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FBMN with MZmine2

Introduction

The Feature-Based Molecular Networking (FBMN) is a computational method that bridges popular mass spectrometry data processing tools for LC-MS/MS and molecular networking analysis on GNPS. The tools supported are: MZmine2, OpenMS, MS-DIAL, MetaboScape, XCMS, and Progenesis QI.

The main documentation for Feature-Based Molecular Networking can be accessed here:

Below we are describing how to use MZmine2 with the FBMN workflow on GNPS.

Mass spectrometry processing with MZmine2

Download the latest version of MZmine2 software (version MZmine v2.33 minimum) at https://github.com/mzmine/mzmine2/releases.

Citations and development

This work builds on the efforts of our many colleagues, please cite their work:

Katajamaa, M., Miettinen, J. & Oresic, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634–636 (2006).

Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010).

Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016)

The development of the features used in the pipeline is publicly accessible here.

Mass Spectrometry Data Processing with MZmine2: step-by-step documentation

In MZmine2, a sequence of steps are performed to process the mass spectrometry data. Here we will present key steps required to process LC-MS/MS data acquired in non-targeted mode (data dependent acquisition). For conveniency we are also providing a batch file (XML format) that can be imported directly in MZmine2.

IMPORTANT: MZmine2 parameters will vary depending on the instrument used, the acquisition parameters, and the sample studied. The following documentation serves a basic guideline for using MZmine2 with the Feature-Based Molecular Networking workflow.

Please consult the resources below for more details on MZmine2 processing:

  • The video tutorial about [Quick MZMine2 Export to GNPS for Feature Based Molecular Networking]

Download the MZmine2 software

Download the latest version of MZmine2 software (version MZmine v2.33 minimum) at https://github.com/mzmine/mzmine2/releases.

Convert your LC-MS/MS Data to Open Format

MZmine2 accepts different input formats. Note that we recommand to first convert your files to mzML format before doing MZmine2 processing. See the documentation here.

Processing Steps

Below is the schematic representation of the LC-MS/MS data processing steps with MZmine2:

complete workflow view

Below is the overview of the LC-MS/MS data processing steps in the MZmine2 batch mode:

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Batch Import

Here are some MZmine2 batch that are compatible with the Feature-Based Molecular Networking workflow. These batch files can be imported into MZMine2 (Batch mode):

Instrument Gradient Length Matrix Type Sample Size Download
Bruker Maxis HD qTof 10 Min Stool 20 Batch

Processing Steps

Below is a walk through of all the steps

1. Import Files

Go to Menu: Raw data methods / Raw data import / "Select the files"

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2. Mass Detection

This step creates mass lists from your raw data.

Perform mass detection on MS level 1: Menu: Raw data methods / Mass detection / Set filter : MS level 1

IMPORTANT Set an appropriate intensity threshold. You can use the preview window to assess the right threshold on your data. As a rule of thumb, the value should at least correspond to the minimum value set for the triggering of the MS2 scan event. (Example: MAXIS-QTOF: 1E3, Q-Exactive 1E4)

Perform mass detection on MS level 2. The same mass list name must be used.

Go to Menu: Raw data methods / Mass detection / Set filter : MS level 2.

IMPORTANT: Make sure to set an intensity threshold representative of noise level in the MS2 spectra. This is typically lower than for MS1. (Example: maXis QTOF: 1E2; LTQ-XL Orbitrap 1E4, Q-Exactive: 0). If you have any doubt, set it to 0.

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3. Build Chromatogram (LC-MS feature detection part 1)

Go to Menu: Raw data methods / Chromatogram builder

4. Deconvolve the Chromatogram (LC-MS feature detection part 2)

Go to Menu: Peak list methods / Peak detection / Chromatogram deconvolution

IMPORTANT: tick both options "m/z range for MS2 scan pairing (Da)" and "RT range for MS2 scan pairing (min)". The values have to be defined according to your experimental setup (expected chromatographic peak width and the MS mass accuracy).

Example for a UHPLC colum (1.7 µm C18, 50 × 2.1 mm, flow rate of 0.5 mL/min):

  • maXis-QTOF: 12 min gradient, 0.02 Da and 0.15 min

  • Q-Exactive: 5 min gradient, 0.01 Da and 0.1 min

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5. Group isotopes and co-eluting ions

Use the "Isotopic peaks grouper" [recommended] or other alternative (such as the CAMERA module).

Go to Menu: Peak list methods / Isotopes / Isotopic peaks grouper.

IMPORTANT: This depends on your expected peak shapes, duty cycle time and the MS mass accuracy. (Example: MAXIS-QTOF, 10 min gradient, 0.1 min, 0.02 m/z; Q-Exactive, 5 min gradient, 0.05 min, 0.01 m/z)

6. Order the peaklists

Go to Menu: Peak list methods / Order peak lists.

IMPORTANT: This is to ensure the reproducibility of the processing Indeed, the aligned peaklist will change slighlty if that step is not performed.

7. LC-MS feature alignement (Peaklist alignement)

In this step, the peak lists from each samples will be aligned in one aligned peak list. The alignement is performed iteratively using the first peaklist selected (see MZmine documentation). For that reason, make sure the first sample is adapted (not a negative control) or to manually put an representative peaklist in first position.

Go to Menu: Peak list methods / Alignment / Join aligner

8. Detect Missing Peaks / Gap Filling (Optional)

Gap filling enables to retrieve the intensity of a peak in all the samples, even if it was not detected in a previous processing step. Go to Menu: Peak list methods / Gap filling / Peak finder (multi-threaded).

IMPORTANT: This step is optional. Use the multi-threaded peak finder for fast processing.

9. Filter the Peaklist to MS/MS Peaks (Optional)

Depending on the number of features in the aligned peaklist, it is possible to filter the peaklist to keep only features with minimum number of occurences ("Minimum peaks in a row") or a mininum number of isotopic peaks for the feature ("Minimum peaks in an isotope pattern"), or to "Keep only peaks with MS2 scan (GNPS)".

Go to Menu: Peak list methods / Filtering / Peak list row filter / Select the filters

IMPORTANT: if you use a filter, we recommend using the filter "Reset the peak number ID"

IMPORTANT Note that this step was mandatory in the previous version of FBMN with MZmine2, now the filter "Keep only peaks with MS2 scan (GNPS)" is optional.

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10. Use the GNPS Export module

Use the dedicated module "Submit to/Export for GNPS" in MZmine to export the needed file:

  • a feature quantification table with LC-MS feature intensities (.CSV file format)

  • a MS/MS spectral summary file, with a representative MS/MS spectrum per LC-MS feature. list of the MS/MS spectra (.MGF file format) (the most intense MS/MS per feature is selected).

Select the last "filtered aligned peaklist" and Go to Menu: "Peak list methods" / "Export" / "Export for/Submit to GNPS"

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See an example of files outputted by the export module using the workflow: here.

The files can be uploaded to the GNPS web-platform and Feature-Based Molecular Networking job can be directly launched

IMPORTANT: While the possibility to submit the files directly to GNPS and launch a FBMN job on the fly is really convenient for quick data analysis, the job and files will not be saved to your personal account on GNPS if you do not provide username/password, and you are limited to basic presets of parameters. For that reason, we recommend to upload your files with the FTP uploader (see documentation) and prepare your job directly on GNPS (you must be logged in first), or alternatively to clone the submitted job.

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In the "Export for/Submit to GNPS" module, select the option: "Submit to GNPS"

  • [Optional] Metadata file: specify the path to the metadata table in GNPS format. [See documentation here] (networking.md/#metadata)

  • Select the parameters presets for the GNPS job.

  • Email: specify the email to forward the job link

  • Annotation edges (Experimental): that option can be ignored.

  • Open website: if ticked, will open the job webpage.

ADDITIONAL NOTES: The feature table must contain at least the row ID, the row m/z, and row retention time, along with the sample columns. It is currently mandatory for the sample name headers string to have the following format: "filename Peak area". Depending on Note that depending on the steps used in MZmine the sample name header can be "filename baseline-corrected Peak area", but this has to changed back to "filename Peak area".

Video Tutorial - Quick MZMine2 Export to GNPS for FBMN.

Feature Based Molecular Networking in GNPS

The main documentation of the Feature Based Molecular Networking workflow on GNPS can be consulted on that page. The workflow for Feature Based Molecular Networking in GNPS is different from the "classic" molecular networking workflow. Access the FBMN workflow here (You need to be logged in first !).

Basically, you will need to upload the files ouputted by the MZmine2 processing (test files are accessible here):

  1. A feature quantification table (.CSV file format).
  2. A MS/MS spectral summary (.MGF file format)
  3. A metadata table - described here

There are several additional normalization options specifically for feature detection. We can normalize the features per LC/MS run and aggregate by groups with either a sum or average (recommended).

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Video Tutorial - Analyze Feature Based Molecular Networking in GNPS

This video presents

Feature-Based Molecular Networking in Cytoscape

Cytoscape is an open source software platform used to visualize, analyze and annotate molecular networks from GNPS. See the documentation here

Demo GNPS job of Feature Based Molecular Networking

Here is an example FBMN job with files resulting from MZmine2 processing of a subset of the [American Gut Project] (http://humanfoodproject.com/americangut/).

Tutorials

See our tutorial on using MZmine2 for FBMN analysis of a cohort from the [American Gut Project] (http://humanfoodproject.com/americangut/), and our tutorial on running a FBMN analysis on GNP.

Page contributors

Louis Felix Nothias (UCSD), Daniel Petras (UCSD), Ming Wang (UCSD)

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