Comparative evaluation of proteomics software suites for DDA and DIA acquisition strategies

Mass spectrometry (MS) is a very powerful approach for proteome analysis, offering different instruments that can acquire raw data in different acquisition modes. In addition, an increasing palette of software tools have become available for the analysis of MS data, making the selection of suitable methods a complex decision. Here, we provide a comparative evaluation of six current software suites, namely FragPipe, MaxQuant, PEAKS Studio, DIA-NN, MaxDIA and Spectronaut for data acquired on three different mass spectrometers: a QExactive-HF, Exploris480 and a timsTOF Pro. To assess the software in a fair and systematic manner, we investigated their performances analyzing the same data of a human proteome sample spiked with different amounts of E.coli proteins, which we generated on each instrument by both DDA and DIA. We evaluated the capabilities of each software by the attained proteome coverage, we determined false-identifications and focused specifically on protein quantification to give practical advice to the end-user. Notably, we found that performance of software differed profoundly across instruments, which calls for certain software to be used only in combination with specific instruments, while indicating that tools may be optimized for more universal application. Moreover, in an unconventional approach, we linked the magnitudes of the detected proteome range to their respective quantitative accuracy, allowing filtering based on data quality before making biological inferences. When comparing software tools across acquisition modes, we observed that among the tested DIA suites only results from DIA-NN aligned to DDA-derived data, making DIA-NN particularly powerful for integration of data sets. We further noticed that DDA software handled our entrapment approach much better than the DIA tools, leading to fewer false-identifications, and we therefore explored ways to tighten the existing filters, thus suggesting new standards for the analysis of DIA data. Beyond serving as a comprehensive reference for state-of-the-art software capabilities, we envision this work to constitute a valuable resource to peer researchers, encouraging adaptation and refinement of data analysis in the proteomics community.