- Targeted feature detection for label-free quantification
- Parallel execution of OpenMS tools in KNIME
- DNA heteroconjugate detection (Flett et al.)
- Non-targeted LC-MS-based lipidomics
- Basic Peptide Identification
- Consensus Peptide Identification
- Peptide Identification and Label-free Quantification
- Protein Inference
- SWATH Analysis
- Small Molecule Identification and Quantification
The workflow exemplifies the analysis capabilities of OpenMS in combination with KNIME. Identification and quantification results are combined and subjected to a simple analysis controlling for the concentration of background peptides being constant and visualize the fold change of investigated peptides across several runs.
What it does:
Quantification will be done with the aid of the intensities of so-called features, the peak pattern resulting from peptides (with isotopic representatives) eluting over a certain time. In this example workflow we will use the TOPP tool FeatureFinderCentroided to find those peptide features. Quantifiable features are extracted from the and mapped against the FDR filtered identification results.
This workflow is part of the OpenMS tutorial and described in detail in the tutorial.
Guide for important instrument-specific FeatureFinderCentroided settings:
| Q-TOF | LTQ Orbitrap | |
| intensity:bins | 10 | 10 |
| mass_trace:mz_tolerance | 0.02 | 0.004 |
| isotopic_pattern:mz_tolerance | 0.04 | 0.005 |