{
  "_id": "6a2b2c78578398594318bae5",
  "Package": "mums2",
  "Title": "Microbial Ecology by Tandem Mass Spectrometry",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Allison\", \"Mason\", , \"masonar@umich.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-1339-1592\")),\nperson(\"Gregory\", \"Johnson\", , \"grejoh@umich.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0009-0008-3890-0297\")),\nperson(\"Patrick\", \"Schloss\", , \"pschloss@umich.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-6935-4275\")),\nperson(\"Anton\", \"Pervukhin\", , \"apervukh@minet.uni-jena.de\", role = c(\"ctb\", \"cph\")),\nperson(\"Florian\", \"Rasche\", , \"m3rafl@minet.uni-jena.de\", role = c(\"ctb\", \"cph\")),\nperson(\"Henner\", \"Sudek\", , \"Henner.Sudek@CeBiTec.Uni-Bielefeld.DE\", role = c(\"ctb\", \"cph\")),\nperson(\"Marcel\", \"Martin\", , \"Marcel.Martin@CeBiTec.Uni-Bielefeld.DE\", role = c(\"ctb\", \"cph\")),\nperson(\"Yuanyue\", \"Li\", , \"mail@yli.one\", role = c(\"ctb\", \"cph\"))\n)",
  "Description": "Tools that researchers can use to analyze untargeted\nmetabolomics data generated using tandem mass spectroscopy from\nmicrobial communities. The overall approach taken to analyze\nmetabolomics data parallels that used to analyze microbial\ncommunities using 16S rRNA gene sequencing data. Thus, we have\na number of methods a user is able to use to generate data.\nFirstly, users can import Mass Spectrometry 1(MS1) data and\nfilter it. Users are then able to match Mass Spectrometry\n2(MS2) data to the filtered (or unfiltered) MS1 data. With the\nmatched data users are able to cluster it, annotate it, predict\nde novo chemical formulas and calculate alpha and beta\ndiversity. For chemical formula predictions, this was the\nmethod used; \"Towards de novo identification of metabolites by\nanalyzing tandem mass spectra\" (Sebastian Böcker, Florian\nRasche (2008) <doi:10.1093/bioinformatics/btn270>). The\nsimilarity/dissimilarity calculations we used to cluster our\ndata together was: \"Spectral entropy outperforms MS/MS dot\nproduct similarity for small-molecule compound identification\"\n(Li, Y., Kind, T., Folz, J. et al. (2021)\n<doi:10.1038/s41592-021-01331-z>) and \"Sharing and community\ncuration of mass spectrometry data with Global Natural Products\nSocial Molecular Networking\" (Wang, M., Carver, J., Phelan, V.\net al. (2021) <doi:10.1038/nbt.3597>).",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
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  "URL": "https://github.com/mums2/mums2, https://www.mums2.org/mums2/",
  "BugReports": "https://github.com/mums2/mums2/issues",
  "biocViews": "",
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  "Repository": "https://mums2.r-universe.dev",
  "Date/Publication": "2026-06-11 19:47:13 UTC",
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  "Packaged": {
    "Date": "2026-06-11 21:25:47 UTC",
    "User": "root"
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  "Author": "Allison Mason [aut] (ORCID: <https://orcid.org/0000-0003-1339-1592>),\nGregory Johnson [aut] (ORCID: <https://orcid.org/0009-0008-3890-0297>),\nPatrick Schloss [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6935-4275>),\nAnton Pervukhin [ctb, cph],\nFlorian Rasche [ctb, cph],\nHenner Sudek [ctb, cph],\nMarcel Martin [ctb, cph],\nYuanyue Li [ctb, cph]",
  "Maintainer": "Patrick Schloss <pschloss@umich.edu>",
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        "Process data",
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        "Preprocess MS2 data",
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