Data Streaming for Metabolomics: Accelerating Data Processing and Analysis from Days to Minutes
| Autor: | H. Paul Benton, Brian T. Abe, J. Rafael Montenegro-Burke, Luc Teyton, Mingliang Fang, Aries E. Aisporna, Julijana Ivanisevic, Duane Rinehart, Tao Huan, Luke L. Lairson, Erica M. Forsberg, Dennis W. Wolan, Benedikt Warth, Gary Siuzdak |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Complex data type Data processing Time Factors Chemistry T-Lymphocytes Real-time computing Data Compression Bottleneck Article Analytical Chemistry Workflow 03 medical and health sciences Data Compression/economics Data Compression/methods Data Mining/economics Data Mining/methods Humans Metabolomics/economics Metabolomics/methods Software T-Lymphocytes/metabolism 030104 developmental biology Metabolomics Server Data file Data Mining Throughput (business) Data transmission |
| Zdroj: | Analytical Chemistry Analytical chemistry, vol. 89, no. 2, pp. 1254-1259 |
| ISSN: | 1520-6882 0003-2700 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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