Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles. Scientific contribution This pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries.

中文翻译：

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matchms 中可重复的 MS/MS 文库清洗管道


事实证明，质谱库对于质谱注释至关重要，无论是库匹配还是训练新的机器学习算法。训练机器学习模型的关键一步是高质量训练数据的可用性。向用户提交开放的质谱数据公共图书馆通常受到元数据管理和协调有限的困扰。由此产生的数据质量的可变性使得机器学习模型的训练具有挑战性。在这里，我们提出了一个库清理管道，旨在清理串联质谱库数据。该管道的设计以易用性、灵活性和可重复性为主要原则。科学贡献 该管道将产生更清洁的公共质谱库，从而改善质谱中的库搜索和机器学习训练数据集的质量。该管道建立在之前的工作基础上，通过验证结构注释添加了用于管理和更正注释库的新功能。由于我们软件的高质量、可重复性和改进的记录功能，我们认为我们的新管道有潜力成为清洁串联质谱库领域的标准。