DIA Analysis using OpenSwathWorkflow


  • How to analyze data independent acquisition (DIA) mass spectrometry (MS) data using OpenSwathWorkflow?

  • How to detect different Spike-in amounts of Ecoli in a HEK-Ecoli Benchmark DIA dataset?

  • Analysis of HEK-Ecoli Spike-in DIA data in Galaxy

  • Understanding DIA data principles and characteristics

  • Using OpenSwathworkflow to analyze HEK-Ecoli Spike-in DIA data

Time estimation: 2 hours
Level: Intermediate
Supporting Materials
Last modification: Jul 19, 2021
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License The GTN Framework is MIT


This training covers data independent acquisition (DIA) mass spectrometry (MS) applying spectral libraries for peptide identification and quantification. You can learn how to prepare and optimize a spectral library for the analysis of DIA data in theDIA library generation tutorial.

The proteome refers to the entirety of proteins in a biological system (e.g cell, tissue, organism). Proteomics is the large-scale experimental analysis of proteins and proteomes, most often performed by mass spectrometry that enables great sensitivity and throughput. Especially for complex protein mixtures, bottom-up mass spectrometry is the standard approach. In bottom-up proteomics, proteins are digested with a specific protease into peptides and the measured peptides are in silico reassembled into the corresponding proteins. Inside the mass spectrometer, not only the peptides are measured (MS1 level), but the peptides are also fragmented into smaller peptides which are measured again (MS2 level). This is referred to as tandem-mass spectrometry (MS/MS). Identification of peptides is performed by peptide spectrum matching of the theoretical spectra generated from the input protein database (fasta file) with the measured MS2 spectra. Peptide quantification is most often performed by measuring the area under the curve of the MS1 level peptide peaks, but special techniques such as TMT allow to quantify peptides on MS2 level. Nowadays, bottom-up tandem-mass spectrometry approaches allow for the identification and quantification of several thousand proteins.

In clinical proteomic studies often two or more conditions should be compared against each other, thus focusing on the proteins which were found in all conditions. Using the data dependent acquisition (DDA) approach could lead to limited numbers of cumulative proteins, due to method instrinsic dependencies (e.g. if the same peptides is selected for fragmentation in all measurements). Over the last decade another acquisition method has been developed addressing the urgent need for increased cumulatively identified and quantified peptides and proteins across multiple measurements (Ludwig et al. 2018). The so called data independent acquisition (DIA) circumvents the time and abundance dependent selection for fragmentation by using predefined fragmentation windows (m/z windows) going through the whole m/z range of the previous MS1 scan.

Figure 1: Measurement principles of DIA and m/z window schema of staggered DIA windows.

Therefore, all peptides which are present in the same m/z window at the same time are fragmented simultaneously and a MS2 spectra containing fragments from multiple peptides is acquired. Using the same m/z windows for all measurements, results in more reproducible fragmentation and potential identification across multiple measurements. However, the resulting MS2 spectra contain fragments from multiple peptides and are often more complex and do not allow to directly link a specific (m/z) mass from the MS1 to a single MS2 fragment spectra.

Figure 2: The MS2 scans in the DIA approach contain fragment ions from multiple precursers and are therefore more complex than the precursor-specific MS2 scans in DDA.

To allow for the identification of peptides in those ambiguous MS2 spectra, a spectral library can be used. The spectral library contains experimentally measured MS2 spectra, which are specific for one precursor (from previous DDA measurements). In more recent approaches the MS2 spectra can be predicted based on theoretical peptide sequences (e.g. from a protein database).

Figure 3: Spectral libraries are necesseary for the identification of peptides in DIA MS2 scans. In this example the spectral library is generated based on DDA data from the same samples.

One of the open-source software options for DIA data analysis is OpenSwath (Röst et al. 2014), which requires a spectral library, retention time alignment peptides as well as the DIA MS data.

The dataset in this tutorial consists of two different Spike-in mixtures of human and Ecoli peptides, with an increase of the Ecoli peptides in one of the Spike-ins. For each Spike-in there a four replicate measurements to allow for statistical analysis later on. Here we will go through the data analysis of DIA mass spectrometry data and will get first impressions on which of the Spike-ins might contain higher amounts of Ecoli peptides.


In this tutorial, we will cover:

  1. Get data
  2. raw File conversion with msconvert
  3. DIA analysis using OpenSwathWorkflow
    1. Combining osw results using PyProphet merge
  4. FDR estimation using PyProphet score
    1. Apply scores with PyProphet peptide
    2. Apply scores with PyProphet protein
    3. Exporting the results with PyProphet export

Get data

hands_on Hands-on: Data upload

  1. Create a new history for this tutorial and give it a meaningful name

    Tip: Creating a new history

    Click the new-history icon at the top of the history panel.

    If the new-history is missing:

    1. Click on the galaxy-gear icon (History options) on the top of the history panel
    2. Select the option Create New from the menu
  2. Import the fasta and raw files as well as the sample annotation and the iRT Transition file from Zenodo
    • Copy the link location
    • Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

    • Select Paste/Fetch Data
    • Paste the link into the text field

    • Press Start

    • Close the window

    • By default, Galaxy uses the URL as the name, so rename the files with a more useful name.
  3. Once the files are green, rename the sample annotation file in ‘Sample_annotation’, the spectral library in ‘HEK_Ecoli_lib’, the iRT transition file in ‘iRTassays’ and the raw files in ‘Sample1.raw’, ‘Sample2.raw’, ‘Sample3.raw’, ‘Sample4.raw’, ‘Sample5.raw’, ‘Sample6.raw’, ‘Sample7.raw’ and ‘Sample8.raw’

    Tip: Renaming a dataset

    • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
    • In the central panel, change the Name field
    • Click the Save button
  4. Generate a collection for all .raw files (and name it DIA_data)

    Tip: Creating a dataset collection

    • Click on Operations on multiple datasets (check box icon) at the top of the history panel Operations on multiple datasets button
    • Check all the datasets in your history you would like to include
    • Click For all selected.. and choose Build dataset list

      build list collection menu item

    • Enter a name for your collection
    • Click Create List to build your collection
    • Click on the checkmark icon at the top of your history again

raw File conversion with msconvert

hands_on Hands-on: Converting vendor specific raw to open mzML format

  1. msconvert Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/msconvert/msconvert/3.0.19052.1 with the following parameters:
    • param-collection “Input unrefined MS data”: DIA_data
    • “Do you agree to the vendor licenses?”: Yes
    • “Output Type”: mzML
    • In “Data Processing Filters”:
      • “Apply peak picking?”: Yes
      • “Demultiplex overlapping or MSX spectra”: Yes
        • “Optimization”: Overlap only
    • In “General Options”:
      • “SIM as Spectra”: Yes
      • “Ignore unknown instrument error”: Yes
    • In “Output Encoding Settings”:
      • “Intensity Encoding Precision”: 64

    comment Comment: Demultiplexing

    Demultiplexing is needed when the MS2 m/z windows of consecutive scan cycles are overlapping e.g. for the staggered approach (50% shift between the cycles).

DIA analysis using OpenSwathWorkflow

hands_on Hands-on: DIA analysis using OpenSwathWorkflow

  1. OpenSwathWorkflow Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/openms_openswathworkflow/OpenSwathWorkflow/2.6+galaxy0 with the following parameters:
    • param-collection “Input files separated by blank”: DIA_data (output of msconvert tool)
    • param-file “transition file (‘TraML’,’tsv’,’pqp’)”: HEK_Ecoli_lib
    • param-file “transition file (‘TraML’)”: iRTassays
    • “Extraction window in Thomson or ppm (see mz_extraction_window_unit)”: 10.0
    • “Extraction window used in MS1 in Thomson or ppm (see mz_extraction_window_ms1_unit)”: 10.0
    • In “Parameters for the RTNormalization for iRT petides”:
      • “Which outlier detection method to use (valid: ‘iter_residual’, ‘iter_jackknife’, ‘ransac’, ‘none’)”: none
      • “Whether the algorithms should try to choose the best peptides based on their peak shape for normalization”: Yes
      • “Minimal number of bins required to be covered”: 7
    • In “Scoring parameters section”:
      • In “TransitionGroupPicker”:
        • “Minimal peak width (s), discard all peaks below this value (-1 means no action)”: 5.0
        • “Tries to compute a quality value for each peakgroup and detect outlier transitions”: Yes
      • In “Scores”:
        • “Use the MI (mutual information) score”: No
    • “Advanced Options”: Show Advanced Options
      • “Output an XIC with a RT-window by this much large”: 100.0
      • “Extraction window used for iRT and m/z correction in Thomson or ppm (see irt_mz_extraction_window_unit)”: 10.0
      • “Whether to run OpenSWATH directly on the input data, cache data to disk first or to perform a datareduction step first”: cacheWorkingInMemory
      • “Use the retention time normalization peptide MS2 masses to perform a mass correction (linear, weighted by intensity linear or quadratic) of all spectra”: regression_delta_ppm
    • “Optional outputs”: out_osw

    comment Comment: Mass tolerances and “Minimal number of bins required to be covered”

    Here we analyze data acquired on a QExactive Plus MS instrument which uses an Orbitrap and generates high resolution data. Other instrumentation (such as TOF devices) might require larger mass tolerances for improved peptide identification. Furthermore, here we require at least 7 of the iRT peptides to be found in each of the DIA measurements. This number can be set to lower values if for some reasons fewer iRT peptides were found in some of the measurements.

Combining osw results using PyProphet merge

hands_on Hands-on: Combining the individual osw results with pyprophet merge

  1. PyProphet merge Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet_merge/pyprophet_merge/ with the following parameters:
    • param-collection “Input file”: out_osw (output of OpenSwathWorkflow tool)
    • param-file “Template osw file”: HEK_Ecoli_lib

FDR estimation using PyProphet score

hands_on Hands-on: Semi-supervised learning and scoring of OpenSwathWorkflow results

  1. PyProphet score Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet_score/pyprophet_score/ with the following parameters:
    • param-file “Input file”: merged.osw (output of PyProphet merge tool)
    • “Either a ‘LDA’ or ‘XGBoost’ classifier is used for semi-supervised learning”: XGBoost

    comment Comment: FDR scoring using pyprophet score

    During this step q-values corresponding to the FDR of peak identification is estimated with pyprophet. Typically this is the most time consuming step due to the involved maschine learning processes. To decrease the input size one can use PyProphet subsample to randomly select subsets of the identifications from each run in the merged.osw (PyProphet merge output). In this case, the FDR estimation needs to be applied on the full merged.osw afterwards using the scored subsample.osw in the “Apply PyProphet score weights file (osw format) instead of semi-supervised learning.” section of PyProphet score. The generated report.pdf is helpful to identify potential errors as well as get first insights on the quality of the identifications.

tip Tip: Continue with results from Zenodo

In case the pyprophet score run is not yet finished, the results can be downloaded from Zenodo to be able to continue the tutorial

  1. Import the files from Zenodo

question Questions

  1. Does the distribution of target identifications differ from the decoy distribution?
  2. Is the observed distribution of decoy and target identifications expected?

solution Solution

  1. Yes, we can see a clearly different distribution of the target identification and the decoys. Both, target and decoy distribution were highest around 0. However, the target distribution shows a second peak at positiv d-score values.
  2. The decoy identifications show a Gaussian distribution around 0 which could be explained by the fact that the decoy sequences were randomly generated alterations from the target sequences in the spectral library (see DIA library generation tutorial). Most target identifications show also d-scores around 0, thus reflect potential false positive identifications. Only the distribution of target identifications shows a second increase in higher d-score values, representing more confident identifications.

Apply scores with PyProphet peptide

hands_on Hands-on: Conduct peptide inference in experiment-wide and global context

  1. PyProphet peptide Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet_peptide/pyprophet_peptide/ with the following parameters:
    • param-file “Input file”: score.osw (output of PyProphet score tool)
    • “Context to estimate protein-level FDR control”: experiment-wide
  2. PyProphet peptide Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet_peptide/pyprophet_peptide/ with the following parameters:
    • param-file “Input file”: peptide.osw (output of PyProphet peptide tool)
    • “Context to estimate protein-level FDR control”: global

Apply scores with PyProphet protein

hands_on Hands-on: Conduct protein inference in experiment-wide and global context

  1. PyProphet protein Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet_protein/pyprophet_protein/ with the following parameters:
    • param-file “Input file”: peptide.osw (output of the second PyProphet peptide tool)
    • “Context to estimate protein-level FDR control”: experiment-wide
  2. PyProphet protein Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet_protein/pyprophet_protein/ with the following parameters:
    • param-file “Input file”: protein.osw (output of PyProphet protein tool)
    • “Context to estimate protein-level FDR control”: global

question Questions

  1. How does the score distribution differ between the peptide and the protein inference (in global context)?
  2. What could be the reason for this difference?

solution Solution

  1. The d-score distribution and the ROC curve for the protein inference shows better target decoy discriminatory ability than for the peptide inference.
  2. The probabilty of multiple decoy peptides from the same protein having a high score is lower than for multiple target peptides from the same protein. Thus, the discriminatory scores on protein level are higher.

Exporting the results with PyProphet export

hands_on Hands-on: Exporting pyprophet scored OSW results

  1. PyProphet export Tool: toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet_export/pyprophet_export/ with the following parameters:
    • param-file “Input file”: protein.osw (output of PyProphet protein tool)
    • “Export format, either matrix, legacy_split, legacy_merged (mProphet/PyProphet) or score_plots format”: legacy_merged
    • “Use swath2stats to export file for statsics”: yes
      • param-file “Study design tabular file”: Sample_annotation

    comment Comment: swath2stats functionality

    Utilizing the swath2stats functionality generates a summary as well as a peptide and protein expression matrix. In addition to the non-processed pyprophet tabular output a refined tabular is generated using the specified criteria. The refined tabular msstats_input.tabular is dircetly compatable with MSstats for statistical analysis.

question Questions

  1. How many different peptides and proteins were identified and quatified?
  2. Could you already tell from the summary which Spike-in contained higher amounts of Ecoli peptides?

solution Solution

  1. In total, over 27.300 peptides and over 5.100 proteins were identified and quantified in the DIA measurements.
  2. No, the summary mainly provides an overview of the identifications in each individual DIA measurement as well as some descriptive statistics such as CVs and correlations.

hands_on Hands-On: Analysis of Ecoli Spike-in

  1. Select lines that match an expression Tool: Grep1 with the following parameters:
    • param-file “Select lines from”: protein_signal.tabular (output of PyProphet export tool)
    • “that”: Matching
    • “the pattern”: (ECOLI)|(Spike_in)

question Questions

  1. How many Ecoli proteins were identified and quantified in the six DIA measurements?
  2. Can you guess which Spike-in contains higher amounts of Ecoli peptides?

solution Solution

  1. Over 800 Ecoli proteins were identified and quantified in the six DIA measurements.
  2. It seems that the samples in Spike_in_2 contained higher amounts of Ecoli peptides than the samples in Spike_in_1.


Figure 4: All-in one workflow for DIA analysis in Galaxy. The DIA data analysis using OpenSwathWorkflow is highlighted in yellow.

Using OpenSwathWorkflow and a spectral library (from DDA data) we were able to analyze data independent acquisition (DIA) data. Furthermore, we were able to process the FDR scored results and take a guess on which Spike-in contained higher amounts of Ecoli. To further investigate the two different Spike-in amounts as well as getting a significant result we would need to perform statistical analysis e.g. using MSstats.

Figure 5: Galaxy worfklow containing all necessary steps for the analysis of DIA data using a spectral library and OpenSwathWorkflow

Key points

  • OpenSwathWorkflow enables analysis of data independent acquisition mass spectrometry data

  • DIA data analysis of HEK-Ecoli Spike-in dataset

Frequently Asked Questions

Have questions about this tutorial? Check out the FAQ page for the Proteomics topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum

Useful literature

Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.


  1. Röst, H. L., G. Rosenberger, P. Navarro, L. Gillet, S. M. Miladinović et al., 2014 OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature biotechnology 32: 219–23. 10.1038/nbt.2841 http://www.ncbi.nlm.nih.gov/pubmed/24727770
  2. Ludwig, C., L. Gillet, G. Rosenberger, S. Amon, B. C. Collins et al., 2018 Data‐independent acquisition‐based SWATH ‐ MS for quantitative proteomics: a tutorial . Molecular Systems Biology 14: 1–23. 10.15252/msb.20178126


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Citing this Tutorial

  1. Matthias Fahrner, Melanie Föll, 2021 DIA Analysis using OpenSwathWorkflow (Galaxy Training Materials). https://training.galaxyproject.org/archive/2021-08-01/topics/proteomics/tutorials/DIA_Analysis_OSW/tutorial.html Online; accessed TODAY
  2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

details BibTeX

author = "Matthias Fahrner and Melanie Föll",
title = "DIA Analysis using OpenSwathWorkflow (Galaxy Training Materials)",
year = "2021",
month = "07",
day = "19"
url = "\url{https://training.galaxyproject.org/archive/2021-08-01/topics/proteomics/tutorials/DIA_Analysis_OSW/tutorial.html}",
note = "[Online; accessed TODAY]"
    doi = {10.1016/j.cels.2018.05.012},
    url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
    year = 2018,
    month = {jun},
    publisher = {Elsevier {BV}},
    volume = {6},
    number = {6},
    pages = {752--758.e1},
    author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
    title = {Community-Driven Data Analysis Training for Biology},
    journal = {Cell Systems}

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