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

Journal: Journal of proteome research
Published Date:

Abstract

Recently, deep-learning-based 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 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.

Authors

  • Kongxin Gu
    Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto 862-0973, Japan.
  • Masanaga Kenko
    Department of Pharmaceutical Microbiology, School of Pharmacy, Kumamoto University, Kumamoto 862-0973, Japan.
  • Koji Ogawa
    ProteoBridge Corporation, Tokyo 135-0064, Japan.
  • Naoki Goshima
    ProteoBridge Corporation, Tokyo 135-0064, Japan.
  • Takeshi Masuda
    Department of Molecular and Internal Medicine.
  • Shingo Ito
    Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto 862-0973, Japan.
  • Sumio Ohtsuki
    Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto 862-0973, Japan.