Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Qingzu He; Chuan-Qi Zhong; Xiang Li; Huan Guo; Yiming Li; Mingxuan Gao; Rongshan Yu; Xianming Liu; Fangfei Zhang; Donghui Guo; Fangfu Ye; Tiannan Guo; Jianwei Shuai; Jiahuai Han

doi:10.34133/research.0179

Dear-DIA^XMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Research (Wash D C). 2023 Jun 26:6:0179. doi: 10.34133/research.0179. eCollection 2023.

Authors

Qingzu He^{1

2}, Chuan-Qi Zhong^{3

4}, Xiang Li^{1

4}, Huan Guo¹, Yiming Li¹, Mingxuan Gao⁵, Rongshan Yu^{5

6}, Xianming Liu⁷, Fangfei Zhang^{8

9}, Donghui Guo¹⁰, Fangfu Ye², Tiannan Guo^{8

9

11}, Jianwei Shuai^{2

1

4

6}, Jiahuai Han^{3

4

6}

Affiliations

¹ Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China.
² Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China.
³ School of Life Sciences, Xiamen University, Xiamen 361102, China.
⁴ State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen 361102, China.
⁵ Department of Computer Science, Xiamen University, Xiamen 361005, China.
⁶ National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen 361102, China.
⁷ Bruker (Beijing) Scientific Technology Co. Ltd., Beijing, China.
⁸ Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China.
⁹ Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China.
¹⁰ Department of Electronic Engineering, Xiamen University, Xiamen 361005, China.
¹¹ Westlake Omics Ltd., Yunmeng Road 1, Hangzhou, China.

Abstract

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.