Numerical compression schemes for proteomics mass spectrometry data

Johan Teleman, Andrew W. Dowsey, Faviel F. Gonzalez-Galarza, Simon Perkins, Brian Pratt, Hannes L. Röst, Lars Malmström, Johan Malmström, Andrew R. Jones, Eric W. Deutsch*, Fredrik Levander

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

30 Citations (Scopus)

Abstract

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naïve mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.

Original languageEnglish
Pages (from-to)1537-1542
Number of pages6
JournalMolecular and Cellular Proteomics
Volume13
Issue number6
DOIs
Publication statusPublished - 2014

Structured keywords

  • Jean Golding

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