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OpenMS

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Allows to manage and analyse Liquid chromatography coupled to mass spectrometry (LC-MS) data. OpenMS is a programming library and tool collection integrated into full-featured workflow systems, such as KNIME, Galaxy and WS-PGRADE, to facilitate bioinformatics research in the field of MS on all levels. The software provides pre-built and ready-to-use tools for analysis of both proteomics and non-targeted metabolomics data.

Ursgal

Performs complex multi-dimensional data analysis in terms of parameter variation. Ursgal is an unified interface that makes computational proteomics unified and scriptable, offering the possibility to exchange or extend any processing steps. This method allows the rapid development of novel workflows that require scriptable and unified access to mass spectrometry (MS) analysis tools. This type of analysis allow researchers to optimize their workflow and MS setup and thus potentially offer a deeper insight into their biological questions.

ms-data-core-api

A free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources.

eMZed

An open source framework for rapid and interactive development of LCMS data analysis workflows in Python. The goal was to establish a unique framework with comprehensive basic functionalities that are easy to apply and allow for the extension and modification of the framework in a straightforward manner. eMZed supports the iterative development and prototyping of individual evaluation strategies by providing a computing environment and tools for inspecting and modifying underlying LC/MS data. The framework specifically addresses non-expert programmers, as it requires only basic knowledge of Python and relies largely on existing successful open-source software, e.g. OpenMS.