pyprophet 3.0.13

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

PyProphet is a Python re-implementation of the mProphet algorithm [1] optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in [2] and has since been extended to support new data types and analysis modes [3,4].

Please consult the OpenSWATH website for usage instructions and help.

Installation

Option 1: Python Package Index (PyPI)

Install the stable version of pyprophet from the PyPI:

    $ pip install pyprophet

Option 2: From Source

We strongly advise to install PyProphet in a Python virtualenv. PyProphet is compatible with Python 3.

Install the development version of pyprophet from GitHub:

    $ git clone https://github.com/pyprophet/pyprophet.git
    $ cd pyprophet  
    $ pip install . 

or

    $ pip install git+https://github.com/PyProphet/pyprophet.git@master

Option 3: Docker / Singularity

PyProphet is also available from Docker (automated builds):

Pull the latest version of pyprophet from DockerHub or Github Container Registry (synced with releases):

    # Dockerhub
    $ docker pull pyprophet/pyprophet:latest

    # Github Container Registry
    $ docker pull ghcr.io/pyprophet/pyprophet:latest

    # Singularity image
    $ singularity pull pyprophet.sif oras://ghcr.io/pyprophet/pyprophet-sif:latest

Running pyprophet

pyprophet is not only a Python package, but also a command line tool:

   $ pyprophet --help

or:

   $ pyprophet score --in=tests/test_data.txt

Documentation

API and CLI documentation is available on Read the Docs.

Running tests

The pyprophet tests are best executed using py.test and the pytest-regtest plugin:

    $ pip install pytest
    $ pip install pytest-regtest
    $ py.test -n auto ./tests

References

1. Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods. 2011 May;8(5):430-5. doi: 10.1038/nmeth.1584. Epub 2011 Mar 20.

2. Teleman J, Röst HL, Rosenberger G, Schmitt U, Malmström L, Malmström J, Levander F. DIANA--algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics. 2015 Feb 15;31(4):555-62. doi: 10.1093/bioinformatics/btu686. Epub 2014 Oct 27.

3. Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS. Nat Biotechnol 2017 Aug;35(8):781-788. doi: 10.1038/nbt.3908. Epub 2017 Jun 12.

4. Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat Methods. 2017 Sep;14(9):921-927. doi: 10.1038/nmeth.4398. Epub 2017 Aug 21.