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.
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.
J Biotechnol. 2017 Nov 10;261:142-148. doi: 10.1016/j.jbiotec.2017.05.016. Epub 2017 May 27.
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.
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.
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.
近年来，出现了几种基于质谱的组学技术，可用于研究蛋白质、脂质和代谢物等数千种生物活性成分的定性和定量变化。这些方法所促成的研究有助于人类疾病的诊断和发病机制，以及阐明生物分子之间的结构和相互作用。同时，质谱领域的技术进步导致数据量不断增加，这就要求潜在分析软件在效率、准确性和重现性方面达到高标准。
本文介绍了 OpenMS 的现状和正在进行的开发，OpenMS 是一个通用的开源框架，旨在实现高通量质谱数据的可重复分析。它通过 C++ 和 Python 中的干净应用程序编程接口实现了对 MS 数据的常见处理操作。185 个工具和用于典型 MS 实验的预制工作流程的集合，为非开发人员提供了方便的分析，同时在不丧失灵活性的情况下促进了可重复的研究。
OpenMS 将通过改进的持续集成/部署策略、使用更新的培训材料进行定期培训以及提供多种支持来源，继续提高开发人员和用户的易用性。活跃的开发人员社区确保了新功能的加入，以支持最先进的研究。
