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DTSTAMP:20260617T092457Z
UID:18211e48-d45c-448f-9a8a-50b3031f0951
DTSTART:20260113T160000Z
DTEND:20260113T170000Z
DESCRIPTION:Educators: \nTimo Sachsenberg\, Tom Müller (CIBI)\n\nDate: \na
 pprox. 60 min\n\nLocation: \nOnline\n\nContents:\nThis session of the VMOL
  Seminar provides a practical introduction to open-source metabolomics dat
 a analysis with pyOpenMS. Participants will get a broad overview of the Op
 enMS ecosystem and learn where pyOpenMS fits in for reproducible\, automat
 able workflow: from quick prototyping to custom method development.\n\nWe 
 will highlight several concrete use cases where participants can benefit f
 rom pyOpenMS and showcase UMetaFlow as an example of an untargeted metabol
 omics workflow that uses advanced feature detection capabilities of OpenMS
 .\n\nParticipants will:\n\nGet a broad overview of the OpenMS ecosystem an
 d metabolomics-relevant components\nLearn what pyOpenMS enables for automa
 tion\, customization\, and reproducibility\nSee practical examples of meta
 bolomics feature detection workflows\nPrerequisites:\nBasic understanding 
 of MS-based metabolomics is helpful\, but not required. Some Python famili
 arity is useful\, but not mandatory.\n\nLearning goals:\n\nUnderstand the 
 OpenMS ecosystem and where pyOpenMS fits for metabolomics.\nIdentify commo
 n analysis scenarios where pyOpenMS is a viable solution\nLearn how featur
 e detection workflows like UMetaFlow are implemented on top of pyOpenMS\nK
 eywords:\nMetabolomics\, computational mass spectrometry\, OpenMS\, pyOpen
 MS\, Python\, untargeted analysis\, reproducible analysis\, UMetaFlow\n\nT
 ools:\nOpenMS\, pyOpenMS\, UMetaFlow\n\nAudience:\nMetabolomics researcher
 s\, students\, core-facility staff\, and developers interested in programm
 able MS data analysis.\n\nContact:\ntimo.sachsenberg@uni-tuebingen.de\nReg
 ister at https://www.functional-metabolomics.com/ms-seminar to receive a l
 ink.
SUMMARY:pyOpenMS for Metabolomics
URL;VALUE=URI:https://www.denbi.de/training-courses-2026/2008-pyopenms-for-
 metabolomics
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