mums2 - Microbial Ecology by Tandem Mass Spectrometry
Tools that researchers can use to analyze untargeted
metabolomics data generated using tandem mass spectroscopy from
microbial communities. The overall approach taken to analyze
metabolomics data parallels that used to analyze microbial
communities using 16S rRNA gene sequencing data. Thus, we have
a number of methods a user is able to use to generate data.
Firstly, users can import Mass Spectrometry 1(MS1) data and
filter it. Users are then able to match Mass Spectrometry
2(MS2) data to the filtered (or unfiltered) MS1 data. With the
matched data users are able to cluster it, annotate it, predict
de novo chemical formulas and calculate alpha and beta
diversity. For chemical formula predictions, this was the
method used; "Towards de novo identification of metabolites by
analyzing tandem mass spectra" (Sebastian Böcker, Florian
Rasche (2008) <doi:10.1093/bioinformatics/btn270>). The
similarity/dissimilarity calculations we used to cluster our
data together was: "Spectral entropy outperforms MS/MS dot
product similarity for small-molecule compound identification"
(Li, Y., Kind, T., Folz, J. et al. (2021)
<doi:10.1038/s41592-021-01331-z>) and "Sharing and community
curation of mass spectrometry data with Global Natural Products
Social Molecular Networking" (Wang, M., Carver, J., Phelan, V.
et al. (2021) <doi:10.1038/nbt.3597>).
