With the maturation of proteomics technologies, these tools are being used to improve our understanding of many basic and clinical questions in human and model organism biology. To handle this, laboratories have shown that targeted mass spectrometry measurements offer a promising alternative to immunological based assays. Critical to these experiments is software to handle the generation of instrument methods and the consequent analysis of the resulting data. Our laboratory has developed a client software tool to handle these analyses called Skyline. In the last 8 years, Skyline has become one of the most widely software tools in proteomics and mass spectrometry. In this grant, we propose to continue the development and maintenance of Skyline and its associated software ecosystem, which currently supports 1000s of investigators in their basic science, pre-clinical, and translational research. Specifically, our proposal has five aims. 1) Expand our test infrastructure to improve the robustness, stress test the software, confirm compatibility across computer systems, and track performance over time. 2) Improve support for non-proteomics workflows. 3) Improve the collection, sharing, and dissemination of quantitative mass spectrometry data. 4) Modify Skyline to work in a cloud computing environment. 5) Provide continued support and training for the Skyline ecosystem.

Public Health Relevance

Mass spectrometry has been a fundamental technology for the analysis of proteins in health and disease. Targeted mass spectrometry measurements offer a promising alternative to immunological based assays that are the standard for quantitative protein measurements in clinical and basic research laboratories. Critical to these experiments is our software, Skyline and the associated ecosystem of tools, which have been developed to handle the generation of instrument methods and the subsequent analysis of the resulting data.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM103551-09
Application #
9773183
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Krepkiy, Dmitriy
Project Start
2011-09-14
Project End
2020-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
9
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Washington
Department
Genetics
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Sharma, Vagisha; Eckels, Josh; Schilling, Birgit et al. (2018) Panorama Public: A Public Repository for Quantitative Data Sets Processed in Skyline. Mol Cell Proteomics 17:1239-1244
MacLean, Brendan X; Pratt, Brian S; Egertson, Jarrett D et al. (2018) Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions. J Am Soc Mass Spectrom 29:2182-2188
Henderson, Clark M; Shulman, Nicholas J; MacLean, Brendan et al. (2018) Skyline Performs as Well as Vendor Software in the Quantitative Analysis of Serum 25-Hydroxy Vitamin D and Vitamin D Binding Globulin. Clin Chem 64:408-410
Pino, Lindsay K; Searle, Brian C; Bollinger, James G et al. (2017) The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev :
Bereman, Michael S; Beri, Joshua; Sharma, Vagisha et al. (2016) An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments. J Proteome Res 15:4763-4769
Whiteaker, Jeffrey R; Halusa, Goran N; Hoofnagle, Andrew N et al. (2016) Using the CPTAC Assay Portal to Identify and Implement Highly Characterized Targeted Proteomics Assays. Methods Mol Biol 1410:223-36
Bollinger, James G; Stergachis, Andrew B; Johnson, Richard S et al. (2016) Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards. Methods Mol Biol 1410:207-21
Kawahara, Rebeca; Bollinger, James G; Rivera, César et al. (2016) A targeted proteomic strategy for the measurement of oral cancer candidate biomarkers in human saliva. Proteomics 16:159-73
Egertson, Jarrett D; MacLean, Brendan; Johnson, Richard et al. (2015) Multiplexed peptide analysis using data-independent acquisition and Skyline. Nat Protoc 10:887-903
Abbatiello, Susan E; Schilling, Birgit; Mani, D R et al. (2015) Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma. Mol Cell Proteomics 14:2357-74

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