Feature-based Molecular Networking (Hands-On)
Gastgeber/-in: Daniel Petras (CMFI)
The seminar will be held bi-weekly: Every other Tuesday at 11:00–12:00 am (CET)
Ort: Online via Zoom
The seminar will be held remotely through Zoom. Depending on the pandemic situation, we will eventually move to an in person seminar in spring. The zoom link for the seminar will be provided via mail.
All seminar sessions will be recorded and eventually uploaded to youtube.
The seminar will is open to the public. If you have friends/colleagues who you think could be interested in this, feel free to forward the information. For better exchange of information and announcements, we encourage you to:
Sign up with your email and some background information here.
This bi-weekly seminar series is targeted to give a general introduction to contemporary mass spectrometry and in particular how to analyze biomolecules. We will cover fundamental hardware principles on how a mass spectrometer works and application focused data analysis approaches. The seminar is meant to be very interactive and will have a large hands-on component for the data analysis tools.
The goal is to start from zero and bring everybody to a level of executing independently different mass spec data analysis tasks within a few sessions.
As the seminar will be very user focused, the content will only be set for the first lectures and will then evolve based on the participants interest. The idea is hereby that we will have guest lectures from experts in the field (e.g. mass spec software developers) as well as participants of the seminar who want to highlight their own data acquisition and data analysis approaches. That said, the seminar shall serve as a dynamic platform to teach and exchange ideas and concepts in mass spectrometry.
The focus will be at the beginning on non-targeted LC-MS/MS and data analysis with GNPS (gnps.ucsd.edu) and MZmine (mzmine.github.io) environment but might then shift to other metabolomics approaches (e.g. targeted metabolomics and other non-targeted approaches such as flow-injection analysis) as well as proteomics methods (e.g. shot-gun and top-down proteomics).
All software highlighted will be open source.