1 Introduction

The mzR package aims at providing a common, low-level interface to several mass spectrometry data formats, namely mzData (Orchard et al. 2007), mzXML (Pedrioli et al. 2004), mzML (Martens et al. 2010) for raw data, and mzIdentML (Jones et al. 2012), somewhat similar to the Bioconductor package affyio for affymetrix raw data. No processing is done in mzR, which is left to packages such as r BiocStyle::Biocpkg("xcms") (Smith et al. 2006, Tautenhahn:2008) or MSnbase (Gatto and Lilley 2012). These packages also provide more convenient, high-level interfaces to raw and identification. data

Most importantly, access to the data should be fast and memory efficient. This is made possible by allowing on-disk random file access, i.e. retrieving specific data of interest without having to sequentially browser the full content nor loading the entire data into memory.

The actual work of reading and parsing the data files is handled by the included C/C++ libraries or backends. The mzRramp RAMP parser, written at the Institute for Systems Biology (ISB) is a fast and lightweight parser in pure C. Later, it gained support for the mzData format. The C++ reference implementation for the mzML is the proteowizard library (Kessner et al. 2008) (pwiz in short), which in turn makes use of the boost C++ (http://www.boost.org/) library. RAMP is able to access mzML files by calling pwiz methods. More recently, the proteowizard (http://proteowizard.sourceforge.net/) (Chambers et al. 2012) has been fully integrated using the mzRpwiz backend for raw data, and is not the default option. The mzRnetCDF backend provides support to CDF-based formats. Finally, the mzRident backend is available to access identification data (mzIdentML) through pwiz.

The mzR package is in essence a collection of wrappers to the C++ code, and benefits from the C++ interface provided through the Rcpp package (Eddelbuettel and François 2011).

2 Mass spectrometry raw data

All the mass spectrometry file formats are organized similarly, where a set of metadata nodes about the run is followed by a list of spectra with the actual masses and intensities. In addition, each of these spectra has its own set of metadata, such as the retention time and acquisition parameters.

2.1 Spectral data access

Access to the spectral data is done via the peaks function. The return value is a list of two-column mass-to-charge and intensity matrices or a single matrix if one spectrum is queried.

2.2 Chromatogram access

Access to the chromatogram(s) is done using the chromatogram (or chromatograms) function, that return one (or a list of) data.frames. See ?chromatogram for details. This functionality is only available with the pwiz backend.

2.3 Identification result access

The main access to identification result is done via psms, score and modifications. psms and score will return the detailed information on each psm and scores. modifications will return the details on each modification found in peptide.

2.4 Metadata access

Run metadata is available via several functions such as instrumentInfo() or runInfo(). The individual fields can be accessed via e.g. detector() etc.

Spectrum metadata is available via header(), which will return a list (for single scans) or a dataframe with information such as the basePeakMZ, peaksCount, … or, for higher-order MS the msLevel and precursor information.

Identification metadatais available via mzidInfo(), which will return a list with information such as the software, ModificationSearched, enzymes, SpectraSource and other information for this identification result.

The availability of this metadata can not always be guaranteed, and depends on the MS software which converted the data.

3 Example

3.1 mzXML/mzML/mzData files

A short example sequence to read data from a mass spectrometer. First open the file.

library(mzR)
## Loading required package: Rcpp
library(msdata)

mzxml <- system.file("threonine/threonine_i2_e35_pH_tree.mzXML", 
                     package = "msdata")
aa <- openMSfile(mzxml)

We can obtain different kind of header information.

runInfo(aa)
## $scanCount
## [1] 55
## 
## $lowMz
## [1] 50.0036
## 
## $highMz
## [1] 298.673
## 
## $dStartTime
## [1] 0.3485
## 
## $dEndTime
## [1] 390.027
## 
## $msLevels
## [1] 1 2 3 4
## 
## $startTimeStamp
## [1] NA
instrumentInfo(aa)
## $manufacturer
## [1] "Thermo Scientific"
## 
## $model
## [1] "LTQ Orbitrap"
## 
## $ionisation
## [1] "electrospray ionization"
## 
## $analyzer
## [1] "fourier transform ion cyclotron resonance mass spectrometer"
## 
## $detector
## [1] "unknown"
## 
## $software
## [1] "Xcalibur software 2.2 SP1"
## 
## $sample
## [1] ""
## 
## $source
## [1] ""
header(aa,1)
## $seqNum
## [1] 1
## 
## $acquisitionNum
## [1] 1
## 
## $msLevel
## [1] 1
## 
## $polarity
## [1] 1
## 
## $peaksCount
## [1] 684
## 
## $totIonCurrent
## [1] 341427000
## 
## $retentionTime
## [1] 0.3485
## 
## $basePeakMZ
## [1] 120.066
## 
## $basePeakIntensity
## [1] 211860000
## 
## $collisionEnergy
## [1] 0
## 
## $ionisationEnergy
## [1] 0
## 
## $lowMZ
## [1] 50.3254
## 
## $highMZ
## [1] 298.673
## 
## $precursorScanNum
## [1] 0
## 
## $precursorMZ
## [1] 0
## 
## $precursorCharge
## [1] 0
## 
## $precursorIntensity
## [1] 0
## 
## $mergedScan
## [1] 0
## 
## $mergedResultScanNum
## [1] 0
## 
## $mergedResultStartScanNum
## [1] 0
## 
## $mergedResultEndScanNum
## [1] 0
## 
## $injectionTime
## [1] 0
## 
## $filterString
## [1] NA
## 
## $spectrumId
## [1] "controllerType=0 controllerNumber=1 scan=1"
## 
## $centroided
## [1] TRUE

Read a single spectrum from the file.

pl <- peaks(aa,10)
peaksCount(aa,10)
## [1] 317
head(pl)
##          [,1]     [,2]
## [1,] 50.08176 6984.858
## [2,] 50.62267 7719.419
## [3,] 50.70530 7185.290
## [4,] 50.73298 7509.140
## [5,] 50.83848 9366.624
## [6,] 50.88303 8012.808
plot(pl[,1], pl[,2], type="h", lwd=1)

One should always close the file when not needed any more. This will release the memory of cached content.

close(aa)

3.2 mzIdentML files

You can use openIDfile to read a mzIdentML file (version 1.1), which use the pwiz backend.

library(mzR)
library(msdata)

file <- system.file("mzid", "Tandem.mzid.gz", package="msdata")
x <- openIDfile(file)

mzidInfo function will return general information about this identification result.

mzidInfo(x)
## $FileProvider
## [1] "researcher"
## 
## $CreationDate
## [1] "2012-07-25T14:03:16"
## 
## $software
## [1] "xtandem x! tandem CYCLONE (2010.06.01.5) "  
## [2] "ProteoWizard MzIdentML 3.0.501 ProteoWizard"
## 
## $ModificationSearched
## [1] "Oxidation"       "Carbamidomethyl"
## 
## $FragmentTolerance
## [1] "0.8 dalton"
## 
## $ParentTolerance
## [1] "1.5 dalton"
## 
## $enzymes
## $enzymes$name
## [1] "Trypsin"
## 
## $enzymes$nTermGain
## [1] "H"
## 
## $enzymes$cTermGain
## [1] "OH"
## 
## $enzymes$minDistance
## [1] "0"
## 
## $enzymes$missedCleavages
## [1] "1"
## 
## 
## $SpectraSource
## [1] "D:/TestSpace/NeoTestMarch2011/55merge.mgf"

psms will return the detailed information on each peptide-spectrum-match, include spectrumID, chargeState, sequence. modNum and others.

p <- psms(x)
colnames(p)
##  [1] "spectrumID"               "chargeState"             
##  [3] "rank"                     "passThreshold"           
##  [5] "experimentalMassToCharge" "calculatedMassToCharge"  
##  [7] "sequence"                 "modNum"                  
##  [9] "isDecoy"                  "post"                    
## [11] "pre"                      "start"                   
## [13] "end"                      "DatabaseAccess"          
## [15] "DBseqLength"              "DatabaseSeq"             
## [17] "DatabaseDescription"      "acquisitionNum"

The modifications information can be accessed using modifications, which will return the spectrumID, sequence, name, mass and location.

m <- modifications(x)
head(m)
##   spectrumID                 sequence            name    mass location
## 1   index=12  LCYIALDFDEEMKAAEDSSDIEK Carbamidomethyl 57.0215        2
## 2   index=12  LCYIALDFDEEMKAAEDSSDIEK       Oxidation 15.9949       12
## 3  index=285   KDLYGNVVLSGGTTMYEGIGER       Oxidation 15.9949       15
## 4   index=83   KDLYGNVVLSGGTTMYEGIGER       Oxidation 15.9949       15
## 5   index=21 VIDENFGLVEGLMTTVHAATGTQK       Oxidation 15.9949       13
## 6  index=198          GVGGAIVLVLYDEMK       Oxidation 15.9949       14

Since different software will use different scoring function, we provide a score to extract the scores for each psm. It will return a data.frame with different columns depending on software generating this file.

scr <- score(x)
colnames(scr)
## [1] "spectrumID"          "X.Tandem.expect"     "X.Tandem.hyperscore"

4 Future plans

Other file formats provided by HUPO, such as mzQuantML for quantitative data (Walzer et al. 2013) are also possible in the future.

5 Session information

Chambers, Matthew C., Brendan Maclean, Robert Burke, Dario Amodei, Daniel L. Ruderman, Steffen Neumann, Laurent Gatto, et al. 2012. “A cross-platform toolkit for mass spectrometry and proteomics.” Nat Biotech 30 (10). Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.:918–20. https://doi.org/10.1038/nbt.2377.

Eddelbuettel, Dirk, and Romain François. 2011. “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software 40 (8):1–18. http://www.jstatsoft.org/v40/i08/.

Gatto, L, and K S Lilley. 2012. “MSnbase – an R/Bioconductor Package for Isobaric Tagged Mass Spectrometry Data Visualization, Processing and Quantitation.” Bioinformatics 28 (2):288–9. https://doi.org/10.1093/bioinformatics/btr645.

Jones, A R, M Eisenacher, G Mayer, O Kohlbacher, J Siepen, S J Hubbard, J N Selley, et al. 2012. “The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results.” Mol Cell Proteomics 11 (7):M111.014381. https://doi.org/10.1074/mcp.M111.014381.

Kessner, Darren, Matt Chambers, Robert Burke, David Agus, and Parag Mallick. 2008. “ProteoWizard: Open Source Software for Rapid Proteomics Tools Development.” Bioinformatics 24 (21):2534–6. https://doi.org/10.1093/bioinformatics/btn323.

Martens, Lennart, Matthew Chambers, Marc Sturm, Darren Kessner, Fredrik Levander, Jim Shofstahl, Wilfred H Tang, et al. 2010. “MzML - a Community Standard for Mass Spectrometry Data.” Molecular and Cellular Proteomics : MCP. https://doi.org/10.1074/mcp.R110.000133.

Orchard, Sandra, Luisa Montechi-Palazzi, Eric W Deutsch, Pierre-Alain Binz, Andrew R Jones, Norman Paton, Angel Pizarro, David M Creasy, Jérôme Wojcik, and Henning Hermjakob. 2007. “Five Years of Progress in the Standardization of Proteomics Data 4th Annual Spring Workshop of the Hupo-Proteomics Standards Initiative April 23-25, 2007 Ecole Nationale Supérieure (Ens), Lyon, France.” Proteomics 7 (19):3436–40. https://doi.org/10.1002/pmic.200700658.

Pedrioli, Patrick G A, Jimmy K Eng, Robert Hubley, Mathijs Vogelzang, Eric W Deutsch, Brian Raught, Brian Pratt, et al. 2004. “A Common Open Representation of Mass Spectrometry Data and Its Application to Proteomics Research.” Nat. Biotechnol. 22 (11):1459–66. https://doi.org/10.1038/nbt1031.

Smith, C A, E J Want, G O’Maille, R Abagyan, and G Siuzdak. 2006. “XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification.” Anal Chem 78 (3):779–87. https://doi.org/10.1021/ac051437y.

Walzer, M, D Qi, G Mayer, J Uszkoreit, M Eisenacher, T Sachsenberg, F F Gonzalez-Galarza, et al. 2013. “The mzQuantML Data Standard for Mass Spectrometry-Based Quantitative Studies in Proteomics.” Mol Cell Proteomics 12 (8):2332–40. https://doi.org/10.1074/mcp.O113.028506.