Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method.

Yan Zhang, Ranjeet Bhamber, Isabel Riba Garcia, Hanqing Liao, Richard D Unwin, Andrew W Dowsey

    Research output: Contribution to journalArticlepeer-review


    As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from
    Original languageEnglish
    Issue number8
    Publication statusPublished - Apr 2015


    • Bioinformatics
    • Mass spectrometry
    • Quality control
    • Signal decomposition
    • Visualization


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