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Trackable and scalable LC-MS metabolomics data processing using asari

Author

Listed:
  • Shuzhao Li

    (Jackson Laboratory for Genomic Medicine
    University of Connecticut School of Medicine)

  • Amnah Siddiqa

    (Jackson Laboratory for Genomic Medicine)

  • Maheshwor Thapa

    (Jackson Laboratory for Genomic Medicine)

  • Yuanye Chi

    (Jackson Laboratory for Genomic Medicine)

  • Shujian Zheng

    (Jackson Laboratory for Genomic Medicine)

Abstract

Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.

Suggested Citation

  • Shuzhao Li & Amnah Siddiqa & Maheshwor Thapa & Yuanye Chi & Shujian Zheng, 2023. "Trackable and scalable LC-MS metabolomics data processing using asari," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39889-1
    DOI: 10.1038/s41467-023-39889-1
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    Cited by:

    1. Zhiqiang Pang & Lei Xu & Charles Viau & Yao Lu & Reza Salavati & Niladri Basu & Jianguo Xia, 2024. "MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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