In fgcz/MsBackendRawFileReader: Mass Spectrometry Backend for Reading Thermo Fisher Scientific raw Files

BiocStyle::markdown()
knitr::opts_chunk$set(fig.wide = TRUE, fig.retina = 3)

The figure below depicts the idea of the r BiocStyle::Biocpkg("Spectra") framework. For a detailed description, read [@Spectra].

  knitr::include_graphics("arch.jpg")

Requirements

suppressMessages(
  stopifnot(require(Spectra),
            require(MsBackendRawFileReader),
            require(tartare),
            require(BiocParallel))
)

assemblies aka Common Intermediate Language bytecode The download and install can be done on all platforms using the command: rawrr::installRawFileReaderDLLs()

if (isFALSE(rawrr::.checkDllInMonoPath())){
  rawrr::installRawFileReaderDLLs()
}

if (isFALSE(file.exists(rawrr:::.rawrrAssembly()))){
 rawrr::installRawrrExe()
}

Load data

# fetch via ExperimentHub
library(ExperimentHub)
eh <- ExperimentHub::ExperimentHub()

query(eh, c('tartare'))

The RawFileReader libraries require a file extension ending with .raw.

EH3220 <- normalizePath(eh[["EH3220"]])
(rawfileEH3220 <- paste0(EH3220, ".raw"))
if (!file.exists(rawfileEH3220)){
  file.link(EH3220, rawfileEH3220)
}

EH3222 <- normalizePath(eh[["EH3222"]])
(rawfileEH3222 <- paste0(EH3222, ".raw"))
if (!file.exists(rawfileEH3222)){
  file.link(EH3222, rawfileEH3222)
}

EH4547  <- normalizePath(eh[["EH4547"]])
(rawfileEH4547  <- paste0(EH4547 , ".raw"))
if (!file.exists(rawfileEH4547 )){
  file.link(EH4547 , rawfileEH4547 )
}

Usage

Call the constructor using Spectra::backendInitialize, see also [@Spectra].

beRaw <- Spectra::backendInitialize(
  MsBackendRawFileReader::MsBackendRawFileReader(),
  files = c(rawfileEH3220, rawfileEH3222, rawfileEH4547))

Call the print method

beRaw

beRaw |> Spectra::spectraVariables()

Application example

Peptide Identification

Here we reproduce the Figure 2 of @rawrr r BiocStyle::Biocpkg("rawrr"). The r BiocStyle::Githubpkg("fgcz/MsBackendRawFileReader") ships with a filterScan method using functionality provided by the C# libraries by Thermo Fisher Scientific @rawfilereader.

(S <- (beRaw |>  
   filterScan("FTMS + c NSI Full ms2 487.2567@hcd27.00 [100.0000-1015.0000]") )[437]) |> 
  plotSpectra()

# supposed to be scanIndex 9594
S

# add yIonSeries to the plot
(yIonSeries <- protViz::fragmentIon("LGGNEQVTR")[[1]]$y[1:8])
names(yIonSeries) <- paste0("y", seq(1, length(yIonSeries)))
abline(v = yIonSeries, col='#DDDDDD88', lwd=5)
axis(3, yIonSeries, names(yIonSeries))

Class extension

For demonstration reasons, we extent the MsBackend class by a filter method. The filterIons function returns spectra if and only if all fragment ions, given as argument, match. We use r BiocStyle::CRANpkg("protViz")``::findNN binary search method for determining the nearest mZ peak for each ion. If the mass error between an ion and an mz value is less than the given mass tolerance, an ion is considered a hit.

setGeneric("filterIons", function(object, ...) standardGeneric("filterIons"))

setMethod("filterIons", "MsBackend",
  function(object, mZ=numeric(), tol=numeric(), ...) {

    keep <- lapply(peaksData(object, BPPARAM = bpparam()),
                   FUN=function(x){
       NN <- protViz::findNN(mZ, x[, 1])
       hit <- (error <- mZ - x[NN, 1]) < tol & x[NN, 2] >= quantile(x[, 2], .9)
       if (sum(hit) == length(mZ))
         TRUE
       else
         FALSE
                   })
    object[unlist(keep)]
  })

The lines below implement a simple targeted peptide search engine. The R code snippet takes as input a MsBackendRawFileReader object containing r length(beRaw) spectra and y-fragment-ion mZ values determined for LGGNEQVTR++.

start_time <- Sys.time()
X <- beRaw |> 
  MsBackendRawFileReader::filterScan("FTMS + c NSI Full ms2 487.2567@hcd27.00 [100.0000-1015.0000]") |>
  filterIons(yIonSeries, tol = 0.005) |> 
  Spectra::Spectra() |>
  Spectra::peaksData() 
end_time <- Sys.time()

The defined filterIons method runs on r length(beRaw |> MsBackendRawFileReader::filterScan("FTMS + c NSI Full ms2 487.2567@hcd27.00 [100.0000-1015.0000]")) input spectra and returns r length(X) spectra.

The runtime is shown below.

end_time - start_time

Next, we define and apply a method for graphing LGGNEQVTR peptide spectrum matches. Also, the function returns some statistics of the match.

## A helper plot function to visualize a peptide spectrum match for 
## the LGGNEQVTR peptide.
.plot.LGGNEQVTR <- function(x){

  yIonSeries <- protViz::fragmentIon("LGGNEQVTR")[[1]]$y[1:8]
  names(yIonSeries) <- paste0("y", seq(1, length(yIonSeries)))

  plot(x, type = 'h', xlim = range(yIonSeries))
  abline(v = yIonSeries, col = '#DDDDDD88', lwd=5)
  axis(3, yIonSeries, names(yIonSeries))

  # find nearest mZ value
  idx <- protViz::findNN(yIonSeries, x[,1])

  data.frame(
    ion = names(yIonSeries),
    mZ.yIon = yIonSeries,
    mZ = x[idx, 1],
    intensity = x[idx, 2]
  )
}

op <- par(mfrow=c(4, 1), mar=c(4, 4, 4, 1))
XC <- X |>
  lapply(FUN = .plot.LGGNEQVTR) |>
  Reduce(f = rbind) 

stats::aggregate(mZ ~ ion, data = XC, FUN = base::mean)
stats::aggregate(intensity ~ ion, data = XC, FUN = base::max)

We demonstrate the Spectra::combinePeaks method and aggregate the four spectra into a single peak matrix. The statistics returned by .plot.LGGNEQVTR() should be identical to the aggregation code snippet output above.

X |>
  Spectra::combinePeaks(ppm=10, intensityFun=base::max) |>
  .plot.LGGNEQVTR()

Export Mascot Generic Format File

Below we demonstrate the interaction with the r Biocpkg('MsBackendMgf') package while composing a Mascot Generic Format mgf file which is compatible with conducting an MS/MS Ions Search using Mascot Server (>=2.7) @Perkins1999.

if (require(MsBackendMgf)){
    ## Map Spectra variables to Mascot Server compatible vocabulary.
    map <- c(custom = "TITLE",
             msLevel = "CHARGE",
             scanIndex = "SCANS",
             precursorMz = "PEPMASS",
             rtime = "RTINSECONDS")

    ## Compose custom TITLE
    beRaw$custom <- paste0("File: ", beRaw$dataOrigin, " ; SpectrumID: ", S$scanIndex)

    (mgf <- tempfile(fileext = '.mgf'))

    (beRaw |>
            filterScan("FTMS + c NSI Full ms2 487.2567@hcd27.00 [100.0000-1015.0000]") )[437] |>
        Spectra::Spectra() |>
        Spectra::selectSpectraVariables(c("rtime", "precursorMz",
                                          "precursorCharge", "msLevel", "scanIndex", "custom")) |>
        MsBackendMgf::export(backend = MsBackendMgf::MsBackendMgf(),
                             file = mgf, map = map)
    readLines(mgf) |> head(12)
    readLines(mgf) |> tail()
}

To extract all tandem spectra, you can use the code snippets below

S <- Spectra::backendInitialize(
  MsBackendRawFileReader::MsBackendRawFileReader(),
  files = c(rawfileEH4547)) |>
  Spectra() 

S

S |>
  MsBackendMgf::export(backend = MsBackendMgf::MsBackendMgf(),
                       file = mgf,
                       map = map)

Next, we generate an mgf file for each scan type. This is helpful, e.g., for optimizing search settings tandem mass spectrometry sequence database search tool as comet @comet2012 or mascot server @Perkins1999.

## Define scanType patterns
scanTypePattern <- list(
  EThcD.lowres = "ITMS.+sa Full ms2.+@etd.+@hcd.+",
  ETciD.lowres = "ITMS.+sa Full ms2.+@etd.+@cid.+",
  CID.lowres = "ITMS[^@]+@cid[^@]+$",
  HCD.lowres = "ITMS[^@]+@hcd[^@]+$",
  EThcD.highres = "FTMS.+sa Full ms2.+@etd.+@hcd.+",
  HCD.highres = "FTMS[^@]+@hcd[^@]+$"
)

beRaw <- Spectra::backendInitialize(
  MsBackendRawFileReader::MsBackendRawFileReader(),
  files = c(rawrr::sampleFilePath()))

beRaw <- Spectra::backendInitialize(
  MsBackendRawFileReader::MsBackendRawFileReader(),
  files = rawrr::sampleFilePath())

beRaw$custom <- paste0("File: ", gsub("/srv/www/htdocs/Data2San/", "", beRaw$dataOrigin), " ; SpectrumID: ", beRaw$scanIndex)

.generate_mgf <- function(ext, pattern,  dir=tempdir(), ...){
  mgf <- file.path(dir, paste0(sub("\\.raw", "", unique(basename(beRaw$dataOrigin))),
                               ".", ext, ".mgf"))

  idx <- beRaw$scanType |> grepl(patter=pattern)

  if (sum(idx) == 0) return (NULL)

  message(paste0("Extracting ", sum(idx), " ",
                 pattern, " scans\n\t to file ", mgf, " ..."))

  beRaw[which(idx)] |>
    Spectra::Spectra() |>
    Spectra::selectSpectraVariables(c("rtime", "precursorMz",
    "precursorCharge", "msLevel", "scanIndex", "custom")) |>
    MsBackendMgf::export(backend = MsBackendMgf::MsBackendMgf(),
                         file = mgf,
                         map = map)

  mgf
}

#mapply(ext = names(scanTypePattern),
#      scanTypePattern,
#       FUN = .generate_mgf) |>
#  lapply(FUN = function(f){if (file.exists(f)) {readLines(f) |> head()}})

Procesing queue

Given the task, we want to filter an MS2 of peak list recorded on an Orbitrap device to be interested only in the top peak within 100 Da mass windows. The following code snippet will demonstrate a solution.

## Define a function that takes a matrix as input and derives
## the top n most intense peaks within a mass window.
## Of note, here, we require centroided data. (no profile mode!)
MsBackendRawFileReader:::.top_n

We add our custom code to the processing queue of the Spectra object. Of note, we use n = 1 in praxis n = 10 for a 100 Da mass window, which seems to be a practical choice.

S_2 <- Spectra::addProcessing(S, MsBackendRawFileReader:::.top_n, n = 1) 

The plot below displays a visual control of the custom filter function top_n. On the top is the original spectrum, and the filtered one is on the bottom. A point indicates peaks that match.

Spectra::plotSpectraMirror(S[9594], S_2[9594], ppm = 50)

The following snippet prints the values of the filtered peaklist and the mZ values of the y-ions.

S_2[9594] |> mz() |> unlist()
yIonSeries

Evaluation

Efficiency - I/O Benchmark

When reading spectra the MsBackendRawFileReader:::.RawFileReader_read_peaks method is calling the rawrr::readSpectrum method.

The figure below displays the time performance for reading a single spectrum in dependency from the chunk size (how many spectra are read in one function call) for reading different numbers of overall spectra.

ioBm <- file.path(system.file(package = 'MsBackendRawFileReader'),
               'extdata', 'specs.csv') |>
  read.csv2(header=TRUE)

# perform and include a local IO benchmark
ioBmLocal <- ioBenchmark(1000, c(32, 64, 128, 256), rawfile = rawfileEH4547)


lattice::xyplot((1 / as.numeric(time)) * workers ~ size | factor(n) ,
                group = host,
                data = rbind(ioBm, ioBmLocal),
                horizontal = FALSE,
        scales=list(y = list(log = 10)),
                auto.key = TRUE,
                layout = c(3, 1),
                ylab = 'spectra read in one second',
                xlab = 'number of spectra / file')

Effectiveness

We compare the output of the Thermo Fischer Scientific raw files versus their corresponding mzXML files using Spectra::MsBackendMzR relying on the r BiocStyle::Biocpkg("mzR") package.

mzXMLEH3219 <- normalizePath(eh[["EH3219"]])
mzXMLEH3221 <- normalizePath(eh[["EH3221"]])

if (require(mzR)){
  beMzXML <- Spectra::backendInitialize(
    Spectra::MsBackendMzR(),
    files = c(mzXMLEH3219))

  beRaw <- Spectra::backendInitialize(
    MsBackendRawFileReader::MsBackendRawFileReader(),
    files = c(rawfileEH3220))

  intensity.xml <- sapply(intensity(beMzXML[1:100]), sum)
  intensity.raw <- sapply(intensity(beRaw[1:100]), sum)

  plot(intensity.xml ~ intensity.raw, log = 'xy', asp = 1,
    pch = 16, col = rgb(0.5, 0.5, 0.5, alpha=0.5), cex=2)
  abline(lm(intensity.xml ~ intensity.raw), 
    col='red')
}

Are all scans of the raw file in the mzXML file?

if (require(mzR)){
  table(scanIndex(beRaw) %in% scanIndex(beMzXML))
}

Session information {-}

sessionInfo()

References {-}

fgcz/MsBackendRawFileReader documentation built on Oct. 3, 2024, 1:42 a.m.