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imzML: Imaging Mass Spectrometry Markup Language: A Common Data Format for Mass Spectrometry Imaging

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Data Mining in Proteomics

Abstract

Imaging mass spectrometry is the method of scanning a sample of interest and generating an “image” of the intensity distribution of a specific analyte. The data sets consist of a large number of mass spectra which are usually acquired with identical settings. Existing data formats are not sufficient to describe an MS imaging experiment completely. The data format imzML was developed to allow the flexible and efficient exchange of MS imaging data between different instruments and data analysis software.

For this purpose, the MS imaging data is divided in two separate files. The mass spectral data is stored in a binary file to ensure efficient storage. All metadata (e.g., instrumental parameters, sample details) are stored in an XML file which is based on the standard data format mzML developed by HUPO-PSI. The original mzML controlled vocabulary was extended to include specific parameters of imaging mass spectrometry (such as x/y position and spatial resolution). The two files (XML and binary) are connected by offset values in the XML file and are unambiguously linked by a universally unique identifier. The resulting datasets are comparable in size to the raw data and the separate metadata file allows flexible handling of large datasets.

Several imaging MS software tools already support imzML. This allows choosing from a (growing) number of processing tools. One is no longer limited to proprietary software, but is able to use the processing software which is best suited for a specific question or application. On the other hand, measurements from different instruments can be compared within one software application using identical settings for data processing. All necessary information for evaluating and implementing imzML can be found at http://www.imzML.org.

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Römpp, A. et al. (2011). imzML: Imaging Mass Spectrometry Markup Language: A Common Data Format for Mass Spectrometry Imaging. In: Hamacher, M., Eisenacher, M., Stephan, C. (eds) Data Mining in Proteomics. Methods in Molecular Biology, vol 696. Humana Press. https://doi.org/10.1007/978-1-60761-987-1_12

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  • DOI: https://doi.org/10.1007/978-1-60761-987-1_12

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-60761-986-4

  • Online ISBN: 978-1-60761-987-1

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