Package: MsBackendMgf
Authors: RforMassSpectrometry Package Maintainer [cre],
Laurent Gatto [aut] (ORCID: https://orcid.org/0000-0002-1520-2268), Johannes Rainer
[aut] (ORCID: https://orcid.org/0000-0002-6977-7147), Sebastian Gibb
[aut] (ORCID: https://orcid.org/0000-0001-7406-4443), Michael Witting
[ctb] (ORCID: https://orcid.org/0000-0002-1462-4426), Adriano Rutz
[ctb] (ORCID: https://orcid.org/0000-0003-0443-9902)
Last modified: 2024-11-20 11:10:46.201777
Compiled: Wed Nov 20 11:13:25 2024
Introduction
The Spectra package provides a central infrastructure for the handling of Mass Spectrometry (MS) data. The package supports interchangeable use of different backends to import MS data from a variety of sources (such as mzML files). The MsBackendMgf package allows the import of MS/MS data from mgf (Mascot Generic Format) files. This vignette illustrates the usage of the MsBackendMgf package.
Installation
To install this package, start R and enter:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MsBackendMgf")This will install this package and all eventually missing dependencies.
Importing MS/MS data from mgf files
Mgf files store one to multiple spectra, typically centroided and of MS level 2. In our short example below, we load 2 mgf files which are provided with this package. Below we first load all required packages and define the paths to the mgf files.
library(Spectra)
library(MsBackendMgf)
fls <- dir(system.file("extdata", package = "MsBackendMgf"),
full.names = TRUE, pattern = "mgf$")
fls## [1] "/__w/_temp/Library/MsBackendMgf/extdata/spectra.mgf"
## [2] "/__w/_temp/Library/MsBackendMgf/extdata/spectra2.mgf"
## [3] "/__w/_temp/Library/MsBackendMgf/extdata/spectra3_empty_peaks.mgf"
## [4] "/__w/_temp/Library/MsBackendMgf/extdata/spectra4.mgf"MS data can be accessed and analyzed through Spectra
objects. Below we create a Spectra with the data from these
mgf files. To this end we provide the file names and specify to use a
MsBackendMgf() backend as source to enable data
import.
sps <- Spectra(fls, source = MsBackendMgf())## Start data import from 4 files ... doneWith that we have now full access to all imported spectra variables that we list below.
spectraVariables(sps)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "dataStorage" "dataOrigin"
## [7] "centroided" "smoothed"
## [9] "polarity" "precScanNum"
## [11] "precursorMz" "precursorIntensity"
## [13] "precursorCharge" "collisionEnergy"
## [15] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz" "TITLE"
## [19] "RAWFILE" "CLUSTER_ID"
## [21] "MSLEVEL"Besides default spectra variables, such as msLevel,
rtime, precursorMz, we also have additional
spectra variables such as the TITLE of each spectrum in the
mgf file.
sps$rtime## [1] 1028.000 1117.000 1127.000 2678.940 2373.511 2511.030 NA 162.070
## [9] 1028.000 1028.000
sps$TITLE## [1] "File193 Spectrum1719 scans: 2162"
## [2] "File193 Spectrum1944 scans: 2406"
## [3] "File193 Spectrum1968 scans: 2432"
## [4] "mzspec:PXD004732:01650b_BC2-TUM_first_pool_53_01_01-3xHCD-1h-R2:scan:41840:WNQLQAFWGTGK/2"
## [5] "mzspec:PXD002084:TCGA-AA-A01D-01A-23_W_VU_20121106_A0218_5I_R_FR15:scan:5209:DLTDYLMK/2"
## [6] "mzspec:MSV000080679:j11962_C1orf144:scan:10671:DLTDYLMK/2"
## [7] "CCMSLIB00000840351"
## [8] "blank_2-A,1_01_29559.812.812.1"
## [9] "File193 Spectrum1719 scans: 2162"
## [10] "File193 Spectrum1719 scans: 2162"By default, fields in the mgf file are mapped to spectra variable
names using the mapping returned by the
spectraVariableMapping function:
## rtime acquisitionNum precursorMz precursorIntensity
## "RTINSECONDS" "SCANS" "PEPMASS" "PEPMASSINT"
## precursorCharge
## "CHARGE"The names of this character vector are the spectra
variable names (such as "rtime") and the field in the mgf
file that contains that information are the values (such as
"RTINSECONDS"). Note that it is also possible to overwrite
this mapping (e.g. for certain mgf dialects) or to add
additional mappings. Below we add the mapping of the mgf field
"TITLE" to a spectra variable called
"spectrumName".
map <- c(spectrumName = "TITLE", spectraVariableMapping(MsBackendMgf()))
map## spectrumName rtime acquisitionNum precursorMz
## "TITLE" "RTINSECONDS" "SCANS" "PEPMASS"
## precursorIntensity precursorCharge
## "PEPMASSINT" "CHARGE"We can then pass this mapping to the backendInitialize
method, or the Spectra constructor.
sps <- Spectra(fls, source = MsBackendMgf(), mapping = map)## Start data import from 4 files ... doneWe can now access the spectrum’s title with the newly created spectra
variable "spectrumName":
sps$spectrumName## [1] "File193 Spectrum1719 scans: 2162"
## [2] "File193 Spectrum1944 scans: 2406"
## [3] "File193 Spectrum1968 scans: 2432"
## [4] "mzspec:PXD004732:01650b_BC2-TUM_first_pool_53_01_01-3xHCD-1h-R2:scan:41840:WNQLQAFWGTGK/2"
## [5] "mzspec:PXD002084:TCGA-AA-A01D-01A-23_W_VU_20121106_A0218_5I_R_FR15:scan:5209:DLTDYLMK/2"
## [6] "mzspec:MSV000080679:j11962_C1orf144:scan:10671:DLTDYLMK/2"
## [7] "CCMSLIB00000840351"
## [8] "blank_2-A,1_01_29559.812.812.1"
## [9] "File193 Spectrum1719 scans: 2162"
## [10] "File193 Spectrum1719 scans: 2162"In addition we can also access the m/z and intensity values of each spectrum.
mz(sps)## NumericList of length 10
## [[1]] 102.0548 103.00494 103.03531 ... 1388.58691 1405.59729 1406.57666
## [[2]] 101.07074 102.05486 103.00227 ... 1331.56726 1348.58496 1349.59241
## [[3]] 102.05556 103.00014 115.05058 ... 1333.599 1334.61304 1335.64368
## [[4]] 101.07122 109.68925 115.86999 120.0811 ... 1260.6073 1261.614 1272.6572
## [[5]] 130.164459228516 144.150299072266 ... 1019.23852539062 1020.52404785156
## [[6]] 110.070594787598 120.080627441406 ... 887.756652832031 998.447387695312
## [[7]] 51.022404 57.033543 57.060638 ... 636.130188 660.481445 753.358521
## [[8]] numeric(0)
## [[9]] 102.0548 103.00494 103.03531 ... 1388.58691 1405.59729 1406.57666
## [[10]] 102.0548 103.00494 103.03531 ... 1388.58691 1405.59729 1406.57666
intensity(sps)## NumericList of length 10
## [[1]] 753.738 385.376 315.441 413.206 ... 3038.73 2016.43 1146.04 704.175
## [[2]] 1228.93 1424.66 1550.9 1455.45 ... 7380.41 4960.92 5743.83 1780.76
## [[3]] 1340.44 1714.76 1938.82 1450.36 2019 ... 5323.02 2265.43 4768.14 1532.12
## [[4]] 81011.57 4123.349 4006.9321 66933.17 ... 22042.248 18096.48 12666.438
## [[5]] 14.1766004562378 18.5806427001953 ... 22.7096385955811 14.864013671875
## [[6]] 1748.57495117188 8689.9951171875 ... 2907.08422851562 2663.30908203125
## [[7]] 65.219513 178.758606 13.01786 119.898499 ... 22.05921 30.57095 14.11111
## [[8]] numeric(0)
## [[9]] 753.738 385.376 315.441 413.206 ... 3038.73 2016.43 1146.04 704.175
## [[10]] 753.738 385.376 315.441 413.206 ... 3038.73 2016.43 1146.04 704.175The MsBackendMgf backend allows also to export data in
mgf format. Below we export the data to a temporary file. We hence call
the export function on our Spectra object
specifying backend = MsBackendMgf() to use this backend for
the export of the data. Note that we use again our custom mapping of
variables such that the spectra variable "spectrumName"
will be exported as the spectrums’ title.
fl <- tempfile()
export(sps, backend = MsBackendMgf(), file = fl, mapping = map)We next read the first lines from the exported file to verify that the title was exported properly.
readLines(fl)[1:12]## [1] "BEGIN IONS"
## [2] "msLevel=2"
## [3] "RTINSECONDS=1028"
## [4] "SCANS=2162"
## [5] "centroided=TRUE"
## [6] "PEPMASS=816.33826"
## [7] "CHARGE=2+"
## [8] "TITLE=File193 Spectrum1719 scans: 2162"
## [9] "102.0548 753.738"
## [10] "103.00494 385.376"
## [11] "103.03531 315.441"
## [12] "115.05001 413.206"Note that the MsBackendMgf exports all spectra variables
as fields in the mgf file. To illustrate this we add below a new spectra
variable to the object and export the data.
sps$new_variable <- "A"
export(sps, backend = MsBackendMgf(), file = fl)
readLines(fl)[1:12]## [1] "BEGIN IONS"
## [2] "TITLE=msLevel 2; retentionTime ; scanNum "
## [3] "msLevel=2"
## [4] "RTINSECONDS=1028"
## [5] "SCANS=2162"
## [6] "centroided=TRUE"
## [7] "PEPMASS=816.33826"
## [8] "CHARGE=2+"
## [9] "spectrumName=File193 Spectrum1719 scans: 2162"
## [10] "new_variable=A"
## [11] "102.0548 753.738"
## [12] "103.00494 385.376"We can see that also our newly defined variable was exported. Also,
because we did not provide our custom variable mapping this time, the
variable "spectrumName" was not used as
the spectrum’s title.
Sometimes it might be required to not export all spectra variables
since some exported fields might not be recognized/supported by external
tools. Using the selectSpectraVariables function we can
reduce our Spectra object to export to contain only
relevant spectra variables. Below we restrict the data to only m/z,
intensity, retention time, acquisition number, precursor m/z and
precursor charge and export these to an mgf file. Also, some external
tools don’t support the "TITLE" field in the MGF file. To
disable export of the spectrum ID/title exportTitle = FALSE
can be used.
sps_ex <- selectSpectraVariables(sps, c("mz", "intensity", "rtime",
"acquisitionNum", "precursorMz",
"precursorCharge"))
export(sps_ex, backend = MsBackendMgf(), file = fl, exportTitle = FALSE)
readLines(fl)[1:12]## [1] "BEGIN IONS" "RTINSECONDS=1028" "SCANS=2162"
## [4] "PEPMASS=816.33826" "CHARGE=2+" "102.0548 753.738"
## [7] "103.00494 385.376" "103.03531 315.441" "115.05001 413.206"
## [10] "115.08686 588.273" "120.08063 800.016" "124.10555 526.761"Parallel processing
The MsBackendMgf package supports parallel processing for
data import. Parallel processing can be enabled by providing the
parallel processing setup to the backendInitialize function
(or the readMgf function) with the BPPARAM
parameter. By default (with BPPARAM = SerialParam())
parallel processing is disabled. If enabled, and data import is
performed on a single file, the extraction of spectra information on the
imported MGF file is performed in parallel. If data is to be imported
from multiple files, the import is performed in parallel on a per-file
basis (i.e. in parallel from the different files). Generally, the
performance gain through parallel processing is only moderate and it is
only suggested if a large number of files need to be processed, or if
the MGF file is very large (e.g. containing over 100,000 spectra).
Session information
## R Under development (unstable) (2024-11-13 r87330)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] MsBackendMgf_1.15.1 Spectra_1.15.13 BiocParallel_1.41.0
## [4] S4Vectors_0.45.2 BiocGenerics_0.53.3 generics_0.1.3
## [7] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.9 compiler_4.5.0 BiocManager_1.30.25
## [4] parallel_4.5.0 cluster_2.1.6 jquerylib_0.1.4
## [7] systemfonts_1.1.0 IRanges_2.41.1 textshaping_0.4.0
## [10] yaml_2.3.10 fastmap_1.2.0 R6_2.5.1
## [13] ProtGenerics_1.37.1 knitr_1.49 htmlwidgets_1.6.4
## [16] MASS_7.3-61 bookdown_0.41 desc_1.4.3
## [19] bslib_0.8.0 rlang_1.1.4 cachem_1.1.0
## [22] xfun_0.49 fs_1.6.5 MsCoreUtils_1.17.3
## [25] sass_0.4.9 cli_3.6.3 pkgdown_2.1.1.9000
## [28] digest_0.6.37 MetaboCoreUtils_1.15.0 lifecycle_1.0.4
## [31] clue_0.3-66 evaluate_1.0.1 codetools_0.2-20
## [34] ragg_1.3.3 rmarkdown_2.29 tools_4.5.0
## [37] htmltools_0.5.8.1