Running mzOS as a script

Perform analysis

Installing mzOS made a script available in your current environnement. It is called mzos. The aim of this script is to perform an analysis given a XCMS peaklist:

  • Deisotoping
  • Feature annotation (adducts, fragments)
  • Database search (only HMDB and LMSD are supported for now)
  • Annotation confidence estimation
    • using network presence/missing metabolites information (Bayesian inference)
    • using observed/theoritical isotopic pattern

Parameters

parser.add_argument("-x", "--xcms_pkl", type=str, help="path to the xcms peaklist", required=True)
parser.add_argument("-p", "--polarity", default='negative', choices=['negative', 'positive'], help='experiment polarity', required=True)
parser.add_argument("--mz_tol_ppm", type=float, default=10.0, help='mass over charge tolerance', required=False)
parser.add_argument("--dims", default=False, action='store_true', help='direct infusion MS experiment', required=False)
parser.add_argument('--db', default='hmdb', choices=['hmdb', 'lmsd', 'hmdb + lmsd'], required=False)
parser.add_argument("--output", type=str, default="annotations.tsv", required=False)
parser.add_argument("--bayes", default=True, required=False)

--xcms_pkl

Simply the path to the XCMS peaklist

--polarity

Polarity used to acquire spectra. Should be 'negative' or 'positive'

--mz_tol_ppm

Tolerance in mass precision used in algorithm (in ppm). Defautlt 10.

--dims

If the experiment is a direct infusion experiment True or False.

--db

Database to search for. 'hmdb' or 'lmsd' or 'hmdb + lmsd'

--ouput

Path to the result directory

--bayes

Perform Bayesian algorithm or not (can be time consuming). True or False.

Example

Be sure to activate the virtual environnement where you installed mzOS.

mzos --xcms_pkl C:\Users\M\xcms_results.tsv --polarity 'negative' --db 'hmdb + lmsd' --output '.\annotations.csv'

Result file

Matrix with several columns:

  • id: id of the feature.

  • mz: mass over charge

  • time: time elution

  • Main putative attribution: the most probable tag that can be assigned to this feature. It can be:

    • a monoistope + n isotopes + n adducts/fragments

    • an isotope (C13 ...)

    • an adduct

    • a fragment

    • nothing

  • Main attribution pattern composition: one of the most important column, showing all relations between features. For example:

    • feature detected as an isotope in Main putative tag shows Isotope C13 of 2546 for charge=1. Here you can find to which feature it has been detected as a C13 isotope. You have also a charge information.

    • feature detected as a monoisotope:

  • Putative secondary attributions: All possible other attributions (or tag) that algorithm found but set as secondary (main is considered as the most probable)

  • Putative annotation: matching metabolite(s)

  • Putative formula: chemical formula of the matching metabolite

  • Inchi: inchi metabolite formula

  • Database_id: Database Id such as HMDB_ID and KEGG_ID

  • Isotopic pattern matching score: RMSD between observed and theoritical isotopic pattern intensities

  • Annotation assignment probability: Using network analysis to infer a presence probability using a bayesian algorithm (see metsamp)

  • Annotation pattern composition: Isotopic pattern detected to use the Isotopic pattern matching score