Interface for HRMS and IMS-HRMS raw data

Source: R/msdata.R

Description, configuration and utilities for the raw (IMS-)HRMS data interface of patRoon.

availableBackends(
  anaInfo = NULL,
  needTypes = NULL,
  checkOption = TRUE,
  verbose = TRUE
)

Arguments

anaInfo

Optional. If not NULL then anaInfo should be a analysis information table, and only those backends that can read each of the analyses are returned.

needTypes

Only applicable if anaInfo is set: should be "centroid", "profile" and/or "ims" to filter file types.

checkOption

Set to TRUE to only consider backends that are included in the patRoon.MS.backends option.

verbose

Set to TRUE to print the status of each backend.

Value

availableBackends returns (invisibly) a character vector with the names of the available backends.

Details

Version 3.0 of patRoon introduced an extensible and highly optimized interface to read raw data from HRMS and IMS-HRMS instruments. This interface supports chooseable 'backends' which perform the reading of file data from various formats. Subsequent steps such as the formation of extracted ion chromatograms, mobilograms and collection and averaging of mass spectra are then performed in patRoon. The interface is largely coded in C++ (using Rcpp), uses OpenMP parallelization and applies several other optimization strategies to make it suitable to rapidly process large amounts of raw data, e.g. as encountered in IMS-HRMS workflow.

The following backends for reading (IMS-)HRMS data are currently available:

  • "opentims": uses the OpenTIMS library to read Bruker TIMS data. This backends supports very fast reading of raw instrument .d data files directly, and therefore does not require any file conversions. This backend only supports 64 bit Windows and Linux systems. See the Backend installation section below installation instructions.

  • "mzr": uses the mzR package to read .mzML and .mzXML files. This package was more or less the default in patRoon prior to 3.0, and due to its popularity and age is a stable and well tested option. The mzr backend currently reads the complete analysis file at once, which makes it more RAM intensive compared to other backends. The read data is cached to speed up any subsequent operations that require the file data. This backend currently does not support IMS data. Since mzR is a dependency of patRoon, no additional installation is necessary.

  • "mstoolkit": Uses the MSToolKit C++ library to read .mzML and .mzXML files, including IMS-HRMS data. The MSToolKit library has been developed for many years, and was recently updated with IMS-HRMS support. See the Backend installation section below installation instructions.

  • "streamcraft": Uses the StreamCraft C++ library to read .mzML and .mzXML files, including IMS-HRMS data. The StreamCraft library is still young and somewhat experimental. The library is integrated within patRoon and therefore does not require any further installation.

The availableBackends function is used to query the available backends on the system.

Configuration

The following package options influence the behavior of raw data interface:

  • patRoon.MS.backends: A character vector with the names of the backends that may be choosen. The default is all backends. The first backend will be chosen that is available, is able to read at least one of the available analysis file types and formats (as configured by the analysis information) and supports IMS if needed.

  • patRoon.MS.preferIMS: A logical value that indicates whether the IMS data should be preferred, even if the processing step does not require IMS data and non-IMS data is also available. Setting this to TRUE probably result in some additional computational overhead, but may avoid any inconsistencies between the IMS data and non-IMS data that may have been introduced during the conversion step of the latter. This option is only relevant for the mstoolkit and streamcraft backends (and if one of these backends is actually used).

  • patRoon.threads: An integer value that indicates the number of threads to use for parallelization (multithreading). The default is determined from the number of physical cores of the system (obtained with the parallel::detectCores function).

  • patRoon.path.TDFSDK: The file path to the Bruker TDF-SDK library file. See the Backend installation section below.

Backend installation

The opentims backend requires the win64/timsdata.dll (Windows) or linux64/libtimsdata.so (Linux) file from the TDF-SDK from Bruker (requires login). The patRoonExt package makes these files automatically available for patRoon. Otherwise the patRoon.path.TDFSDK option should be manually set to the file path of the timsdata.dll or linux64/libtimsdata.so file.

When patRoon is installed from source, e.g. on Linux/macOS systems or when using remotes::install_github for installation, then the https://github.com/rickhelmus/Rmstoolkitlib R package must be installed in advance.

The availableBackends function can be used to verify if the dependencies for these backends are met.

References

Eddelbuettel D (2013). Seamless R and C++ Integration with Rcpp. Springer, New York. doi:10.1007/978-1-4614-6868-4 , ISBN 978-1-4614-6867-7.

Eddelbuettel D, Balamuta J (2018). “Extending R with C++: A Brief Introduction to Rcpp.” The American Statistician, 72(1), 28-36. doi:10.1080/00031305.2017.1375990 .

Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software, 40(8), 1–18. doi:10.18637/jss.v040.i08 .

Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I, Bates D, Chambers J (2026). Rcpp: Seamless R and C++ Integration. R package version 1.1.1, https://www.rcpp.org.

Dagum L, Menon R (1998). “OpenMP: an industry standard API for shared-memory programming.” IEEE Computational Science and Engineering, 5(1), 46-55. doi:10.1109/99.660313 .

Łącki MK, Startek MP, Brehmer S, Distler U, Tenzer S (2021). “OpenTIMS, TimsPy, and TimsR: Open and Easy Access to timsTOF Raw Data.” Journal of Proteome Research, 20(4), 2122–2129. ISSN 1535-3907, doi:10.1021/acs.jproteome.0c00962 , http://dx.doi.org/10.1021/acs.jproteome.0c00962.

Chambers, C. M, Maclean, Brendan, Burke, Robert, Amodei, Dario, Ruderman, L. D, Neumann, Steffen, Gatto, Laurent, Fischer, Bernd, Pratt, Brian, Egertson, Jarrett, Hoff, Katherine, Kessner, Darren, Tasman, Natalie, Shulman, Nicholas, Frewen, Barbara, Baker, A. T, Brusniak, Mi-Youn, Paulse, Christopher, Creasy, David, Flashner, Lisa, Kani, Kian, Moulding, Chris, Seymour, L. S, Nuwaysir, M. L, Lefebvre, Brent, Kuhlmann, Frank, Roark, Joe, Rainer, Paape, Detlev, Suckau, Hemenway, Tina, Huhmer, Andreas, Langridge, James, Connolly, Brian, Chadick, Trey, Holly, Krisztina, Eckels, Josh, Deutsch, W. E, Moritz, L. R, Katz, E. J, Agus, B. D, MacCoss, Michael, Tabb, L. D, Mallick, Parag (2012). “A cross-platform toolkit for mass spectrometry and proteomics.” Nat Biotech, 30(10), 918–920. doi:10.1038/nbt.2377 , http://dx.doi.org/10.1038/nbt.2377.

Keller A, Eng J, Zhang N, Li X, Aebersold R (2005). “A uniform proteomics MS/MS analysis platform utilizing open XML file formats.” Mol Syst Biol.

Kessner D, Chambers M, Burke R, Agus D, Mallick P (2008). “ProteoWizard: open source software for rapid proteomics tools development.” Bioinformatics, 24(21), 2534–2536. doi:10.1093/bioinformatics/btn323 .

Martens L, Chambers M, Sturm M, Kessner D, Levander F, Shofstahl J, Tang WH, Rompp A, Neumann S, Pizarro AD, Montecchi-Palazzi L, Tasman N, Coleman M, Reisinger F, Souda P, Hermjakob H, Binz P, Deutsch EW (2010). “mzML - a Community Standard for Mass Spectrometry Data.” Mol Cell Proteomics. doi:10.1074/mcp.R110.000133 .

Pedrioli PGA, Eng JK, Hubley R, Vogelzang M, Deutsch EW, Raught B, Pratt B, Nilsson E, Angeletti RH, Apweiler R, Cheung K, Costello CE, Hermjakob H, Huang S, Julian RK, Kapp E, McComb ME, Oliver SG, Omenn G, Paton NW, Simpson R, Smith R, Taylor CF, Zhu W, Aebersold R (2004). “A common open representation of mass spectrometry data and its application to proteomics research.” Nat Biotechnol, 22(11), 1459–1466. doi:10.1038/nbt1031 .