||40 (Research article)
||Teleman, Johan; Dowsey, Andrew; Gonzalez-Galarza, Faviel; Perkins, Simon; Pratt, Brian; Rost, Hannes; Malmström, Lars; Malmström, Johan; Jones, Andrew; Deutsch, Eric; Levander, Fredrik
||Numerical compression schemes for proteomics mass spectrometry data.
||Mol Cell Proteomics (2014) 13(6) 1537-42
||48 citations (journal impact: 7.25)
||The open XML format mzML used for representation of mass spectrometry 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 naive mzML representation is 4-fold or even up to 18-fold larger compared to the original vendor file. In disk IO 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.
||We describe a software implementation that uses the nature of MS data to achive high compression and fast read-speads.