OpenSlice: Quantitative data sharing from HyperPeaks to global ion chromatograms (GICs). Issue 18 (September 2016)

• Record Type: Journal Article
• Authors: Askenazi, Manor; Fenyö, David
• Other Names: Haynes Paul A. guestEditor.; Stein Stephen E. guestEditor.; Washburn Michael P. guestEditor.
• Is Part Of: Proteomics. Volume 16:Issue 18(2016)
• Page Start: 2495
• Page End: 2501
• Publication Date: 2016-09
• Subjects: Bioinformatics -- Databases -- Data mining -- Data processing and analysis; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201600096
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract : Data sharing in the field of MS has advanced greatly thanks to innovations such as the standardized formats, data repositories, and publications guidelines. However, there is currently no data sharing mechanism that enables real‐time data browsing and deep linking on a large scale: unrestricted data access (particularly at the quantitative level) ultimately requires the user to download a local copy of the relevant data files (e.g., in order to generate extracted ion chromatograms [XICs]). In this technical resource, we present a set of technologies (collectively termed OpenSlice) that enable the user to quantitatively query hundreds of hours of proteomics discovery data (i.e., nontargeted acquisition) in real time: the user is able to effectively generate XICs for arbitrary masses on the fly and across the entire dataset (so‐called global ion chromatograms), interacting with the results through a very intuitive browser‐based interface. A key design consideration underlying the OpenSlice approach is the notion that every aspect of the acquired data must be accessible through a RESTful uniform resource locator based application programming interface, up to and including individual chromatographic peaks (hence HyperPeaks). A publicly accessible demonstration of this technology based on the Clinical Proteomics Tumor Analysis Consortium CompRef dataset is made available athttp://compref.fenyolab.org .