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<p>The expansion of untargeted metabolomics has made publicly accessible spectral libraries indispensable for metabolite annotation and machine learning applications. Enhancing the quality and consistency of these libraries is crucial for improving the accuracy of metabolite identification and training machine learning models. However, public spectral libraries often suffer from variability in user submissions, unintentional errors, and a lack of standardization. Existing metadata cleaning and normalization tools typically exclude spectra with incorrect or unsupported metadata rather than attempting to correct them, resulting in the loss of valuable spectral data and associated metabolites details. This study introduces STRIKER (SpecTRal lIbrary maKER), a repair tool specifically designed to address adduct metadata deficiencies using a distance-based metric and a deep learning model. STRIKER leverages advanced similarity-based approaches to predict adducts in spectra lacking adduct metadata. It corrects adduct-related errors and standardizes adduct formatting using a deep learning model based on the multi-layer perceptron (MLP) algorithm. STRIKER achieved 95–99% correct adduct matching and 98% adduct correction accuracy. These corrections substantially reduce the number of missing or unusable spectra and metabolites, thereby enhancing the accuracy of metabolite identification and improving data quality for machine learning applications. The tool also facilitates a convenient construction of the Human Metabolome Database (HMDB) spectral library by integrating data files from the HMDB website. Furthermore, it enables users to extract customized sub libraries from larger libraries, supporting tailored analyses for specific research objectives with percised search space. STRIKER is an open-source, user-friendly Python graphical interface designed to be accessible to researchers with minimal bioinformatics expertise. Available at the following repository under an MIT license: <a href="https://striker-gui.sourceforge.io">https://striker-gui.sourceforge.io</a>.</p><p><b>Scientific contribution</b></p><p>The software is designed to preserve the maximum number of valid spectra in open mass spectral libraries, thereby supporting more comprehensive metabolite annotation in untargeted metabolomics. Its graphical user interface further facilitates the engagement of researchers without programming expertise, enabling them to enhance the quality and usability of spectral libraries.</p>

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STRIKER: a spectral metadata repairing tool for expanding the comprehensiveness of spectral libraries

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月27日

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  • Ahmed Karam,
  • Asmaa Ramzy,
  • Taghreed Khaled Abdelmoneim,
  • Maha Mokhtar,
  • Nada A. Youssef,
  • Aya Osama,
  • Nabila Sabar,
  • Sameh Magdeldin

摘要

The expansion of untargeted metabolomics has made publicly accessible spectral libraries indispensable for metabolite annotation and machine learning applications. Enhancing the quality and consistency of these libraries is crucial for improving the accuracy of metabolite identification and training machine learning models. However, public spectral libraries often suffer from variability in user submissions, unintentional errors, and a lack of standardization. Existing metadata cleaning and normalization tools typically exclude spectra with incorrect or unsupported metadata rather than attempting to correct them, resulting in the loss of valuable spectral data and associated metabolites details. This study introduces STRIKER (SpecTRal lIbrary maKER), a repair tool specifically designed to address adduct metadata deficiencies using a distance-based metric and a deep learning model. STRIKER leverages advanced similarity-based approaches to predict adducts in spectra lacking adduct metadata. It corrects adduct-related errors and standardizes adduct formatting using a deep learning model based on the multi-layer perceptron (MLP) algorithm. STRIKER achieved 95–99% correct adduct matching and 98% adduct correction accuracy. These corrections substantially reduce the number of missing or unusable spectra and metabolites, thereby enhancing the accuracy of metabolite identification and improving data quality for machine learning applications. The tool also facilitates a convenient construction of the Human Metabolome Database (HMDB) spectral library by integrating data files from the HMDB website. Furthermore, it enables users to extract customized sub libraries from larger libraries, supporting tailored analyses for specific research objectives with percised search space. STRIKER is an open-source, user-friendly Python graphical interface designed to be accessible to researchers with minimal bioinformatics expertise. Available at the following repository under an MIT license: https://striker-gui.sourceforge.io.

Scientific contribution

The software is designed to preserve the maximum number of valid spectra in open mass spectral libraries, thereby supporting more comprehensive metabolite annotation in untargeted metabolomics. Its graphical user interface further facilitates the engagement of researchers without programming expertise, enabling them to enhance the quality and usability of spectral libraries.