Evaluation of the False Discovery Rate in Library-Free Search by DIA-NN Using In Vitro Human Proteome

Recently, deep-learning-based in silico spectral libraries have gained increasing attention. Several data-independent acquisition (DIA) software tools have integrated this feature, known as a library-free search, thereby making DIA analysis more accessible. However, controlling the false discovery rate (FDR) is challenging owing to the vast amount of peptide information in in silico libraries. In this study, we introduced a stringent method to evaluate FDR control using DIA software. Recombinant proteins were synthesized from full-length human cDNA libraries and analyzed by using liquid chromatography-mass spectrometry and DIA software. The results were compared with known protein sequences to calculate the FDR. Notably, we compared the identification performance of DIA-NN versions 1.8.1, 1.9.2, and 2.1.0. Versions 1.9.2 and 2.10 identified more peptides than version 1.8.1, and versions 1.9.2 and 2.1.0 used a more conservative identification approach, thus significantly improving the FDR control. Across the synthesized recombinant protein mixtures, the average FDR at the precursor level was 0.538% for version 1.8.1, 0.389% for version 1.9.2, and 0.385% for version 2.1.0; at the protein level, the FDRs were 2.85%, 1.81%, and 1.81%, respectively. Collectively, our data set provides valuable insights for comparing FDR controls across DIA software and aiding bioinformaticians in enhancing their tools.