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) ). 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) ) 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) ).

mums2

mums2 is a package that was created to supply researchers with a collection of tools that they can use to analyze untargeted metabolomics data. This package will supply tools to analysis tandem Mass spectrometry (MS) data in a easy and efficient manner. The package supports importation and filtration of MS1 feature tables, annotations, de novo molecular formula predictions, scoring of MS2 spectra, clustering, alpha and beta diversity calculations, and community distance calculations.

Installation

You can install the CRAN version with:

install.packages("mums2")

You can install the development version of mums2 from GitHub with:

devtools::install_github("mums2/mums2")

Get started

See the Getting Started page to get started.

Getting help

If you encounter an issue, please file an issue on GitHub. Please include a minimal reproducible example with your issue.

Contributing

Is there a feature you’d like to see included, please let us know! Pull requests are welcome on GitHub.