The implemented feature detection algorithm SAFD (Section S1.2) has the main advantage that it can perform feature detection on both profile and centroided data. Where the former, even though computationally more expensive, can avoid false peaks introduced by centroiding or other steps and work directly with the raw MS1 data. If preferred, the profile data can also be centroided using the SAFD package and then perform feature detection. As for the componentization step, CompCreate (Section S1.3) is implemented, which is able to componentize both DDA and DIA data, including PRM, SWATH, and multi-collision analysis. Moreover, the algorithm reports all grouped information (i.e., (in-source) fragments, isotopes, and adducts) in the output, providing full traceability of grouped MS1 features instead of removing this information from the feature list. Moreover, CompCreate uses the parameter free naive Bayes isotope detection model and cumulative neutral loss model for fragment deconvolution. Since a wide variety of data types can be analyzed with these algorithms, consistent outputs can be obtained for further analysis.