ClearAIF: An R-Based Computational Pipeline for DIA Metabolomic Data Processing and Reporting

Background:

In a metabolomics core facility setting, supporting a diverse user base requires efficient and comprehensive MS/MS data acquisition while minimizing repeated sample injections. Data-Independent Acquisition (DIA, or All-ion fragmentation, AIF) provides an effective solution by capturing MS/MS data for all precursor ions in a single run, making it well-suited for untargeted metabolomics. While several AIF processing tools are available, opportunities remain to improve the accuracy of the MS/MS spectral deconvolution, the transparency in pseudospectrum reporting, and better integration with community-driven software ecosystems such as TidyMass. To address these needs, we developed a new computational pipeline, ClearAIF, to enhance the transparency, interpretability, and usability of DIA/AIF-derived data.

Methods:

We developed ClearAIF, an R-based, open-source, and modular computational pipeline to reconstruct high-quality pseudo-MS/MS spectra from DIA/AIF data. A distinctive feature of the workflow is a cascade spectral purity plot that transparently reports fragment assignment, which offers an intuitive visualization of precursor–fragment relationships, enabling users to directly assess fragment reliability. The pipeline is also compatible with the MetID package within TidyMass for metabolite annotation, allowing efficient integration into existing annotation workflows.

Results and Impact:

To maximize spectral information in DIA/AIF datasets, we optimized data acquisition using entropy-optimized collision energies. Using cancer cell lysates as test cases, this approach delivered broader metabolome coverage and reliable reconstruction of high-quality pseudo-MS/MS spectra. When applied to DDA datasets, ClearAIF improved the quality of MS/MS spectra, supporting highly credible metabolite annotation and robust library development. By improving the transparency and interpretability of AIF-derived spectra, ClearAIF facilitates marker validation and advances efforts in metabolite discovery, offering practical benefits for core facility workflows while providing a flexible and accessible solution for the broader metabolomics community.