Educators:
Timo Sachsenberg (CIBI)

Date:
09.09.2019

Location:
GCB 2021 - Online

Contents:
The course introduces key concepts of non-targeted label-free proteomics. Non-targeted methods are ideal for unbiased discovery studies and scale well for large-scale studies (e.g., clinical proteomics). Based on example datasets we will then introduce several open-source software tools for proteomics primarily focusing on OpenMS (www.openms.org). We will demonstrate how these tools can be combined into complex data analysis workflows including visualization of results. Participants will have the opportunity to design custom analysis workflows together with instructors.

Prerequisites:
Target audience are computational scientists interested in working with raw mass spectrometric data.

Learning goals:
- Introduction to computational mass spectrometry proteomics
- OpenMS and the integration platform KNIME
- Hands-on: Identification and Quantification workflow for Label-free quantitative proteomics
- Optional: Developing tools with the OpenMS library
- Optional: Large scale data processing with OpenMS (nextflow or galaxy)

Software Requirements:
Installer versions of required software will be made available.

Keywords:
LC-MS based proteomics, OpenMS, workflows, KNIME, data analysis

Tools:
OpenMS/pyOpenMS, KNIME