A parser for raw and identification mass-spectrometry data
19 November 2024
Package
mzR 2.41.1
1 Introduction
The mzR package aims at providing a common,
low-level interface to several mass spectrometry data formats, namely,
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 r BiocStyle::Biocpkg("mzR"), which is left to packages such as r BiocStyle::Biocpkg("xcms") (Smith et al. 2006, Tautenhahn:2008) or r BiocStyle::Biocpkg("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 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. 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 files
A short example sequence to read data from a mass spectrometer. First open the file.
library(mzR)## Loading required package: Rcpplibrary(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] NAinstrumentInfo(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 acquisitionNum msLevel polarity peaksCount totIonCurrent retentionTime
## 1 1 1 1 1 684 341427000 0.3485
## basePeakMZ basePeakIntensity collisionEnergy ionisationEnergy lowMZ highMZ
## 1 120.066 211860000 NA 0 50.3254 298.673
## precursorScanNum precursorMZ precursorCharge precursorIntensity mergedScan
## 1 NA NA NA NA NA
## mergedResultScanNum mergedResultStartScanNum mergedResultEndScanNum
## 1 NA NA NA
## injectionTime filterString spectrumId
## 1 0 <NA> controllerType=0 controllerNumber=1 scan=1
## centroided ionMobilityDriftTime isolationWindowTargetMZ
## 1 TRUE NA NA
## isolationWindowLowerOffset isolationWindowUpperOffset scanWindowLowerLimit
## 1 NA NA 50.3254
## scanWindowUpperLimit
## 1 298.673Read a single spectrum from the file.
pl <- peaks(aa,10)
peaksCount(aa,10)## [1] 317head(pl)## mz intensity
## [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 8012.808plot(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 3.0.21263 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 14Since 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.