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.
Getting started
Mass Spectrometry Concepts
OpenMS Algorithms
- Parameter Handling
- Algorithms
- Smoothing
- Centroiding
- Spectrum Normalization
- Spectra Merge Algorithm
- Charge and Isotope Deconvolution
- Feature Detection
- Feature Map Annotation with Peptide Identifications
- Map Alignment
- Adduct Detection
- Feature Linking
- Peptide Search
- Chromatographic Analysis
- Quality Control
- Mass Decomposition
- 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 Features with Identified Compounds
- Visualize Consensus Features with Identifications
- Untargeted Metabolomics Pre-Processing
Advanced Topics
- Logging
- Reading Raw MS Data
- Vendor Formats
- Other MS Data Formats
- mzML Files
- Scoring Spectra
- Export to pandas DataFrame
- Arrow and Parquet
- Mass Spectrometry Imaging
- Query MSExperiment with MassQL
- Memory Management
- Threads and Parallel Processing
- pyOpenMS in R
- Interactive Plots
- Interfacing with ML Libraries