SODAMeets is a platform where data generators and computational scientists can share their use of software/data. For each meeting, we will have two speakers present on software or data they would like to share with the community, emphasizing how these software/data are used. Speakers will be requested to fill out our SODA website so that we collect relevant information on these software/data presented.
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Aug. 13, 2024 at 2:00 pm ET:
Dr. Nick Rigel, Ohio State University
COLMARppm: a chemical shift predictor for small molecules in water
Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. COLMARppm uses an empirical prediction strategy to provide highly accurate chemical shift predictions for metabolites within seconds.
Andrew Patt, Bioinformatics Scientist, National Center for Advancing Translational Sciences
metLinkR: an R package for automated linking of metabolite identifiers to common names
Metabolites are referenced in spectral, structural and pathway databases with a diverse array of schemas, including various internal database identifiers and large tables of common name synonyms. Cross-linking metabolite identifiers is a required step for meta-analysis of metabolomic results across studies but made difficult due to the lack of a consensus identifier system. We have implemented metLinkR, an R package that leverages RefMet and RaMP-DB to automate and simplify cross-linking metabolite identifiers across studies and generating common names. MetLinkR accepts as input metabolite common names and identifiers from five different databases (HMDB, KEGG, ChEBI, LIPIDMAPS and PubChem) to exhaustively search for possible overlap in supplied metabolites from input datasets. In an example of thirteen metabolomic datasets totaling 8,337 metabolites, metLinkR identified and provided common names for 1,231 metabolites in common between at least 2 datasets in less than 5 minutes, and produced standardized names for 72.6% of the input metabolites. In another example comprising five datasets with 3,512 metabolites, metLinkR identified 1,230 metabolites in common between at least two datasets in under 3 minutes, and produced standardized names for 82.4% of the input metabolites. Outputs of MetLInkR include output tables and metrics allowing users to readily double check the mappings and to get an overview of chemical classes represented. Overall, MetLinkR provides a streamlined solution for a common task in metabolomic epidemiology and other fields that meta-analyze metabolomic data. The R package, vignette and source code are freely downloadable at https://github.com/ncats/metLinkR
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