OpenSWATH

Introduction

OpenSWATH [3] allows the analysis of LC-MS/MS DIA (data independent acquisition) data using the approach described by Gillet et al. [4]. The DIA approach described there uses 32 cycles to iterate through precursor ion windows from 400-426 Da to 1175-1201 Da and at each step acquires a complete, multiplexed fragment ion spectrum of all precursors present in that window. After 32 fragmentations (or 3.2 seconds), the cycle is restarted and the first window (400-426 Da) is fragmented again, thus delivering complete “snapshots” of all fragments of a specific window every 3.2 seconds. The analysis approach described by Gillet et al. extracts ion traces of specific fragment ions from all MS2 spectra that have the same precursor isolation window, thus generating data that is very similar to SRM traces.

Installation of OpenSWATH

OpenSWATH has been fully integrated since OpenMS 1.10 [2], [1], [5], [6], [7].

Installation of mProphet

mProphet[8] is available as standalone script in External_Tools▸mProphet. R and the package MASS are further required to execute mProphet. Please obtain a version for either Windows, Mac or Linux directly from CRAN. PyProphet, a much faster reimplementation of the mProphet algorithm is available from PyPI. The usage of pyprophet instead of mProphet is suggested for large-scale applications.

mProphet will be used in this tutorial.

Generating the Assay Library

Generating TraML from transition lists

OpenSWATH requires an assay library to be supplied in the TraML format[9]. To enable manual editing of transition lists, the TOPP tool TargetedFileConverter is available, which uses tab separated files as input. Example datasets are provided in ExampleData▸OpenSWATH▸assay. Please note that the transition lists need to be named .tsv.

The header of the transition list contains the following variables (with example values in brackets):

Required Columns: PrecursorMz

The mass-to-charge (m/z) of the precursor ion. (924.539)

ProductMz

The mass-to-charge (m/z) of the product or fragment ion. (728.99)

LibraryIntensity

The relative intensity of the transition. (0.74)

NormalizedRetentionTime

The normalized retention time (or iRT)[10] of the peptide. (26.5)

Targeted Proteomics Columns ProteinId

A unique identifier for the protein. (AQUA4SWATH_HMLangeA)

PeptideSequence

The unmodified peptide sequence. (ADSTGTLVITDPTR)

ModifiedPeptideSequence

The peptide sequence with UniMod modifications. (ADSTGTLVITDPTR(UniMod:267))

PrecursorCharge

The precursor ion charge. (2)

ProductCharge

The product ion charge. (2)

Grouping Columns: TransitionGroupId

A unique identifier for the transition group. (AQUA4SWATH_HMLangeA_ADSTGTLVITDPTR(UniMod:267)/2)

TransitionId

A unique identifier for the transition. (AQUA4SWATH_HMLangeA_ADSTGTLVITDPTR(UniMod:267)/2_y8)

Decoy

A binary value whether the transition is target or decoy. (target: 0, decoy: 1)

PeptideGroupLabel

Which label group the peptide belongs to.

DetectingTransition

Use transition for peak group detection. (1)

IdentifyingTransition

Use transition for peptidoform inference using IPF. (0)

QuantifyingTransition

Use transition to quantify peak group. (1)

For further instructions about generic transition list and assay library generation please see the following link. To convert transitions lists to TraML, use the TargetedFileConverter: Please use the absolute path to your OpenMS installation.

Linux or Mac

On the Terminal:

 TargetedFileConverter -in OpenSWATH_SGS_AssayLibrary_woDecoy.tsv -out OpenSWATH_SGS_AssayLibrary_woDecoy.TraML

Windows

On the TOPP command:

 TargetedFileConverter.exe -in OpenSWATH_SGS_AssayLibrary_woDecoy.tsv -out OpenSWATH_SGS_AssayLibrary_woDecoy.TraML

Appending decoys to a TraML file

In addition to the target assays, OpenSWATH requires decoy assays in the library which are later used for classification and error rate estimation. For the decoy generation it is crucial that the decoys represent the targets in a realistic but unnatural manner without interfering with the targets. The methods for decoy generation implemented in OpenSWATH include ’shuffle’, ’pseudo-reverse’, ’reverse’ and ’shift’. To append decoys to a TraML, the TOPP tool OpenSwathDecoyGenerator can be used: Please use the absolute path to your OpenMS installation.

Linux or Mac

On the Terminal:

OpenSwathDecoyGenerator -in OpenSWATH_SGS_AssayLibrary_woDecoy.TraML -out OpenSWATH_SGS_AssayLibrary.TraML -method shuffle -switchKR false

Windows

On the TOPP command:

OpenSwathDecoyGenerator.exe -in OpenSWATH_SGS_AssayLibrary_woDecoy.TraML -out OpenSWATH_SGS_AssayLibrary.TraML -method shuffle -switchKR false

OpenSWATH KNIME

An example KNIME workflow for OpenSWATH is supplied in Workflows (Fig. 44). The example dataset can be used for this workflow (filenames in brackets):

  1. Open Workflows▸OpenSWATH.knwf in KNIME: File > Import KNIME Workflow…

  2. Select the normalized retention time (iRT) assay library in TraML format by double-clicking on node File Importer > iRT Assay Library. (ExampleData▸OpenSWATH▸assay▸OpenSWATHiRTAssayLibrary.TraML).

  3. Select the SWATH MS data in mzML format as input by double-clicking on node Input File > SWATH-MS files. (ExampleData▸OpenSWATH▸data▸splitnapedroL120420x010SW-*.nf.pp.mzML).

  4. Select the target peptide assay library in TraML format as input by double-clicking on node Input Files > Assay Library. (ExampleData▸OpenSWATH▸assay▸OpenSWATHSGSAssayLibrary.TraML).

  5. Set the output destination by double-clicking on node Output File.

  6. Run the workflow.

The resulting output can be found at your selected path, which will be used as input for mProphet. Execute the script on the Terminal (Linux or Mac) or cmd.exe (Windows) in ExampleData▸OpenSWATH▸result. Please use the absolute path to your R installation and the result file:

R --slave --args bin_dir=../../../External_Tools/mProphet/ mquest=OpenSWATH_quant.tsv workflow=LABEL_FREE num_xval=5 run_log=FALSE write_classifier=1 write_all_pg=1 < ../../../External_Tools/mProphet/mProphet.R

or for Windows:

"C:\Program Files\R\R-3.5.1\bin\x86\R.exe" --slave --args bin_dir=../../../External_Tools/mProphet/ mquest=OpenSWATH_quant.tsv workflow=LABEL_FREE num_xval=5 run_log=FALSE write_classifier=1 write_all_pg=1 < ../../../External_Tools/mProphet/mProphet.R

The main output will be called: OpenSWATH▸result▸mProphetxallxpeakgroups.xls with statistical information available in OpenSWATH▸result▸mProphet.pdf.

Please note that due to the semi-supervised machine learning approach of mProphet the results differ slightly when mProphet is executed several times.

OpenSWATH KNIME Workflow.

Figure 44: OpenSWATH KNIME Workflow.

Additionally, the chromatogram output (.mzML) can be visualized for inspection with TOPPView. For additional instructions on how to use pyProphet instead of mProphet please have a look at the PyProphet Legacy Workflow. If you want to use the SQLite-based workflow in your lab in the future, please have a look here. The SQLite-based workflow will not be part of the tutorial.

From the example dataset to real-life applications

The sample dataset used in this tutorial is part of the larger SWATH MS Gold Standard (SGS) dataset which is described in the publication of Roest et al.[3]. It contains one of 90 SWATH-MS runs with significant data reduction (peak picking of the raw, profile data) to make file transfer and working with it easier. Usually SWATH-MS datasets are huge with several gigabyte per run. Especially when complex samples in combination with large assay libraries are analyzed, the TOPP tool based workflow requires a lot of computational resources. Additional information and instruction can be found at the following link.

References