iRT peptide, continue reading in section noiRT.

Generation of the spec Library with default (see Table) settings.

res.genSwathIonLib <- genSwathIonLib(data = peptideStd, 
   data.fit = peptideStd.redundant)

## normalizing RT ...

## found 7 iRT peptide(s) in s:\p1239\Proteomics\QEXACTIVE_3\ctrachse_20140910_Nuclei_diff_extraction_methods\20140910_01_fetuin_400amol_1.raw

## found 7 iRT peptide(s) in s:\p1239\Proteomics\QEXACTIVE_3\ctrachse_20140910_Nuclei_diff_extraction_methods\20140910_07_fetuin_400amol_2.raw

## building model ...

## generating ion library ...

## start generating specLSet object ...

## length of findNN idx  137

## length of genSwathIonLibSpecL   137

## time taken:  0.135942697525024 secs

## length of genSwathIonLibSpecL  after fragmentIonRange filtering 137

genSwathIonLib default settings

summary(res.genSwathIonLib)

## Summary of a "specLSet" object.
## 
## Parameter:
## 
## Number of precursor (q1 and peptideModSeq) = 137
## Number of unique precursor
## (q1.in-silico and peptideModSeq) = 126
## Number of iRT peptide(s) = 8
## Which std peptides (iRTs) where found in which raw files:
##   0140910_01_fetuin_400amol_1.raw GAGSSEPVTGLDAK 
##       0140910_01_fetuin_400amol_1.raw TPVITGAPYEYR 
##       0140910_01_fetuin_400amol_1.raw VEATFGVDESNAK 
##       0140910_07_fetuin_400amol_2.raw ADVTPADFSEWSK 
##       0140910_07_fetuin_400amol_2.raw DGLDAASYYAPVR 
##       0140910_07_fetuin_400amol_2.raw GTFIIDPGGVIR 
##       0140910_07_fetuin_400amol_2.raw LFLQFGAQGSPFLK 
##       0140910_07_fetuin_400amol_2.raw TPVISGGPYEYR 
## 
## Number of transitions frequency:
##  4   1
##  5   5
##  6   10
##  7   7
##  8   18
##  9   32
##  10  64
## 
## Number of annotated precursor = 6
## Number of file(s)
##  2
## 
## Number of precursors in Filename(s)
##  0140910_01_fetuin_400amol_1.raw 21
##  0140910_07_fetuin_400amol_2.raw 116
## 
## Misc:
## 
## Memory usage =    676976 bytes

The determined mass spec coordinates of the selected tandem mass spectrum demoIdx look like this:

res.genSwathIonLib@ionlibrary[[demoIdx]]

## An "specL" object.
## 
## 
## content:
## group_id = GAGSSEPVTGLDAK.2 
## peptide_sequence = GAGSSEPVTGLDAK 
## proteinInformation = zz|ZZ_FGCZCont0260| 
## q1 = 644.8219 
## q1.in_silico = 1288.638 
## q3 = 800.4497 604.3285 1016.522 503.2805 929.4925 400.7282 
## 333.176 1160.581 703.3948 343.1235 
## q3.in_silico = 800.4512 604.3301 1016.526 503.2824 929.4938 
## 400.7295 333.1769 1160.579 703.3985 343.1615 
## prec_z = 2 
## frg_type = y y y y y y y y y b 
## frg_nr = 8 6 10 5 9 8 3 12 7 8 
## frg_z = 1 1 1 1 1 2 1 1 1 2 
## relativeFragmentIntensity = 100 21 19 12 10 9 9 8 8 6 
## irt = -0.95 
## peptideModSeq = GAGSSEPVTGLDAK 
## mZ.error = 0.001514 0.00156 0.003685 0.001914 0.001318 
## 0.001313 0.000856 0.001846 0.003686 0.0380015 
## uclei_diff_extraction_methods\20140910_01_fetuin_400amol_1.raw
## score = 41.54902 
## 
## size:
## Memory usage: 4776 bytes

It can be displayed using the function.

plot(res.genSwathIonLib@ionlibrary[[demoIdx]])

The following code considers only the top five y ions.

# define customized fragment ions
# for demonstration lets consider only the top five singly charged y ions.

r.genSwathIonLib.top5 <- genSwathIonLib(peptideStd,
    peptideStd.redundant, topN=5,
    fragmentIonFUN=function (b, y) {
      return( cbind(y1_=y) )
      }
    )

## normalizing RT ...

## found 7 iRT peptide(s) in s:\p1239\Proteomics\QEXACTIVE_3\ctrachse_20140910_Nuclei_diff_extraction_methods\20140910_01_fetuin_400amol_1.raw

## found 7 iRT peptide(s) in s:\p1239\Proteomics\QEXACTIVE_3\ctrachse_20140910_Nuclei_diff_extraction_methods\20140910_07_fetuin_400amol_2.raw

## building model ...

## generating ion library ...

## start generating specLSet object ...

## length of findNN idx  137

## length of genSwathIonLibSpecL   137

## time taken:  0.129064321517944 secs

## length of genSwathIonLibSpecL  after fragmentIonRange filtering 137

plot(r.genSwathIonLib.top5@ionlibrary[[demoIdx]])

Normalizing the retention time using iRT peptides

Retention time is an essential parameter in targeted data extraction. However, retention times are difficult to transfer between reverse phase columns or HPLC systems. To make transfer applicable and account for the inter-run shift in retention time Biognosys (Escher et al. 2012) invented the iRT normalization based on iRT / HRM peptides. For this, a set of well-behaving peptides (good flying properties, good fragmentation characteristics, completely artificial) which cover the whole rt-gradient and are spiked into each sample. For this set of peptides, an idependent retention time (dimension less) is suggested by Biognosys. With this at hand, the set of peptides can later be used to apply a linear regression model to adapt all measured retention times into an independent retention time scale.

If the identification results contain iRT peptides, the package supports the conversion to the iRT scale. For this (if the identification the outcome is based on multiple input files), the redundant BiblioSpec file is required where all iRT peptides from all measurements are stored. For the most representative spectrum in the non-redundant R-object the original filename is identified, and the respective linear model for this one particular MS experiment is applied to normalize the retention time to the iRT scale. The iRT peptides, as well as their independent retention times, are stored in the iRTpeptides object.

specL uses by default the iRT peptide table to normalize into the independent retention time but could also be extended or changed to custom iRT peptides if available.

iRTpeptides

##           peptide        rt
## 1       LGGNEQVTR -24.92000
## 2  GAGSSEPVTGLDAK   0.00000
## 3   AAVYHHFISDGVR  10.48963
## 4   VEATFGVDESNAK  12.39000
## 5      YILAGVENSK  19.79000
## 6   HIQNIDIQHLAGK  23.93091
## 7    TPVISGGPYEYR  28.71000
## 8    TPVITGAPYEYR  33.38000
## 9   DGLDAASYYAPVR  42.26000
## 10 TEVSSNHVLIYLDK  43.54062
## 11  ADVTPADFSEWSK  54.62000
## 12 LVAYYTLIGASGQR  64.15480
## 13   GTFIIDPGGVIR  70.52000
## 14 TEHPFTVEEFVLPK  74.50968
## 15 TTNIQGINLLFSSR  84.36927
## 16   GTFIIDPAAVIR  87.23000
## 17 LFLQFGAQGSPFLK 100.00000
## 18  NQGNTWLTAFVLK 104.06935
## 19 DSPVLIDFFEDTER 112.63426
## 20 ITPNLAEFAFSLYR 122.24622
## 21      LGGNETQVR -24.92000
## 22 AGGSSEPVTGLADK   0.00000
## 23  VEATFGVDESANK  12.39000
## 24     YILAGVESNK  19.79000
## 25   TPVISGGPYYER  28.71000
## 26   TPVITGAPYYER  33.38000
## 27  GDLDAASYYAPVR  42.26000
## 28  DAVTPADFSEWSK  54.62000
## 29   TGFIIDPGGVIR  70.52000
## 30   GTFIIDPAAIVR  87.23000
## 31 FLLQFGAQGSPLFK 100.00000

The method genSwathIonLib uses:

fit <- lm(formula = rt ~ aggregateInputRT * fileName, data = m)

to build the linear models for each MS measurement individually. For defining m both data sets were aggregated over the attributes peptide and fileName using the mean operator.

data <- aggregate(df$rt, by = list(df$peptide, df$fileName),
  FUN = mean)
data.fit <- aggregate(df.fit$rt, 
  by = list(df.fit$peptide, df.fit$fileName), 
  FUN = mean)

Afterwards the following join operator was applied.

m <- merge(iRT, data.fit, by.x='peptide', by.y='peptide')

The following graph displays the normalized retention time versus the measured retention time after applying the calculated model to the two data sets.

# calls the plot method for a specLSet object
op <- par(mfrow=c(2,3)) 
plot(res.genSwathIonLib)

## [1] 16.83185 13.13262 18.54058 18.36923 15.30478 15.30478

## [1]  7.032372  6.490769 14.787681 14.544429 15.207398
## [6] 15.207398

par(op)

Shown are the original retention time (in minutes) and iRT (dimensionless) for two standard run experiments (color black and red). Indicated with black {} are the iRT peptides, which are the base for the regression.

Generate the spectral library having no iRTs

If no iRT peptides are contained in the data, not iRT normalization is applied. The scatter plot below shows on the y-axis that there was not iRT transformation.

idx.iRT <- which(unlist(lapply(peptideStd,
  function(x){
    if(x$peptideSequence %in% iRTpeptides$peptide){0}
    else{1}
  })) == 0)

# remove all iRTs and compute ion library
res.genSwathIonLib.no_iRT <- genSwathIonLib(peptideStd[-idx.iRT])

## normalizing RT ...

## no iRT peptides found for building the model.

## => no iRT regression applied, using orgiginal rt instead!

## generating ion library ...

## start generating specLSet object ...

## length of findNN idx  129

## length of genSwathIonLibSpecL   129

## time taken:  0.126365184783936 secs

## length of genSwathIonLibSpecL  after fragmentIonRange filtering 129

summary(res.genSwathIonLib.no_iRT)

## Summary of a "specLSet" object.
## 
## Parameter:
## 
## Number of precursor (q1 and peptideModSeq) = 129
## Number of unique precursor
## (q1.in-silico and peptideModSeq) = 118
## Number of iRT peptide(s) = 0
## Number of transitions frequency:
##  4   1
##  5   5
##  6   10
##  7   7
##  8   17
##  9   31
##  10  58
## 
## Number of annotated precursor = 0
## Number of file(s)
##  2
## 
## Number of precursors in Filename(s)
##  0140910_01_fetuin_400amol_1.raw 18
##  0140910_07_fetuin_400amol_2.raw 111
## 
## Misc:
## 
## Memory usage =    630368 bytes

op <- par(mfrow = c(2, 3)) 
plot(res.genSwathIonLib.no_iRT)

## [1] 16.83185 18.54058 18.36923 15.30478 15.30478 19.36682

## [1]  7.032372  6.490769 14.787681 14.544429 15.207398
## [6] 15.207398

par(op)

Write output to file

The output can be written as an ASCII text file.

write.spectronaut(res.genSwathIonLib,
  file="specL-Spectronaut.txt")

## writting specL object (including header) to file 'specL-Spectronaut.txt' ...

What can I do with that library now?

The specL output text file can directly be used as input (assay) for the Spectronaut software from Biognosys or with minimal reshaping for Peakview. Alternatively, it can be used as a basis for script-based construction of SRM/MRM assays.