Content
GNPS, in silico structure annotation and MS2LDA provide solutions towards enhanced metabolome mining and structural annotation as well as providing a framework for data sharing. This workshop will enhance the toolkit of the modern clinical metabolomics researcher in maximizing structural information retrieved from a metabolomics experiment as well as highlighting the importance and benefits of data sharing.
Goals and learning points
We will introduce automated tandem mass spectrometry-based dereplication using a combination of the Global Natural Products Social Molecular Networking (GNPS) platform, in silico structure annotation (NAP, SIRIUS+CSI:FingerID followed by MolNetEnhancer) and unsupervised substructure discovery through MS2LDA. Participants will learn:
i) how to submit MZmine preprocessed mass spectrometry data to GNPS
ii) how to run a feature-based mass spectral molecular networking job on the GNPS interface and which parameters are important to consider
iii) how to browse through the results on the GNPS interface
iv) how to run in silico structure prediction through NAP and SIRIUS+CSI:FingerID
v) how to browse through in silico structure results on the GNPS interface
vi) how to run an MS2LDA job on the GNPS interface and which parameters are important to consider
vii) how to browse through the MS2LDA results on the GNPS interface
viii) how to integrate chemical structural information from mass spectral molecular networking, in silico structure annotation and unsupervised substructure discovery through the MolNetEnhancer workflow to highlight broad chemical classes and substructural differences
Format
This will be a hands-on session where participants submit example data to GNPS, perform in silico structure annotation and MS2LDA substructure discovery. Theoretical background as well as practical examples for integration and data interpretation of all platforms will be provided. The session will be interactive, with plenty of room for questions and feedback from the participants. Clinically relevant example data will be provided, however participants are also encouraged to bring their own data.