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DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis

Author

Listed:
  • Yi Liu

    (Beijing University of Technology
    National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics)

  • Yun Yang

    (International Academy of Phronesis Medicine (Guang Dong), No. 96 Xindao Ring South Road, Guangzhou International Bio Island
    South China Institute of Biomedicine)

  • Wendong Chen

    (International Academy of Phronesis Medicine (Guang Dong), No. 96 Xindao Ring South Road, Guangzhou International Bio Island
    South China Institute of Biomedicine)

  • Feng Shen

    (Naval Medical University)

  • Linhai Xie

    (National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
    International Academy of Phronesis Medicine (Guang Dong), No. 96 Xindao Ring South Road, Guangzhou International Bio Island
    South China Institute of Biomedicine)

  • Yingying Zhang

    (National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
    Chongqing University of Posts and Telecommunications)

  • Yuanjun Zhai

    (National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics)

  • Fuchu He

    (National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
    International Academy of Phronesis Medicine (Guang Dong), No. 96 Xindao Ring South Road, Guangzhou International Bio Island
    Chinese Academy of Medical Sciences)

  • Yunping Zhu

    (National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics)

  • Cheng Chang

    (National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
    Chinese Academy of Medical Sciences)

Abstract

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

Suggested Citation

  • Yi Liu & Yun Yang & Wendong Chen & Feng Shen & Linhai Xie & Yingying Zhang & Yuanjun Zhai & Fuchu He & Yunping Zhu & Cheng Chang, 2023. "DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43909-5
    DOI: 10.1038/s41467-023-43909-5
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    References listed on IDEAS

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    1. Walid Mottawea & Cheng-Kang Chiang & Marcus Mühlbauer & Amanda E. Starr & James Butcher & Turki Abujamel & Shelley A. Deeke & Annette Brandel & Hu Zhou & Shadi Shokralla & Mehrdad Hajibabaei & Ruth Si, 2016. "Altered intestinal microbiota–host mitochondria crosstalk in new onset Crohn’s disease," Nature Communications, Nature, vol. 7(1), pages 1-14, December.
    2. Tami L. Swenson & Ulas Karaoz & Joel M. Swenson & Benjamin P. Bowen & Trent R. Northen, 2018. "Linking soil biology and chemistry in biological soil crust using isolate exometabolomics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    3. Matthew The & Lukas Käll, 2020. "Focus on the spectra that matter by clustering of quantification data in shotgun proteomics," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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