AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.