# A parser for raw and identification mass-spectrometry data

Bernd Fischer, Steffen Neumann, Laurent Gatto and Qiang Kou

#### 18 November 2020

#### Package

mzR 2.24.1

# 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).

IMPORTANT New developers that need to access and manipulate raw mass spectrometry data are advised against using this infrastucture directly. They are invited to use the corresponding `MSnExp` (with on disk mode) from theMSnbase package instead. The latter supports reading multiple files at once and offers access to the spectra data (m/z and intensity) as well as all the spectra metadata using a coherent interface. The MSnbase infrastructure itself used the low level classes in mzR, thus offering fast and efficient access.

# 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] NA
    ##
    ## $ionisationEnergy
    ## [1] 0
    ##
    ## $lowMZ
    ## [1] 50.3254
    ##
    ## $highMZ
    ## [1] 298.673
    ##
    ## $precursorScanNum
    ## [1] NA
    ##
    ## $precursorMZ
    ## [1] NA
    ##
    ## $precursorCharge
    ## [1] NA
    ##
    ## $precursorIntensity
    ## [1] NA
    ##
    ## $mergedScan
    ## [1] NA
    ##
    ## $mergedResultScanNum
    ## [1] NA
    ##
    ## $mergedResultStartScanNum
    ## [1] NA
    ##
    ## $mergedResultEndScanNum
    ## [1] NA
    ##
    ## $injectionTime
    ## [1] 0
    ##
    ## $filterString
    ## [1] NA
    ##
    ## $spectrumId
    ## [1] "controllerType=0 controllerNumber=1 scan=1"
    ##
    ## $centroided
    ## [1] TRUE
    ##
    ## $ionMobilityDriftTime
    ## [1] NA
    ##
    ## $isolationWindowTargetMZ
    ## [1] NA
    ##
    ## $isolationWindowLowerOffset
    ## [1] NA
    ##
    ## $isolationWindowUpperOffset
    ## [1] NA
    ##
    ## $scanWindowLowerLimit
    ## [1] NA
    ##
    ## $scanWindowUpperLimit
    ## [1] NA

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"                 "peptideRef"
    ##  [9] "modNum"                   "isDecoy"
    ## [11] "post"                     "pre"
    ## [13] "start"                    "end"
    ## [15] "DatabaseAccess"           "DBseqLength"
    ## [17] "DatabaseSeq"              "DatabaseDescription"
    ## [19] "spectrum.title"           "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
    ## 1   index=12  LCYIALDFDEEMKAAEDSSDIEK
    ## 2   index=12  LCYIALDFDEEMKAAEDSSDIEK
    ## 3  index=285   KDLYGNVVLSGGTTMYEGIGER
    ## 4   index=83   KDLYGNVVLSGGTTMYEGIGER
    ## 5   index=21 VIDENFGLVEGLMTTVHAATGTQK
    ## 6  index=198          GVGGAIVLVLYDEMK
    ##                                            peptideRef            name    mass
    ## 1 LCYIALDFDEEMKAAEDSSDIEK_15.9949@M$12;_57.0215@C$2;_ Carbamidomethyl 57.0215
    ## 2 LCYIALDFDEEMKAAEDSSDIEK_15.9949@M$12;_57.0215@C$2;_       Oxidation 15.9949
    ## 3              KDLYGNVVLSGGTTMYEGIGER_15.9949@M$15;__       Oxidation 15.9949
    ## 4              KDLYGNVVLSGGTTMYEGIGER_15.9949@M$15;__       Oxidation 15.9949
    ## 5            VIDENFGLVEGLMTTVHAATGTQK_15.9949@M$13;__       Oxidation 15.9949
    ## 6                     GVGGAIVLVLYDEMK_15.9949@M$14;__       Oxidation 15.9949
    ##   location
    ## 1        2
    ## 2       12
    ## 3       15
    ## 4       15
    ## 5       13
    ## 6       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.