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SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics

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Abstract

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

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Funding

This work is supported by the Ministry of Science and Technology of the People's Republic of China (STI2030-Major Projects2021ZD0201900), the National Natural Science Foundation of China under Grant 12090052 and 11874310, the Natural Science Foundation of Fujian Province of China (Grant No. 2023J05002), and the Fundamental Research Funds for the Central Universities (Grant No. 20720230017).

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  1. Qingzu He and Huan Guo would be designated as co-first authors due to their equal contributions to the research and preparation of the manuscript.

Authors and Affiliations

  1. Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China

    Qingzu He, Huan Guo, Yulin Li & Xiang Li

  2. Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China

    Qingzu He, Guoqiang He & Jianwei Shuai

  3. Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325001, China

    Jianwei Shuai

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  1. Qingzu He
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  3. Yulin Li
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He, Q., Guo, H., Li, Y. et al. SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00611-4

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