Julianus Pfeuffer1, Timo Sachsenberg2, Oliver Alka2, Mathias Walzer3, Alexander Fillbrunn4, Lars Nilse5, Oliver Schilling5, Knut Reinert6, Oliver Kohlbacher7. 1. Applied Bioinformatics, Department for Computer Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; Center for Bioinformatics, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany. Electronic address: pfeuffer@informatik.uni-tuebingen.de. 2. Applied Bioinformatics, Department for Computer Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; Center for Bioinformatics, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany. 3. Applied Bioinformatics, Department for Computer Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; Center for Bioinformatics, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom. 4. Chair for Bioinformatics and Information Mining, Department of Computer and Information Science, University of Konstanz, Germany. 5. Institute of Molecular Medicine and Cell Research, Freiburg University, Stefan-Meier-Str. 17, 79104 Freiburg, Germany. 6. Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany. 7. Applied Bioinformatics, Department for Computer Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; Center for Bioinformatics, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; Center for Quantitative Biology, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany; Biomolecular Interactions, Max Planck Institute for Developmental Biology, Spemannstr. 35, 72076 Tuebingen, Germany. Electronic address: kohlbacher@informatik.uni-tuebingen.de.
Abstract
BACKGROUND: In recent years, several mass spectrometry-based omics technologies emerged to investigate qualitative and quantitative changes within thousands of biologically active components such as proteins, lipids and metabolites. The research enabled through these methods potentially contributes to the diagnosis and pathophysiology of human diseases as well as to the clarification of structures and interactions between biomolecules. Simultaneously, technological advances in the field of mass spectrometry leading to an ever increasing amount of data, demand high standards in efficiency, accuracy and reproducibility of potential analysis software. RESULTS: This article presents the current state and ongoing developments in OpenMS, a versatile open-source framework aimed at enabling reproducible analyses of high-throughput mass spectrometry data. It provides implementations of frequently occurring processing operations on MS data through a clean application programming interface in C++ and Python. A collection of 185 tools and ready-made workflows for typical MS-based experiments enable convenient analyses for non-developers and facilitate reproducible research without losing flexibility. CONCLUSIONS: OpenMS will continue to increase its ease of use for developers as well as users with improved continuous integration/deployment strategies, regular trainings with updated training materials and multiple sources of support. The active developer community ensures the incorporation of new features to support state of the art research.
BACKGROUND: In recent years, several mass spectrometry-based omics technologies emerged to investigate qualitative and quantitative changes within thousands of biologically active components such as proteins, lipids and metabolites. The research enabled through these methods potentially contributes to the diagnosis and pathophysiology of human diseases as well as to the clarification of structures and interactions between biomolecules. Simultaneously, technological advances in the field of mass spectrometry leading to an ever increasing amount of data, demand high standards in efficiency, accuracy and reproducibility of potential analysis software. RESULTS: This article presents the current state and ongoing developments in OpenMS, a versatile open-source framework aimed at enabling reproducible analyses of high-throughput mass spectrometry data. It provides implementations of frequently occurring processing operations on MS data through a clean application programming interface in C++ and Python. A collection of 185 tools and ready-made workflows for typical MS-based experiments enable convenient analyses for non-developers and facilitate reproducible research without losing flexibility. CONCLUSIONS: OpenMS will continue to increase its ease of use for developers as well as users with improved continuous integration/deployment strategies, regular trainings with updated training materials and multiple sources of support. The active developer community ensures the incorporation of new features to support state of the art research.
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