Advancements in data independent acquisition (DIA) mass spectrometry proteomics have led to a surge in software platforms/applications for the analysis of DIA datasets. Although this growth in software solutions has enabled improved proteome profiling across large clinical cohorts, a major challenge has emerged with differences in protein identification across applications and which one is optimal and closest to ground truth. Discrepancies in number and identity of proteins identified by each application when using the same dataset have left the clinical proteomics and broader proteomics communities with questions about the informatic strategies and what applications should be used in what settings. With a handful of platforms for DIA analysis leading the field, benchmarking and evaluation across applications has been undertaken by many groups, but a clear understanding of the strengths and limitations of each remains to be reached. Furthermore, variability in how DIA data is processed and the use of different bioinformatic platforms compounds the challenge of interpreting and comparing proteomics data in the context of patient/donor heterogeneity. Thus, advancements in DIA analysis require thorough evaluation and validation by the clinical proteomics community as well as improved efforts towards standardization that is amenable with continuous updates to reflect innovation in DIA workflows.
The aim of this Collection is to highlight DIA analysis strategies that are applicable to small and large clinical cohorts and to share findings of how different software platforms perform in the context of specimen type, study size and with respect to post-translational modification analysis. Additionally, a central focus of this Collection will be differences between library-based and library-free DIA strategies and how each of the bioinformatics platforms handles these analyses (particularly pertaining to false discovery rates and dependence on raw file/sample size). In an effort to address the many questions accompanying the advancements in DIA proteomics, the goal is to identify and invite submissions form groups that have and are actively benchmarking and evaluating these various DIA analysis tools across different cohorts (size and/or disease) and specimen types. Ideally, contributing authors will be representative of diverse backgrounds including academia, clinical institutions and industry as their work will provide a comprehensive overview of trends in the field.