Summary

pyOpenMS are the Python bindings to the OpenMS library which are available for Windows, Linux and OSX.

The pyOpenMS package contains Python bindings for a large part of the OpenMS library (http://www.open-ms.de) for mass spectrometry based proteomics. It thus provides facile access to a feature-rich, open-source algorithm library for mass-spectrometry based proteomics analysis. These Python bindings allow raw access to the data-structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ and SWATH analysis tools).

Please see the appendix of the official pyOpenMS Manual for a complete documentation of the pyOpenMS API and all wrapped classes.

Note: the current documentation relates to the 2.4.0 release of pyOpenMS.

Installation

• pyOpenMS Installation
  • Binaries
  • Source
  • Wrap Classes

First steps

• Getting Started
  • Import pyopenms
  • Getting help
  • First look at data
• Reading Raw MS data
  • mzML files in memory
  • indexed mzML files
  • mzML files as streams
  • cached mzML files
• Other MS data formats
  • Identification data (idXML, mzIdentML, pepXML, protXML)
  • Quantiative data (featureXML, consensusXML)
  • Transition data (TraML)

Mass Spectrometry Concepts:

• MS Data
  • Spectrum
  • LC-MS/MS Experiment
  • Chromatogram
• Chemistry
  • Elements
  • Molecular Formula
  • Isotopic Distributions
  • Amino Acid Residue
  • Amino Acid Modifications
• Peptides and Proteins
  • Amino Acid Sequences
  • Modified Sequences
  • Proteins
  • TheoreticalSpectrumGenerator
• Digestion
  • Proteolytic Digestion with Trypsin
  • Proteolytic Digestion with Lys-C
• Identification Data
  • ProteinIdentification
  • PeptideIdentification
  • Storage on disk
• Quantitative Data
  • Feature
  • FeatureMap
  • ConsensusFeature
  • ConsensusMap

Simple Data Manipulation:

• Simple Data Manipulation
  • Filtering Spectra
  • Filtering Spectra and Peaks
  • Memory management

OpenMS Algorithms:

• Parameter Handling
• Algorithms
• Feature Detection

Developers

• Build from source
  • Further questions
• Wrapping Workflow and wrapping new Classes
  • How pyOpenMS wraps Python classes
  • How to wrap new methods in existing classes
  • How to wrap new classes

Indices and tables

• Index