Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method
Yan Zhang1, Ranjeet Bhamber, Isabel Riba-Garcia
1Centre for Endocrinology and Diabetes, Institute of Human Development, Faculty of Medical and Human Sciences, The University of Manchester, Manchester, UK; Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK.
Proteomics
|February 10, 2015
Summary
High-throughput liquid chromatography-mass spectrometry (LC-MS) data visualization is improved with seaMass signal decomposition. This technique enables efficient inspection of raw mass spectrometry (MS) data and analysis results for quality control.
Area of Science:
- Analytical Chemistry
- Bioinformatics
- Computational Biology
Background:
- High-throughput LC-MS data analysis presents challenges in visualization due to increasing data rates and complex data structures.
- Current methods for visualizing raw MS data and analysis results often involve impractical processing and memory overheads, hindering interactive use.
- Data-dependent acquisition in MS/MS spectra can lead to missing quantification data, complicating visualization and interpretation.
Purpose of the Study:
- To develop efficient visualization tools for high-throughput LC-MS data to improve accessibility for practitioners.
- To enable effective quality control, verification, validation, interpretation, and sharing of raw MS data and analysis results.
- To address the limitations of current MS data visualization techniques, particularly concerning interactive use and handling of incomplete quantification data.
Main Methods:
- Leveraging the seaMass technique for novel signal decomposition of LC-MS data.
- Modeling LC-MS data as a 2D surface using a sparse set of weighted B-spline basis functions.
- Implementing an R-tree data model for ordering and spatially partitioning weights to achieve efficient streaming visualizations.
Main Results:
- Development of a core MS1 visualization engine capable of handling large LC-MS datasets.
- Successful overlay of MS/MS annotations onto the visualized data.
- Demonstration of the tool's utility for rapid inspection of chromatographic issues, MS/MS precursor coverage, and potential biomarker interferences.
Conclusions:
- The seaMass visualization engine provides an efficient and accessible solution for inspecting high-throughput LC-MS data.
- The developed tool facilitates critical quality control and data interpretation steps in MS analysis.
- Open-source availability of the software promotes wider adoption and advancement in the field.
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