Data Streaming for Metabolomics: Accelerating Data Processing and Analysis from Days to Minutes

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• 作者：J. Rafael Montenegro-BurkeAries E. Aisporna ; H. Paul Benton ; Duane Rinehart ; Mingliang Fang ; Tao Huan ; Benedikt Warth ; Erica ForsbergBrian T. Abe ; Julijana IvanisevicDennis W. Wolan ; Luc Teyton ; Luke Lairson ; Gary Siuzdak
• 刊名：Analytical Chemistry
• 出版年：2017
• 出版时间：January 17, 2017
• 年：2017
• 卷：89
• 期：2
• 页码：1254-1259
• 全文大小：461K
• ISSN：1520-6882

文摘

The speed and throughput of analytical platforms has been a driving force in recent years in the “omics” technologies and while great strides have been accomplished in both chromatography and mass spectrometry, data analysis times have not benefited at the same pace. Even though personal computers have become more powerful, data transfer times still represent a bottleneck in data processing because of the increasingly complex data files and studies with a greater number of samples. To meet the demand of analyzing hundreds to thousands of samples within a given experiment, we have developed a data streaming platform, XCMS Stream, which capitalizes on the acquisition time to compress and stream recently acquired data files to data processing servers, mimicking just-in-time production strategies from the manufacturing industry. The utility of this XCMS Online-based technology is demonstrated here in the analysis of T cell metabolism and other large-scale metabolomic studies. A large scale example on a 1000 sample data set demonstrated a 10 000-fold time savings, reducing data analysis time from days to minutes. Further, XCMS Stream has the capability to increase the efficiency of downstream biochemical dependent data acquisition (BDDA) analysis by initiating data conversion and data processing on subsets of data acquired, expanding its application beyond data transfer to smart preliminary data decision-making prior to full acquisition.