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He Q, Guo H, Li Y, He G, Li X, Shuai J. SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics. Interdiscip Sci 2024:10.1007/s12539-024-00611-4. [PMID: 38472692 DOI: 10.1007/s12539-024-00611-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/12/2024] [Accepted: 01/21/2024] [Indexed: 03/14/2024]
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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Affiliation(s)
- Qingzu He
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Huan Guo
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
| | - Yulin Li
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
| | - Guoqiang He
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Xiang Li
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325001, China.
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Li Y, He Q, Guo H, Shuai SC, Cheng J, Liu L, Shuai J. AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics. J Proteome Res 2024; 23:834-843. [PMID: 38252705 DOI: 10.1021/acs.jproteome.3c00729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postprocessing software have been developed for reranking the peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance on shallow models, and limited effectiveness. In this work, we propose AttnPep, a deep learning model for rescoring PSM scores that utilizes the Self-Attention module. This module helps the neural network focus on features relevant to the classification of PSMs and ignore irrelevant features. This allows AttnPep to analyze the output of different search engines and improve PSM discrimination accuracy. We considered a PSM to be correct if it achieves a q-value <0.01 and compared AttnPep with existing mainstream software PeptideProphet, Percolator, and proteoTorch. The results indicated that AttnPep found an average increase in correct PSMs of 9.29% relative to the other methods. Additionally, AttnPep was able to better distinguish between correct and incorrect PSMs and found more synthetic peptides in the complex SWATH data set.
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Affiliation(s)
- Yulin Li
- Department of Physics, Xiamen University, Xiamen 361005, China
| | - Qingzu He
- Department of Physics, Xiamen University, Xiamen 361005, China
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Huan Guo
- Department of Physics, Xiamen University, Xiamen 361005, China
| | - Stella C Shuai
- Biological Science, Northwestern University, Evanston, Illinois 60208, United States
| | - Jinyan Cheng
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Liyu Liu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen 361005, China
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
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He Q, Zhong CQ, Li X, Guo H, Li Y, Gao M, Yu R, Liu X, Zhang F, Guo D, Ye F, Guo T, Shuai J, Han J. Dear-DIA XMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics. RESEARCH (WASHINGTON, D.C.) 2023; 6:0179. [PMID: 37377457 PMCID: PMC10292580 DOI: 10.34133/research.0179] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/01/2023] [Indexed: 06/29/2023]
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-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD 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-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.
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Affiliation(s)
- Qingzu He
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Chuan-Qi Zhong
- School of Life Sciences,
Xiamen University, Xiamen 361102, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
| | - Huan Guo
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
| | - Yiming Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
| | - Mingxuan Gao
- Department of Computer Science,
Xiamen University, Xiamen 361005, China
| | - Rongshan Yu
- Department of Computer Science,
Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
| | - Xianming Liu
- Bruker (Beijing) Scientific Technology Co. Ltd., Beijing, China
| | - Fangfei Zhang
- 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
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Donghui Guo
- Department of Electronic Engineering,
Xiamen University, Xiamen 361005, China
| | - Fangfu Ye
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Tiannan Guo
- 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
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
- Westlake Omics Ltd., Yunmeng Road 1, Hangzhou, China
| | - Jianwei Shuai
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
| | - Jiahuai Han
- School of Life Sciences,
Xiamen University, Xiamen 361102, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
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Wei C, Xiang X, Zhou X, Ren S, Zhou Q, Dong W, Lin H, Wang S, Zhang Y, Lin H, He Q, Lu Y, Jiang X, Shuai J, Jin X, Xie C. Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients. Front Microbiol 2023; 13:1090770. [PMID: 36713206 PMCID: PMC9877536 DOI: 10.3389/fmicb.2022.1090770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Abstract
Background Radiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients. Methods The severity of the patient's radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model's target. A total of 178 patients with cervical cancer were divided into a training set (n = 124) and a validation set (n = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models. Results The radiomics model [AUC=0.6855(0.5174-0.8535)] showed better performance and more net benefit in the validation set than the non-radiomic model [AUC=0.6641(0.4904-0.8378)]. In particular, we applied SHapley Additive exPlanation (SHAP) method for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only. Conclusion We successfully developed and validated an integrated radiomic model containing rectal information. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients.
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Affiliation(s)
- Chaoyi Wei
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xinli Xiang
- The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xiaobo Zhou
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Siyan Ren
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qingyu Zhou
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Wenjun Dong
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Haizhen Lin
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Saijun Wang
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yuyue Zhang
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Hai Lin
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Qingzu He
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Yuer Lu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xiaoming Jiang
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xiance Jin
- Radiotherapy Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China,School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang Province, China,*Correspondence: Xiance Jin, ✉
| | - Congying Xie
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China,Congying Xie, ✉
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