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Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m / z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision , Recall , F 1 - score , and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m / z error of 10 -4 and a relative intensity error of 2 × 10 -2 , adhering to a 5% error threshold ( F 1 - scores above 95%). For a stricter 1% error threshold ( F 1 - scores above 99%), an absolute m / z error of 2 × 10 -5 and a relative intensity error of 2 × 10 -3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

Introduction: Metabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification., Objectives: Develop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community., Methods: MetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference., Results: MetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples., Conclusion: We offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics., (© 2023. The Author(s).)

Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target–decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application. [ABSTRACT FROM AUTHOR]

Background: Liquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching. Results: To comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC–MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 Conclusions: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC–MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm. [ABSTRACT FROM AUTHOR]