Common utilities for interpreting mass spectrometry data
Project description
mass2chem - low level utilities in interpreting mass spectrometry data
This package provides
- functions on handling chemical formulas
- formula based adduct calculation
- indexing and search functions on mass spec data
- libraries of common metabolites, contaminants, mass differences
- [to-do] functions of chemical similary, dataset similarity
Related tools
-
Generalized computing of isotopes and adducts: khipu (https://github.com/shuzhao-li-lab/khipu, https://pubs.acs.org/doi/10.1021/acs.analchem.2c05810)
-
High-level metabolite functions and metabolic models: Json's Metabolite Services (JMS, https://github.com/shuzhao-li-lab/JMS)
-
Metabolomics data processing: asari (https://github.com/shuzhao-li-lab/asari, https://www.nature.com/articles/s41467-023-39889-1)
-
Python-Centric Pipeline for Metabolomics (https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics)
-
Common data models for metabolomics: metDataModel (https://github.com/shuzhao-li/metDataModel)
Third party references:
https://github.com/opencobra/cobrapy/blob/devel/cobra/core/formula.py (using average molecular weight at the time of retrieval, not mass spec oriented)
https://github.com/domdfcoding/chemistry_tools
Pychemy (https://github.com/ginkgobioworks/pychemy). Pychemy at this time isn't good fit for high-resolution metabolomics because its mass calculation is not of enough precision. E.g. in pychemy.adducts, it's wrong to use ('M+3H', 0.33, 1.0073), because the computing/rounding error in 0.33 (correct is 1/3) is far too large for mass precision. For high-resolution measurements, electrons should be considered too.
Please do not hesitate to contact us via the GitHub issues.
Citation to come.
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