Journal
PROTEOMICS
Volume 15, Issue 8, Pages 1419-1427Publisher
WILEY
DOI: 10.1002/pmic.201400428
Keywords
Bioinformatics; Mass spectrometry; Quality control; Signal decomposition; Visualization
Funding
- EPSRC [EP/E03988X/1]
- BBSRC [BB/K016733/1]
- BBSRC [BB/K016733/1, BB/M024954/1] Funding Source: UKRI
- EPSRC [EP/E03988X/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/M024954/1, BB/K016733/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/E03988X/1] Funding Source: researchfish
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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 ismodelled 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 http://seamass.net/viz/.
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