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| OpenMS | |
|---|---|
| Name | OpenMS |
| Developer | The OpenMS Team |
| Released | 2007 |
| Programming language | C++ |
| Operating system | Windows, macOS, Linux |
| Genre | Bioinformatics, Proteomics, Metabolomics |
| License | BSD-style |
OpenMS is an open-source software framework for processing mass spectrometry data, designed for proteomics and metabolomics workflows. It provides libraries, tools, and graphical applications to enable algorithm development, pipeline execution, and reproducible analysis for experimental workflows derived from mass spectrometers manufactured by companies such as Thermo Fisher Scientific, Agilent Technologies, and Bruker. The project integrates with scientific ecosystems spanning institutions like the European Bioinformatics Institute, Max Planck Society, and University of Washington.
OpenMS is a modular platform combining high-performance C++ libraries and Python bindings that support data conversion, peak detection, feature finding, quantification, identification, and statistical evaluation. It interfaces with standards and formats originating from organizations such as the Human Proteome Organization, ProteomeXchange Consortium, and European Molecular Biology Laboratory, enabling interoperability with tools developed at Stanford University, Harvard University, ETH Zurich, and University of Oxford. The framework supports algorithmic research linking to initiatives at National Institutes of Health, European Commission, and regional infrastructures including ELIXIR and de.NBI.
Development began in the mid-2000s within academic networks that included groups from University of Tübingen, Friedrich Schiller University Jena, and collaborators at European Molecular Biology Laboratory. Early funding sources included projects under the European Union and national science foundations such as the German Research Foundation. Contributors have included researchers formerly associated with Max Planck Institute for Informatics, University of California, San Francisco, University of Manchester, and industrial partners like SCIEX and Waters Corporation. Over time, the codebase evolved through collaborations with communities behind projects such as Trans-Proteomic Pipeline, Skyline, MetFrag, and MS-DIAL.
The architecture centers on reusable C++ libraries, command-line utilities, and graphical clients that communicate via standardized file formats promoted by the Proteomics Standards Initiative and coordinated with repositories like PRIDE and MassIVE. Core components include data structures for spectra, chromatograms, and feature maps; algorithm modules for deconvolution and deisotoping; and pipeline systems for batch processing used by groups at Leiden University Medical Center and Karolinska Institutet. Integration adapters facilitate connections to workflow engines such as Galaxy, Nextflow, and Snakemake and to continuous integration platforms employed at institutions like GitHub and GitLab.
OpenMS provides feature detection algorithms focused on high-resolution mass spectrometers from manufacturers such as Thermo Fisher Scientific and Bruker, statistical modules compatible with packages from Bioconductor and R Project for Statistical Computing, and visualization through GUI applications influenced by design patterns from Qt Project. Tools include utilities for retention time alignment, label-free quantification, isobaric labeling support (TMT, iTRAQ), and targeted workflows comparable to Skyline approaches used in clinical research at Mayo Clinic and Johns Hopkins University. The suite supports crosslinking analysis methods akin to work published by teams at EMBL-EBI and structural proteomics efforts at European Synchrotron Radiation Facility.
The framework implements and converts between open standards from groups such as the Proteomics Standards Initiative and repositories like PRIDE Archive and MetaboLights; supported formats include XML-based and mzML-derived representations used by vendors including Waters Corporation and SCIEX. OpenMS interoperates with identification and quantification formats championed by projects at European Bioinformatics Institute and supports annotation schemes referenced in standards committees hosted by HUPO and workflows aligned with guidelines from Clinical Proteomic Tumor Analysis Consortium.
Researchers at universities and biotech companies use OpenMS for shotgun proteomics, targeted proteomics, and metabolomics workflows in studies spanning biomarker discovery, clinical proteogenomics consortia, pharmaceutical development at firms like Pfizer and Novartis, and environmental metabolomics collaborations with agencies such as European Environment Agency. Applications include large-scale cohort studies coordinated with initiatives like The Cancer Genome Atlas and translational research projects linked to Wellcome Trust funding, as well as method development in labs associated with Cold Spring Harbor Laboratory and Scripps Research.
The project follows a permissive BSD-style license encouraging adoption by academic groups such as ETH Zurich and commercial partners including Thermo Fisher Scientific. Community governance has involved steering committees with members from Max Planck Society, University of Tübingen, and funders associated with the European Union Horizon 2020 program. Support mechanisms include mailing lists, developer forums integrated with GitHub, training events at workshops hosted by EMBO and conferences like ASMS and EuBIC, and collaborative development with infrastructure initiatives such as ELIXIR.
Category:Bioinformatics software