The overarching goal of the Chorus project (http://chorusproject.org) is to advance biomedical research by extending our capabilities for the storage, dissemination, sharing, and analysis of the world's mass spectrometry data. To date, the mass spectrometry community has been storing their files in local ?silos? within the respective research laboratory. Each lab currently builds their own computational infrastructure for the analysis of their data. This process is inefficient and redundant. Mechanisms for labs to share and analyze data in a collaborative manner are almost non-existent. Furthermore, neither the vendor data formats, nor the established repositories make it feasible or efficiently to perform analyses between experiments and laboratories. We have developed a cloud infrastructure that facilitates the efficient storage, sharing, and visualization of mass spectrometry data across vendor platforms. We intend to build on this foundation for enabling the community to build tools to access and analyze individual datasets or the collective data as a whole. By bringing data into a shared cloud infrastructure, we improve the analyses that are possible, minimize the challenges with sharing large datasets, and reduce the overall costs. We will improve the value of all mass spectrometry data through standardization of tools, improved data accessibility, increased sharing, more efficient data access, and better communication. 1
Mass spectrometry is arguably the most significant technology for the characterization of biologically relevant molecules in the medical sciences. However, the sharing of mass spectrometric data is hindered by the large amount of data that is collected and the fact that different mass spectrometer manufacturers use different, incompatible ?raw? data formats. Chorus is solving these problems and helping mass spectrometry achieve its true potential. 1
| 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 |
| Pino, Lindsay K; Searle, Brian C; Bollinger, James G et al. (2017) The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev : |
