- 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
- detection of lipid features (i.e., m/z, intensity and retention time centroids with their convex peak hulls for a specific lipid ion) in individual samples
- alignment of samples by retention time
- finding of corresponding lipid features across samples
- feature identification via search by accurate mass or spectral matching
The workflow further demonstrates how one can use KNIME nodes for interactive visualization, and how to do statistical analyses (exemplified in a discovery section for compounds with significantly differing intensities) with in this case scripting nodes for the statistical language R. The use of KNIME nodes for table operations allows to combine results of various subsections to e.g, annotate quantified compounds of interest with putative identifications, or to find features with probable MS/MS matches and suitable precursor information. If chromatographic information about lipid behaviour is available in sufficient detail, it is also sensible to think about using retention time information for filtering. Our demonstration workflow provides a subsection dedicated to filtering mass-based ID candidates via errors between experimental and predicted retention times. The section in question has previously been shown to achieve very good filtering results for an analysed dataset.
There rarely exists a one-size-fits-all solution pipeline. This is especially true for the still developing field of metabolomics. Here, oftentimes each study requires its own custom-tailored analysis pipeline. In this light, our workflow could be considered more of a starting point or initial template to build upon for your individualized lipidomics analyses. We hope this demonstration workflow could illustrate some of the capabilities of OpenMS in KNIME, and convince you of its modularity and adaptability.