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The utility of this Strezming Online-based technology is demonstrated here in the analysis of Annal cell metabolism Anql other large-scale metabolomic studies. A large scale example on a Anal streaming data set demonstrated a 10 fold time savings, reducing data analysis time from days to minutes. Consequently, waiting until a full data set has been acquired to proceed with data analysis is usually the common practice. For instance, the conversion of 1 GB raw LC-MS data file from a proprietary format to an open format used by data processing and data analysis bioinformatic tools can take up to 30 min for dedicated multicore servers using software such as ProteoWizard.

Highly complex multidimensional data and an increase in data file sizes have spurred the transition from instrumentation computer workstations to both on-site servers and workstations for data analysis.

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Even though personal computers have become Anal streaming 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. 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. Another advantage of a streaming platform is coupling data processing and analysis capabilities.

Furthermore, data file conversion is a heavy computational task, which ultimately slows down personal computers. However, these time limitations can be overcome by simultaneously compressing and streaming data files to processing servers while data acquisition LC-MS is underway.

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This streaning requires the manual transfer of data files via portable media e. Data streaming has been adopted by mobile device applications especially in the entertainment and electronic gaming industries; however, its broader application to science has been largely overlooked. Nevertheless, the ability to survey recently acquired discrete data packets from large-scale data sets pertaining to LC-MS, NMR, or omic data in general can be limited by the location of the generated data files because of the use of centralized data analysis servers and in recent years, the more common situation of sharing laboratory equipment in interdisciplinary research.

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.


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