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Galaxy for Mass Spectrometry

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Outline

Introduction to Mass Spectrometry

Instrumentation
Acquisition methods
MS vs. NGS

MS Data Processing

Tools
Methods
Issues

Community Recommendations

Giving Feedback
Documentation and Training
Being aware of Standards and ongoing Developments

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Mass Spectrometry

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High-throughput technologies - MS

Mass Spectrometry (MS)
Measure electromagnetic properties (mass-to-charge ratio [m/z]) of ions to calculate mass & elucidate structure
Widely applied for peptide/proteins & small molecules

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High-throughput technologies – MS coupling

MS is often preceded by chromatography

Major approaches are:

Full-scan (non-target)
Selective (target)

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Selective acquisition

Scan selected ions (m/z) specific to molecules of interest
How much of molecule is present?

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Full-scan analysis

Identify what chemicals are present?
Quantification (even relative) is difficult

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Various MS scan methods (not mentioning different collision types)�

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Precursor scan (MS1)	Product scan (MS2)	MS/MS technique	Additional info
Selected ion monitoring (SIM)	Selected ion monitoring	Selected reaction monitoring (SRM) / Multiple reaction monitoring (MRM)
Selected ion monitoring (SIM)	Full-scan	Parallel reaction monitoring (PRM)
Full-scan	Selected ion monitoring	Full Scan-SIM
	Detection filter(s)	Data dependent acquisition (DDA)/ Information dependent (IDA)	Top N (abundance), neutral loss, precursor ion(s)
	Full scan	Data independent acquisition (DIA)	Window: SWATH, SONAR, variable (vDIA), PAcIFIC etc.
	Full scan	Data independent acquisition (DIA)	No windows: All ions fragmentation (AIF), MS^E etc.

Data complexity

Theoretical detection

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Sequencing & mass spectrometry

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Sequencing 1 million bases

Low-resolution MS (100,000 scans)

Mid-resolution MS (100,000 scans)

High-resolution MS (100,000 scans)

1000 to 10000 x more costly

Large-scale sequencing remains cheaper & easier than large-scale MS

Image: 10.1038/s41570-017-0054

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Sequencing & mass spectrometry

Sequencing is more robust than MS
MS methods provide finer details about phenotype due to greater inclusion of external factors
Sequencing shows all potential biological reactions that can occur
MS shows biological and non-biological reactions that occur

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Further material: 10.1038/s41437-019-0209-z & 10.1038/s41570-017-0054

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MS Data Processing

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Tools Overview

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Processing Overview

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Compatability Issues Example: MSP Files

NIST Format

RECETOX – RIKEN Format

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Spectra with raw intensity & sub-structure annotation

Unique structural identifier

License to enable sharing

Spectra as normalized matrix array

Low resolution i.e. nominal mass

“Cleaned” spectra do not directly represent measurement

Non-unique chemical identifier

Structure is not directly represented

No license

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Galaxy (Tools) Landscape

Many incompatible modules for the same application
Domain – data – instrumentation boundary mismatches
Many non-maintained and de-precated tools
Lack of training material and user reports
Missing standardization

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Recommendations

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Giving Feedback and Reporting Problems

Report errors upstream via GitHub issues or bug reports
Ask developers to provide training, detailed documentation, examples and usecases
Give feedback to training developers

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Giving feedback is important. If something doesn‘t work, let people know. While this is often common practice within the team or institute, there is an inherent communication barrier when it comes to openly posting issues or contacting some unknown person. But most developers are actually very happy about any interaction with a user because an email saying that something doesn‘t work at least means that someone is using the tools we spend so much time developing.

If you have a local bioinformatics team or are using a software, ask the developers to provide training and tutorials for their tool. If there exists already some training, remember to use those Resources and to give feedback – every GTN training actually has a feedback field at the end of the tutorial and the statistics from the feedback are publicly available.

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Documentation & Training

Detailed methods
Contribute a Galaxy Training for your Analysis
Publish a workflow for sub-steps as well as the whole analysis
Annotate tools with metadata and deposit them in a registry

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Being aware of Standards

HUPO Proteomics Standards Initiative

standards not only for proteomics
Regular open calls to discuss standard development

FAIRsharing

Curated list of standards, databases, etc. for different domains

Long standing developments

ELIXIR (commissioned) services and other research infrastructure activities

Research Data Alliance recommendations and outputs

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Reuse, Reduce & Recycle (Software)

Reuse mature software components & contribute instead of starting from scratch
Reduce complexity (& sometimes user friendliness) in favour of generalization and modularity
Instead of making specialized derivatives, generalize an existing codebase

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Galaxy for Mass Spectrometry Community

Establish a global network for Galaxy applications in mass spectrometry
Hosting collective meetings
Coordinating shared resources (software, data, tutorials)
Coordinating shared projects and collaborations across Galaxies
Promoting collaboration and engagement with community stakeholders

�Get involved in the agenda and on matrix!

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Acknowledgements

RECETOX Research Infrastructure (RECETOX RI) supported by Ministry of Education, Youth and Sports (LM2023069), H2020 CETOCOEN Excellence 857560 and OP RDE CZ.02.1.01/0.0/0.0/17_043/0009632). The RECETOX RI is a Czech node of the European Environmental Exposure Assessment Research Infrastructure (EIRENE RI).

This work was funded [or supported] by ELIXIR, the research infrastructure for life-science data through the ELIXIR Staff Exchange Program.

This presentation reflects only the author’s view and the European Commission is not responsible for any use that may be made of the information it contains.

Computational resources provided by the e-INFRA CZ (LM2018140).

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