{
    "info": {
        "author": "Joshua Klein",
        "author_email": "jaklein@bu.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": ".. image:: https://raw.githubusercontent.com/mobiusklein/ms_deisotope/master/docs/_static/logo.png\r\n\r\nA Library for Deisotoping and Charge State Deconvolution For Mass Spectrometry\r\n------------------------------------------------------------------------------\r\n\r\nThis library combines `brainpy` and `ms_peak_picker` to build a toolkit for\r\nMS and MS/MS data. The goal of these libraries is to provide pieces of the puzzle\r\nfor evaluating MS data modularly. The goal of this library is to combine the modules\r\nto streamline processing raw data.\r\n\r\n\r\nInstalling\r\n----------\r\n\r\nBuilding from source requires a version of Cython >= 0.27.0\r\n\r\n\r\nAPI\r\n---\r\n\r\n\r\nData Access\r\n===========\r\n\r\n``ms_deisotope`` can read from mzML, mzXML and MGF files directly, using the ``pyteomics`` library.\r\nOn Windows, it can also use ``comtypes`` to access Thermo's MSFileReader.dll to read RAW files and\r\nAgilent's MassSpecDataReader.dll to read .d directories. Whenever possible, the library provides a\r\ncommon interface to all supported formats. With Thermo's pure .NET library, it can use ``pythonnet``\r\nto read Thermo RAW files on Windows and Linux (and presumably Mac, too).\r\n\r\n.. code:: python\r\n\r\n    from ms_deisotope import MSFileReader\r\n    from ms_deisotope.data_source import mzxml\r\n\r\n    # open a file, selecting the appropriate reader automatically\r\n    reader = MSFileReader(\"path/to/data.mzML\")\r\n\r\n    # or specify the reader type directly\r\n    reader = mzxml.MzXMLLoader(\"path/to/data.mzXML\")\r\n\r\n\r\nAll supported readers provide fast random access for uncompressed files, and support the Iterator\r\ninterface.\r\n\r\n.. code:: python\r\n\r\n    # jump the iterator to the MS1 scan nearest to 30 minutes into the run\r\n    reader.start_from_scan(rt=30)\r\n\r\n    # read out the next MS1 scans and all associated MSn scans\r\n    scan_bunch = next(reader)\r\n    print(scan_bunch.precursor, len(scan_bunch.products))\r\n\r\n\r\nAveragine\r\n=========\r\n\r\nAn \"Averagine\" model is used to describe the composition of an \"average amino acid\",\r\nwhich can then be used to approximate the composition and isotopic abundance of a\r\ncombination of specific amino acids. Given that often the only solution available is\r\nto guess at the composition of a particular *m/z* because there are too many possible\r\nelemental compositions, this is the only tractable solution.\r\n\r\nThis library supports arbitrary Averagine formulae, but the Senko Averagine is provided\r\nby default: `{\"C\": 4.9384, \"H\": 7.7583, \"N\": 1.3577, \"O\": 1.4773, \"S\": 0.0417}`\r\n\r\n.. code:: python\r\n\r\n    from ms_deisotope import Averagine\r\n    from ms_deisotope import plot\r\n\r\n    peptide_averagine = Averagine({\"C\": 4.9384, \"H\": 7.7583, \"N\": 1.3577, \"O\": 1.4773, \"S\": 0.0417})\r\n\r\n    plot.draw_peaklist(peptide_averagine.isotopic_cluster(1266.321, charge=1))\r\n\r\n\r\n`ms_deisotope` includes several pre-defined averagines (or \"averagoses\" as may be more appropriate):\r\n    1. Senko's peptide - `ms_deisotope.peptide`\r\n    2. Native *N*- and *O*-glycan - `ms_deisotope.glycan`\r\n    3. Permethylated glycan - `ms_deisotope.permethylated_glycan`\r\n    4. Glycopeptide - `ms_deisotope.glycopeptide`\r\n    5. Sulfated Glycosaminoglycan - `ms_deisotope.heparan_sulfate`\r\n    6. Unsulfated Glycosaminoglycan - `ms_deisotope.heparin`\r\n\r\nDeconvolution\r\n=============\r\n\r\nThe general-purpose averagine-based deconvolution procedure can be called by using the high level\r\nAPI function `deconvolute_peaks`, which takes a sequence of peaks, an averagine model, and a isotopic\r\ngoodness-of-fit scorer:\r\n\r\n.. code:: python\r\n\r\n    import ms_deisotope\r\n\r\n    deconvoluted_peaks, _ = ms_deisotope.deconvolute_peaks(peaks, averagine=ms_deisotope.peptide,\r\n                                                           scorer=ms_deisotope.MSDeconVFitter(10.))\r\n\r\nThe result is a deisotoped and charge state deconvoluted peak list where each peak's neutral mass is known\r\nand the fitted charge state is recorded along with the isotopic peaks that gave rise to the fit.\r\n\r\nRefer to the documentation for a deeper description of isotopic pattern fitting.\r\n\r\n",
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        "name": "ms-deisotope",
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        "summary": "Access, Deisotope, and Charge Deconvolute Mass Spectra",
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