├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Röst Lab 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PythonProteomics 2 | This repository contains a list of open source Python tools for Proteomics analysis. The list is very likely incomplete and we are happy to take pull request with new tools. 3 | 4 | # Contents 5 | 6 | * [Introduction](#intro) 7 | * Packages 8 | * Full Libraries 9 | * [pyOpenMS](#pyOpenMS) 10 | * [pyteomics](#pyteomics) 11 | * [multiplierz](#multiplierz) 12 | * [pyproteome](#pyproteome) 13 | * Specialized packages 14 | * [pyMzML](#pyMzML) 15 | * [pyProphet](#pyProphet) 16 | * [msproteomicstools](#msproteomicstools) 17 | * [PaDuA](#PaDuA) 18 | * [psims](#psims) 19 | * [param-medic](#param-medic) 20 | 21 | 22 | # Intro 23 | 24 | Python is a versatile scripting language that is widely used in industry and academia. In bioinformatics, there are multiple packages supporting data analysis with Python that range from biological sequence analysis with Biopython to structural modeling and visualization with packages like PyMOL and PyRosetta, to numerical computation and advanced plotting with NumPy/SciPy. In the proteomics community, Python began to be widely used around 2012 when several mature Python packages were published including pymzML, Pyteomics and pyOpenMS. This has led to an ever-increasing interest in the Python programming language in the proteomics and mass spectrometry community. The number of publications referencing or using Python has risen eight fold since 2012 (compared with the same time period before 2012), with multiple open-source Python packages now supporting mass spectrometric data analysis and processing. Computing and data analysis in mass spectrometry is very diverse and in many cases must be tailored to a specific experiment. Often, multiple analysis steps have to be performed (identification, quantification, post-translational modification analysis, filtering, FDR analysis etc.) in an analysis pipeline, which requires high flexibility in the analysis. This is where Python truly shines, due to its flexibility, visualization capabilities and the ability to extend computation with a large number of powerful libraries. Python can be used to quickly prototype software, combine existing libraries into powerful analysis workflows while avoiding the trap of re-inventing the wheel for a new project. 25 | 26 | # Libraries 27 | 28 | ## Full libraries 29 | 30 | These packages contain full-fledged Python libraries that can process Proteomics data 31 | 32 | 33 | ### pyOpenMS 34 | 35 | [pyOpenMS](https://pyopenms.readthedocs.io/en/latest/) is an open-source Python library for mass spectrometry, specifically for the analysis of proteomics and metabolomics data in Python. pyOpenMS implements a set of Python bindings to the OpenMS library for computational mass spectrometry and is available for Windows, Linux and OSX. 36 | 37 | * Code: https://github.com/OpenMS/OpenMS 38 | * Publication: https://www.ncbi.nlm.nih.gov/pubmed/24420968 39 | 40 | 41 | ### pyteomics 42 | 43 | [Pyteomics](https://pyteomics.readthedocs.io/en/latest/) is a collection of lightweight and handy tools for Python that help to handle various sorts of proteomics data. Pyteomics provides a growing set of modules to facilitate the most common tasks in proteomics data analysis, such as: 44 | 45 | * Code: https://bitbucket.org/levitsky/pyteomics/src/default/ 46 | * Publication: https://www.ncbi.nlm.nih.gov/pubmed/30576148 47 | 48 | 49 | ### multiplierz 50 | [multiplierz](https://github.com/BlaisProteomics/multiplierz) is a Python software library and associated GUI desktop environment for managing proteomic mass spectrometry workflows and data analysis. Using the mzAPI interface to native instrument data formats, multiplierz is provides a complete toolset for a variety of methods for peptide identification, quantitation, and experimental reporting. 51 | 52 | * Code: https://github.com/BlaisProteomics/multiplierz 53 | * Publication: https://www.ncbi.nlm.nih.gov/pubmed/28686798 54 | 55 | 56 | ### pyproteome 57 | 58 | [pyproteome](https://github.com/white-lab/pyproteome): Python library for analyzing mass spectrometry proteomics data. 59 | 60 | * Code: https://github.com/white-lab/pyproteome 61 | 62 | ## Specialized packages 63 | 64 | These packages contain specialized functions that help with a specific aspect of Proteomics data processing 65 | 66 | 67 | ### pyMzML 68 | 69 | [pyMzML](https://pymzml.readthedocs.io/en/latest/): Module to parse mzML data in Python based on cElementTree. 70 | 71 | * Code: https://github.com/pymzml/pymzML 72 | * Publication: https://www.ncbi.nlm.nih.gov/pubmed/29394323 73 | 74 | 75 | ### pyProphet 76 | 77 | [PyProphet](https://github.com/PyProphet/pyprophet): Semi-supervised learning and scoring of OpenSWATH results. 78 | 79 | * Code: https://github.com/PyProphet/pyprophet 80 | 81 | 82 | ### msproteomicstools 83 | 84 | [msproteomicstools](http://msproteomicstools.roestlab.org/): is a Python library that can be used in LC-MS/MS based proteomics. It features a core library called msproteomicstoolslib and several associated executable scripts that use the library as well as a GUI for visualizing chromatograms, specifically output from OpenSWATH. 85 | 86 | * Code: https://github.com/msproteomicstools/msproteomicstools 87 | 88 | 89 | ### PaDuA 90 | [PaDuA](https://padua.readthedocs.io/en/latest/) is a Python package to simplify the processing and analysis of quantified proteomics data. Currently it supports processing and analysis of MaxQuant outputs, providing many of the features available in the GUI analysis tool Perseus. By scripting these processing and analysis steps you can get to your results more quickle and reproducibly. 91 | 92 | * Code: https://github.com/mfitzp/padua 93 | * Publication: https://www.ncbi.nlm.nih.gov/pubmed/30525654 94 | 95 | 96 | ### psims 97 | 98 | [psims](https://mobiusklein.github.io/psims/docs/build/html/): A declarative API for writing XML documents for HUPO PSI-MS mzML and mzIdentML 99 | 100 | * Code: https://mobiusklein.github.io/psims/ 101 | * Publication: https://www.ncbi.nlm.nih.gov/pubmed/30563850 102 | 103 | 104 | ### param-medic 105 | 106 | [param-medic](https://github.com/dhmay/param-medic): Param-Medic breathes new life into MS/MS database searches by optimizing search parameter settings for your data. 107 | 108 | * Code: https://github.com/dhmay/param-medic 109 | * Publication: https://www.ncbi.nlm.nih.gov/pubmed/30714740 110 | --------------------------------------------------------------------------------