---
title: "Prepare Peptide Spectrum Matches for Use in Targeted Proteomics"
author:
- name: Christian Panse
affiliation:
- &fgcz Functional Genomics Center Zurich - Swiss Federal Institute of Technology in Zurich
- &sib Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, CH-1015 Lausanne, Switzerland
email: cp@fgcz.ethz.ch
- name: Christian Trachsel
affiliation:
- *fgcz
- name: Jonas Grossmann
email: jg@fgcz.ethz.ch
affiliation:
- *fgcz
- *sib
- name: Witold E. Wolski
affiliation:
- *fgcz
- *sib
email: wew@fgcz.ethz.ch
date: "`r doc_date()`"
package: specL
abstract: |
Targeted data extraction methods are attractive ways to obtain
quantitative peptide information from a proteomics experiment.
Sequential Window Acquisition of all Theoretical Spectra (SWATH) and
Data Independent Acquisition (DIA) methods increase reproducibility of
acquired data because the classical precursor selection is omitted and
all present precursors are fragmented. However, especially for targeted
data extraction, MS coordinates (retention time information precursor
and fragment masses) are required for the particular entities (peptide
ions). These coordinates are usually generated in a so-called discovery
experiment earlier on in the project if not available in public spectral
library repositories. The quality of the assay panel is crucial to
ensure appropriate downstream analysis. For that, a method is needed to
create spectral libraries and to export customizable assay panels.
vignette: |
%\VignetteIndexEntry{Introduction to specL}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
bibliography: specL.bib
output:
BiocStyle::html_document:
toc_float: true
---
# Introduction
Targeted proteomics is a fast evolving field in proteomics science and
was even elected as the method of the year in 2012
\footnote{\url{http://www.nature.com/nmeth/journal/v10/n1/pdf/nmeth.2329.pdf},
2014-09-22}. Especially targeted methods like SWATH [@SWATH] open
promising perspectives for for identifying and quantifying of
peptides and proteins. All targeted
methods have in common the need of precise MS coordinates composed
of precursor mass, fragment masses, and retention time. The combination
of this information is kept in so-called assays or spectra libraries. Here we
present an R package able to produce such libraries out of peptide
identification results (Mascot (dat), TPP (pep.xml and mzXMLs),
ProteinPilot (group), Omssa (omx)).
`r BiocStyle::Biocpkg('specL')` [@specLBioInf] is an easy-to-use,
versatile, and flexible function,
which can be integrated into already existing commercial
or non-commercial analysis pipelines for targeted proteomics
data analysis. Some examples of today's pipelines are ProteinPilot
combined with Peakview (AB Sciex), Spectronaut (Biognosys) or
OpenSwath [@pmid24727770].
In the following vignette it is described how the `r BiocStyle::Biocpkg('specL')` package
can be used for the included data sets `peptideStd` and
`peptideStd.redundant`.
# Workflow
## 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 [@pmid18428681] algorithm
implemented in Skyline [@pmid20147306]. 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\footnote{sqlite uses standart SQL as query language.}
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 [@pmid18806791]).
The first step which has to be performed on the R shell is loading
`r BiocStyle::Biocpkg('specL')` library.
```{r echo=FALSE, eval = TRUE, fig = FALSE, echo=FALSE}
options(prompt = "R> ",
continue = "+ ",
width = 60,
useFancyQuotes = FALSE,
warn = -1)
```
```{r load}
library(specL)
packageVersion('specL')
```
## Read from redundant plus non-redundant blib files
for demonstration, `r BiocStyle::Biocpkg('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.
```{r summry}
summary(peptideStd)
```
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.
```{r peptideStd}
# plot(peptideStd)
plot(0,0, main='MISSING')
```
The individual peptide spectrum match (psm) is displayed by using the
`r BiocStyle::CRANpkg('protViz')` `peakplot` function.
```{r pepttidePlot}
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
`r BiocStyle::CRANpkg('protViz')` perl script
`protViz\-\_mascotDat2RData.pl`.
The Perl script can be found in the exec directory of the
`r BiocStyle::CRANpkg('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`
```{r mascot2psmSet}
specL:::.mascot2psmSet
```
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 `r BiocStyle::Biocpkg('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
`r BiocStyle::CRANpkg('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.
```{r irtFASTAseq}
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.
```{r}
peptideStd[[demoIdx]]$proteinInformation
```
The protein information can be added as follow:
```{r}
peptideStd <- annotate.protein_id(peptideStd,
fasta=fasta.irtFASTAseq)
```
The following lines now show the object indices of those entries which do
have protein information now.
```{r}
(idx <- which(unlist(lapply(peptideStd,
function(x){nchar(x$proteinInformation)>0}))))
```
As expected, there are now a number of peptide sequences
annotated with the protein ID.
```{r}
peptideStd[[demoIdx]]$proteinInformation
```
Of note, that the default digest pattern is defined as
```{r}
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
`r BiocStyle::Biocpkg('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.
```{r}
res.genSwathIonLib <- genSwathIonLib(data = peptideStd,
data.fit = peptideStd.redundant)
```
genSwathIonLib default settings
| parameter | description | value |
|-------------------|--------------------------------|-----------------|
|max.mZ.Da.error | max ms2 tolerance | 0.1 |
|topN | the n most intense fragment ion| 10 |
|fragmentIonMzRange |mZ range filter of fragment ion | c(300, 1800) |
|fragmentIonRange |min/max number of fragment ions |c(5,100) |
|fragmentIonFUN} |desired fragment ion types|b1+,y1+,b2+,y2+,b3+,y3+|
```{r}
summary(res.genSwathIonLib)
```
The determined mass spec coordinates of the selected tandem mass spectrum
`demoIdx` look like this:
```{r}
res.genSwathIonLib@ionlibrary[[demoIdx]]
```
It can be displayed using the \Rfunction{specL::plot} function.
```{r fig.retina=3}
plot(res.genSwathIonLib@ionlibrary[[demoIdx]])
```
The following code considers only the top five y ions.
```{r fig.retina=3}
# 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) )
}
)
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
[@pmid22577012] 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.
`r BiocStyle::Biocpkg('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.
```{r}
iRTpeptides
```
The method genSwathIonLib uses:
```{r fit, eval=FALSE}
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.
```{r eval=FALSE}
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$fileName),
FUN = mean)
```
Afterwards the following join operator was applied.
```{r merge, eval=FALSE}
m <- merge(iRT, data.fit, by.x='peptide', by.y='peptide')
```
The following graph displays the normalized retention time versus
the measured retention time after applying the calculated model
to the two data sets.
```{r fig.retina=3}
# calls the plot method for a specLSet object
op <- par(mfrow=c(2,3))
plot(res.genSwathIonLib)
par(op)
```
Shown are the original retention time (in minutes) and iRT (dimensionless)
for two standard run experiments (color black and red). Indicated
with black {\bf X} are the iRT peptides, which are the base for the
regression.
## Generate the spectral library having no iRTs
If no iRT peptides are contained in the data, not iRT normalization is applied.
The scatter plot below shows on the y-axis that there was not iRT transformation.
```{r fig.retina=3}
idx.iRT <- which(unlist(lapply(peptideStd,
function(x){
if(x$peptideSequence %in% iRTpeptides$peptide){0}
else{1}
})) == 0)
# remove all iRTs and compute ion library
res.genSwathIonLib.no_iRT <- genSwathIonLib(peptideStd[-idx.iRT])
summary(res.genSwathIonLib.no_iRT)
op <- par(mfrow = c(2, 3))
plot(res.genSwathIonLib.no_iRT)
par(op)
```
## Write output to file
The output can be written as an ASCII text file.
```{r}
write.spectronaut(res.genSwathIonLib,
file="specL-Spectronaut.txt")
```
# Epilogue
## What can I do with that library now?
The `r BiocStyle::Biocpkg('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.
# Benchmark
The benchmarks were processed on a
12 core XEON Server (X5650 @ 2.67GHz) running Linux Debian wheezy
having
R version 3.1.1 (2014-07-10) , specL 1.1.2, and BiocParallel 1.0.0
installed. The default setting of BiocParallel uses eight cores.
As FASTA, we used a TAIR10 retrieved from \url{http://www.arabidopsis.org/} and Human Swissprot.
```
\begin{table}[h]
\centering
\resizebox{.99\textwidth}{!}{
\begin{tabular}{rrr|rr|rr}
\hline
fasta=TAIR10 & & & blib [unpublished] & & runtime & \\
\#proteins & \#tryptic peptides & file size & \#specs & file size & annotate & generate\\\hline \hline
71032 & 3423196 & 39M & 39648/118268 & 51M & 79min & 19sec \\
71032 & 3423196 & 39M & 65018/136963 & 120M & 130min & 30sec \\
\hline
fasta=HUMAN & & & blib \cite[Rosenberger]{Rosenberger} & & & \\
88969& 3997085 & 43M & 256908/3060421 & 4.4G & $\approx$7h &$\approx$5min \\
%HUMAN\footnote{Rosenberger et al. in Scientific Data (doi:10.1038/sdata.2014.31)} && & & & 256908/3060421& & 4.4G & & $\approx$5min\\
\hline
\end{tabular}
}
\end{table}
```
The following parameter settings were given to the `genSwathIonLib` function:
```{r eval=FALSE}
res <- genSwathIonLib(data, data.fit,
topN=10,
fragmentIonMzRange=c(200,2000),
fragmentIonRange=c(2,100))
```
# Acknowledgement
The authors thank all colleagues of the Functional Genomics Center
Zuerich (FGCZ), and especial thank goes to our test users
Sira Echevarr\'{i}a~Zome\~{n}o (ETHZ),
Tobias Kockmann (ETHZ),
Lukas von Ziegler (Brain Research Institute, UZH/ETH Zurich),
and Stephan~Michalik (Ernst-Moritz-Arndt-Universität Greifswald, Germany).
# TODO for next releases
- importer for peakview csv format; enable \Rcode{compare.specLSet(object0, object1)}
- new option for \Rfunction{specL::genSwathIonLib}; Exclude fragment
ions from precursor \Rcode{window = TRUE, FALSE}
- new option for \Rfunction{specL::genSwathIonLib}; Predict
transitions for heavy labeled peptides using information from light
peptides \Rcode{predictHeavy = TRUE,FALSE, LabelFile =
"fileWithHeavyAA"}
- new export function into TraML format for compatibility with OpenSWATH \cite{pmid24727770}
- replace \CRANpkg{seqinr} \Rfunction{read.fasta} by using \Biocpkg{Biostrings} \Rfunction{readAAStringSet}
to handle fasta files
- add varMods to specL class
- replace Mascot score by a generic score
- in-silico rt ion map plot (\Rfunction{plot.specLSet})
split window into SWATH windows (one plot per, e.g., 25Da window)
- assay refinement - replace contaminated fragment ion in library
# Session information
An overview of the package versions used to produce this document are
shown below.
```{r sessionInfo, echo=FALSE}
sessionInfo()
```
# References