User guide

This guide is for users that want to use pyopenms to develop and/or prototype scripts and programs for mass spectrometry data analysis and visualization.

You can find the full table of contents in the following. On every page, you can also navigate this table by using the left sidebar. The right sidebar shows the page’s headings and structure.

• Introduction
• Why use OpenMS
• Liquid chromatography (LC)
  • Key components of LC
  • How does LC work?
  • High Performance Liquid Chromatography (HPLC)
• Mass Spectrometry (MS)
  • Key components of MS
  • Identifying Molecules with tandem mass spectrometry (MS2)
• Identification and Quantification of Ions
  • Labeling
  • Label-free quantification (LFQ)

Getting started

• Installation
  • Try online
  • Command Line
  • Nightly/ CI wheels
  • Source (advanced users)
  • Wrap Classes (advanced users)
• Import pyOpenMS
• Using the Help Function
• First Look at Data
  • File Reading
  • Iteration
  • Total Ion Current Calculation
  • Total Ion Current Chromatogram

Mass Spectrometry Concepts

• MS Data
  • Spectrum
  • Chromatogram
  • LC-MS/MS Experiment
  • Example: Precursor Purity
  • Example: Filtering Mass Spectra
• Chemistry
  • Constants
  • Elements
  • Molecular Formulas
  • Amino Acids
  • Amino Acid Modifications
  • Ribonucleotides
• Peptides and Proteins
  • Amino Acid Sequences
  • Modified Sequences
  • Applying Fixed or Variable Modifications to Sequences
  • Proteins and FASTA Files
• Oligonucleotides: RNA
  • Nucleic Acid Sequences
  • Modified Oligonucleotides
  • DNA, RNA and Protein
• Fragment Spectrum Generation
  • Y-ion Mass Spectrum
  • Full Fragment Ion Mass Spectrum
  • Visualization
• Spectrum Alignment
• Digestion
  • Proteolytic Digestion with Trypsin
  • Proteolytic Digestion with Lys-C
  • Oligonucleotide Digestion
• Identification Data
  • Protein Identification
  • PeptideIdentification
  • Storage on Disk
• Quantitative Data
  • features
  • Feature Maps
  • Consensus Features
  • Consensus Maps

OpenMS Algorithms

• Parameter Handling
• Algorithms
• Smoothing
• Centroiding
• Spectrum Normalization
  • Loading the Raw Data
  • Normalization Procedure
  • TIC Normalization
• Spectra Merge Algorithm
  • Loading the Raw Data
  • Block wise spectra merging
  • MS2 spectra merging with precursor method
  • Spectra averaging : gaussian and top hat methods
• Charge and Isotope Deconvolution
  • Single Peak Example
  • Full Spectral De-Isotoping
  • Visualization
• Feature Detection
  • Proteomics
  • Metabolomics - Untargeted
  • Metabolomics - Targeted
• Map Alignment
  • Download Example Data
  • Map Alignment Algorithm
  • Visualization
• Adduct Detection
• Feature Linking
  • Download Example Data
  • features Linking Algorithm
• Peptide Search
  • SimpleSearch
  • Peptide-Spectrum Match (PSM) Inspection
  • Visualization
  • More detailed example
• Chromatographic Analysis
  • Peak Detection
  • Visualization
  • Smoothing
• Quality Control
• Mass Decomposition
  • Fragment Mass to Amino Acid Composition
  • Naive Algorithm
  • Stand-Alone Program
  • Spectrum Tagger
• Export Files for GNPS

Example Workflows

• Identification by Accurate Mass
  • Imports and mzML file path
  • Download Example Data
  • Centroiding
  • Feature Detection
  • Feature Map Retention Time Alignment
  • Visualization of RTs before and after Alignment
  • Feature Linking
  • ConsensusMap to Pandas DataFrame
  • Accurate Mass Search
  • Data Filtering and Imputation
  • Annotate :term:Features<features>` with Identified Compounds
  • Visualize Consensus Features with Identifications
• Untargeted Metabolomics Pre-Processing

Advanced Topics

• Logging
• 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)
• mzML Files
  • Binary Encoding
  • Base64 Encoding
  • Numpress Encoding
• Scoring Spectra
  • Background
  • Generate a Theoretical Spectrum
  • Getting a Score
• Export to pandas DataFrame
  • MSExperiment
  • PeptideIdentification
  • FeatureMap
  • ConsensusMap
• Query MSExperiment with MassQL
• Memory Management
• pyOpenMS in R
  • Install the “reticulate” R Package
  • Import pyOpenMS in R
  • Getting help
  • An example use case
• Interactive Plots
• Interfacing with ML Libraries
  • Overview
  • Preprocessing
  • Modelling

Support

• Support
  • Feature Requests
  • Troubleshooting
• Frequently Asked Questions
  • How can I wrap a new method with pyOpenMS?
  • How can I wrap a new class with pyOpenMS?
  • Can I use multiple output parameters?
• OpenMS Glossary

Indices and Tables

• Index