MS-DIAL is an open-source software pipeline designed to improve the identification and quantification of small molecules in metabolomics studies using data-independent acquisition (DIA) in liquid chromatography tandem mass spectrometry (LC-MS/MS). The software addresses the challenges of DIA by implementing a mathematical deconvolution algorithm that separates co-eluting compounds and noise ions, enhancing the accuracy and coverage of molecular data. The deconvolution process involves two steps: peak spotting and MS/MS-level deconvolution. Peak spotting identifies precursor ions based on retention time and accurate mass, while MS/MS-level deconvolution extracts and reconstructs the original spectra of individual compounds. The software supports various data formats and can handle large-scale analyses, making it suitable for comprehensive metabolome analysis. The effectiveness of MS-DIAL is demonstrated through its application in lipidomic analysis of nine algal species, where it identified 1,023 lipids and revealed chemotaxonomic relationships. The software also includes additional features such as peak alignment, filtering, and missing value interpolation, making it a powerful tool for untargeted metabolomics research.MS-DIAL is an open-source software pipeline designed to improve the identification and quantification of small molecules in metabolomics studies using data-independent acquisition (DIA) in liquid chromatography tandem mass spectrometry (LC-MS/MS). The software addresses the challenges of DIA by implementing a mathematical deconvolution algorithm that separates co-eluting compounds and noise ions, enhancing the accuracy and coverage of molecular data. The deconvolution process involves two steps: peak spotting and MS/MS-level deconvolution. Peak spotting identifies precursor ions based on retention time and accurate mass, while MS/MS-level deconvolution extracts and reconstructs the original spectra of individual compounds. The software supports various data formats and can handle large-scale analyses, making it suitable for comprehensive metabolome analysis. The effectiveness of MS-DIAL is demonstrated through its application in lipidomic analysis of nine algal species, where it identified 1,023 lipids and revealed chemotaxonomic relationships. The software also includes additional features such as peak alignment, filtering, and missing value interpolation, making it a powerful tool for untargeted metabolomics research.