Educators:
Timo Sachsenberg, Tom Müller (CIBI)

Date:
approx. 60 min

Location:
Online

Contents:
This session of the VMOL Seminar provides a practical introduction to open-source metabolomics data analysis with pyOpenMS. Participants will get a broad overview of the OpenMS ecosystem and learn where pyOpenMS fits in for reproducible, automatable workflow: from quick prototyping to custom method development.

We will highlight several concrete use cases where participants can benefit from pyOpenMS and showcase UMetaFlow as an example of an untargeted metabolomics workflow that uses advanced feature detection capabilities of OpenMS.

Participants will:

Get a broad overview of the OpenMS ecosystem and metabolomics-relevant components
Learn what pyOpenMS enables for automation, customization, and reproducibility
See practical examples of metabolomics feature detection workflows
Prerequisites:
Basic understanding of MS-based metabolomics is helpful, but not required. Some Python familiarity is useful, but not mandatory.

Learning goals:

Understand the OpenMS ecosystem and where pyOpenMS fits for metabolomics.
Identify common analysis scenarios where pyOpenMS is a viable solution
Learn how feature detection workflows like UMetaFlow are implemented on top of pyOpenMS
Keywords:
Metabolomics, computational mass spectrometry, OpenMS, pyOpenMS, Python, untargeted analysis, reproducible analysis, UMetaFlow

Tools:
OpenMS, pyOpenMS, UMetaFlow

Audience:
Metabolomics researchers, students, core-facility staff, and developers interested in programmable MS data analysis.

Contact:
[email protected]
Register at https://www.functional-metabolomics.com/ms-seminar to receive a link.