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DIA/SWATH Course

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Software

  • Skyline is a Windows-based software for building SRM methods and analysing the resulting MS data. It employs cutting-edge technologies for creating and iteratively refining targeted methods for large-scale proteomics studies. Mac users need a virtual machine to run Skyline (e.g. VMware Fusion (30 days free trial version)).

  • OpenSWATH is a proteomics software that allows analysis of LC-MS/MS DIA (data independent acquisition) data using the approach described by Gillet et al. [2]. 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 acquire 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.

  • TRIC is an alignment software for targeted proteomics (SRM or SWATH-MS) data. TRIC uses a graph-based alignment strategy based on non-linear retention time correction to integrate information from all available runs. The input consists of a set of csv files derived from a targeted proteomics experiment generated by OpenSWATH (using either mProphet or pyProphet) or generated by Peakview.

  • DIA-Umpire is an open source Java program for computational analysis of data independent acquisition (DIA) mass spectrometry-based proteomics data. It enables untargeted peptide and protein identification and quantitation using DIA data, and also incorporates targeted extraction to reduce the number of cases of missing quantitation.

  • MSstats is an R-based statistical tool which detects differentially abundant proteins, summarises and visualises protein-level inferences, and can be used to design future SRM experiments. The statistical framework behind MSstats is based on linear mixed-effect models, where model-based inferences attain high sensitivity and specificity in protein significance results.

Selected literature



Data-Inpependent Acquisition (DIA/SWATH)
  • Gillet et al., Molecular & Cellular Proteomics 2012: Targeted data extraction of the MS/MS spectra generated by data independent acquisition: a new concept for consistent and accurate proteome analysis (abstract)

  • Chapman et al., Mass Spectrom Reviews: Multiplexed and data-independent tandem mass spectrometry for global proteome profiling (abstract)

  • Rosenberger et al., Scientific Data: A repository of assays to quantify 10,000 human proteins by SWATH-MS (abstract)

  • Schubert et al., Nature Protocols: Building high-quality assay libraries for targeted analysis of SWATH MS data (abstract)

  • Egertson et al. Nature Methods 2013: Multiplexed MS/MS for improved data-independent acquisition (abstract)

  • Venable et al., Nature Methods 2004: Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra (abstract)

  • Ting et al., Molecular & Cellular Proteomics 2015: Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data (abstract)

  • Tsou et al., Proteomics 2016: Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers (abstract)

  • Bruderer et al., Molecular & Cellular Proteomics 2015: Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen treated 3D liver microtissues (abstract)


Software and tools
  • MacLean et al., Bioinformatics 2010: Skyline: an open source document editor for creating and analyzing targeted proteomics experiments (abstract)

  • Escher et al., Proteomics 2012: Using iRT, a normalized retention time for more targeted measurement of peptides (abstract)

  • Reiter et al., Nature Methods 2011: mProphet: automated data processing and statistical validation for large-scale SRM experiments (abstract)

  • Chang et al., Molecular and Cellular Proteomics 2012: Protein significance analysis in selected reaction monitoring (SRM) (abstract)

  • Ahrens et al., Nature Reviews Molecular Cell Biology 2010: Generating and navigating proteome maps using mass spectrometry (abstract)

  • Röst et al., Nature Biotechnology: OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data (abstract)

  • Röst et al., Nature Methods 2016: TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics (abstract)

  • Navarro et al., Nature Biotechnology 2016: A multicenter study benchmarks software tools for label-free proteome quantification (abstract)

  • Blattmann et al., PLoS One 2016: SWATH2stats: An R/Bioconductor Package to Process and Convert Quantitative SWATH-MS Proteomics Data for Downstream Analysis Tools(abstract)

  • Tsou et al., Nature Methods 2015: DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics (abstract)