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

J Rafael Montenegro-Burke1, Aries E Aisporna1, H Paul Benton1

  • 1Scripps Center for Metabolomics, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States.

Analytical Chemistry
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Summary
This summary is machine-generated.

XCMS Stream accelerates omics data analysis by compressing and streaming data during acquisition. This platform reduces analysis time from days to minutes for large-scale metabolomic studies.

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Area of Science:

  • Metabolomics
  • Computational Biology
  • Analytical Chemistry

Background:

  • Omics technologies, particularly chromatography and mass spectrometry, have advanced rapidly.
  • Data analysis bottlenecks persist due to large, complex files and increasing sample numbers.
  • Current data transfer and processing speeds limit the analysis of large-scale omics studies.

Purpose of the Study:

  • To develop a data streaming platform, XCMS Stream, to overcome data analysis bottlenecks in omics research.
  • To reduce data analysis time for large sample sets in metabolomics.
  • To enhance the efficiency of downstream data processing and decision-making.

Main Methods:

  • Developed XCMS Stream, a data streaming platform leveraging acquisition time for compression and transfer.
  • Implemented just-in-time production strategies for data processing.
  • Applied XCMS Stream to T cell metabolism and large-scale metabolomic studies, including a 1000-sample dataset.

Main Results:

  • XCMS Stream demonstrated significant time savings, reducing analysis time for a 1000-sample dataset by 10,000-fold (days to minutes).
  • The platform effectively compresses and streams data during acquisition, minimizing transfer bottlenecks.
  • Enabled preliminary data decision-making before full data acquisition completion.

Conclusions:

  • XCMS Stream significantly accelerates omics data analysis, particularly for large-scale metabolomic studies.
  • The platform's efficiency enhances downstream processes like biochemical dependent data acquisition (BDDA).
  • XCMS Stream represents a novel approach to managing and analyzing high-throughput omics data effectively.