Metadata-Version: 1.1
Name: ms-deisotope
Version: 0.0.9
Summary: Access, Deisotope, and Charge Deconvolute Mass Spectra
Home-page: UNKNOWN
Author: Joshua Klein
Author-email: jaklein@bu.edu
License: UNKNOWN
Project-URL: Issue Tracker, https://github.com/mobiusklein/ms_deisotope/issues
Project-URL: Source Code, https://github.com/mobiusklein/ms_deisotope
Project-URL: Documentation, https://mobiusklein.github.io/ms_deisotope
Description: .. image:: https://raw.githubusercontent.com/mobiusklein/ms_deisotope/master/docs/_static/logo.png
        
        A Library for Deisotoping and Charge State Deconvolution For Mass Spectrometry
        ------------------------------------------------------------------------------
        
        This library combines `brainpy` and `ms_peak_picker` to build a toolkit for
        MS and MS/MS data. The goal of these libraries is to provide pieces of the puzzle
        for evaluating MS data modularly. The goal of this library is to combine the modules
        to streamline processing raw data.
        
        
        Installing
        ----------
        
        Building from source requires a version of Cython >= 0.27.0
        
        
        API
        ---
        
        
        Data Access
        ===========
        
        ``ms_deisotope`` can read from mzML, mzXML and MGF files directly, using the ``pyteomics`` library.
        On Windows, it can also use ``comtypes`` to access Thermo's MSFileReader.dll to read RAW files and
        Agilent's MassSpecDataReader.dll to read .d directories. Whenever possible, the library provides a
        common interface to all supported formats. With Thermo's pure .NET library, it can use ``pythonnet``
        to read Thermo RAW files on Windows and Linux (and presumably Mac, too).
        
        .. code:: python
        
            from ms_deisotope import MSFileReader
            from ms_deisotope.data_source import mzxml
        
            # open a file, selecting the appropriate reader automatically
            reader = MSFileReader("path/to/data.mzML")
        
            # or specify the reader type directly
            reader = mzxml.MzXMLLoader("path/to/data.mzXML")
        
        
        All supported readers provide fast random access for uncompressed files, and support the Iterator
        interface.
        
        .. code:: python
        
            # jump the iterator to the MS1 scan nearest to 30 minutes into the run
            reader.start_from_scan(rt=30)
        
            # read out the next MS1 scans and all associated MSn scans
            scan_bunch = next(reader)
            print(scan_bunch.precursor, len(scan_bunch.products))
        
        
        Averagine
        =========
        
        An "Averagine" model is used to describe the composition of an "average amino acid",
        which can then be used to approximate the composition and isotopic abundance of a
        combination of specific amino acids. Given that often the only solution available is
        to guess at the composition of a particular *m/z* because there are too many possible
        elemental compositions, this is the only tractable solution.
        
        This library supports arbitrary Averagine formulae, but the Senko Averagine is provided
        by default: `{"C": 4.9384, "H": 7.7583, "N": 1.3577, "O": 1.4773, "S": 0.0417}`
        
        .. code:: python
        
            from ms_deisotope import Averagine
            from ms_deisotope import plot
        
            peptide_averagine = Averagine({"C": 4.9384, "H": 7.7583, "N": 1.3577, "O": 1.4773, "S": 0.0417})
        
            plot.draw_peaklist(peptide_averagine.isotopic_cluster(1266.321, charge=1))
        
        
        `ms_deisotope` includes several pre-defined averagines (or "averagoses" as may be more appropriate):
            1. Senko's peptide - `ms_deisotope.peptide`
            2. Native *N*- and *O*-glycan - `ms_deisotope.glycan`
            3. Permethylated glycan - `ms_deisotope.permethylated_glycan`
            4. Glycopeptide - `ms_deisotope.glycopeptide`
            5. Sulfated Glycosaminoglycan - `ms_deisotope.heparan_sulfate`
            6. Unsulfated Glycosaminoglycan - `ms_deisotope.heparin`
        
        Deconvolution
        =============
        
        The general-purpose averagine-based deconvolution procedure can be called by using the high level
        API function `deconvolute_peaks`, which takes a sequence of peaks, an averagine model, and a isotopic
        goodness-of-fit scorer:
        
        .. code:: python
        
            import ms_deisotope
        
            deconvoluted_peaks, _ = ms_deisotope.deconvolute_peaks(peaks, averagine=ms_deisotope.peptide,
                                                                   scorer=ms_deisotope.MSDeconVFitter(10.))
        
        The result is a deisotoped and charge state deconvoluted peak list where each peak's neutral mass is known
        and the fitted charge state is recorded along with the isotopic peaks that gave rise to the fit.
        
        Refer to the documentation for a deeper description of isotopic pattern fitting.
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
