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Open Access Open Badges Technical Note

Cloud-based solution to identify statistically significant MS peaks differentiating sample categories

Jun Ji14, Jeffrey Ling1, Helen Jiang1, Qiaojun Wen1, John C Whitin2, Lu Tian3, Harvey J Cohen2* and Xuefeng B Ling1*

Author Affiliations

1 Departments of Surgery, Stanford University, Stanford, CA 94305, USA

2 Departments of Pediatrics, Stanford University, Stanford, CA 94305, USA

3 Departments of Health Research & Policy, Stanford University, Stanford, CA 94305, USA

4 State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China

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BMC Research Notes 2013, 6:109  doi:10.1186/1756-0500-6-109

Published: 23 March 2013



Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). “Turnkey” solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation.


Here we present an efficient and effective solution, which provides experimental biologists easy access to “cloud” computing capabilities to analyze MS data. The web portal can be accessed at webcite.


Presented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS.