Abstract
Summary: The Python-based, open-source eMZed framework was developed for mass spectrometry (MS) users to create tailored workflows for liquid chromatography (LC)/MS data analysis. 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.Availability: The framework eMZed and its documentation are freely available at http://emzed.biol.ethz.ch/. eMZed is published under the GPL 3.0 license, and an online discussion group is available at https://groups.google.com/group/emzed-users.Contact: kiefer@micro.biol.ethz.chSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
liquid chromatography (LC)-mass spectrometry (MS) data analysis generally requires flexible software tools
2.1 Technical Aspects The eMZed framework is implemented in the Python programming language, which is well established in scientific computing (Oliphant, 2007) and bioinformatics in particular (Cock et al, 2009)
Compared to R and Matlab Python’s standard library is much more extensive and enables rapid application development by various means; e.g., Python supports easy access to online services such as PubChem or Metlin, which are of great interest for metabolomics data analyses
Summary
LC-MS data analysis generally requires flexible software tools. a number of solutions for specific or multiple applications currently exist, many of these belong to one of two extremes. The second group includes closed black-box solutions with graphical user interfaces that are easy to use but inherently nontransparent and inflexible, e.g., Maven (Melamud et al, 2010) and mzMine (Pluskal et al, 2010) Note that libraries such as the Rbased XCMS (Smith et al, 2006) or the Matlab-based Bioinformatics Toolbox (Mathworks, Natick, MA USA) lie between these extremes. The motivation to develop eMZed was to provide an open-source framework to establish transparent and flexible workflows for high-end data treatment that requires only basic programming skills of the user. To this end, we combined the power-
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