Dear-DIA <sup>XMBD</sup> : 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
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 .