SeFilter-DIA : Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics
© 2024. International Association of Scientists in the Interdisciplinary Areas..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Interdisciplinary sciences, computational life sciences - (2024) vom: 12. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
He, Qingzu [VerfasserIn] |
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| Links: |
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| Themen: |
Data-independent acquisition |
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| Anmerkungen: |
Date Revised 13.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s12539-024-00611-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369624114 |
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| 100 | 1 | |a He, Qingzu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a SeFilter-DIA |b Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics |
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| 500 | |a Date Revised 13.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024. International Association of Scientists in the Interdisciplinary Areas. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Data-independent acquisition | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Manual screening peptides | |
| 650 | 4 | |a Mass spectrometry | |
| 650 | 4 | |a Proteomics analysis | |
| 650 | 4 | |a Squeeze-and-excitation networks | |
| 700 | 1 | |a Guo, Huan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yulin |e verfasserin |4 aut | |
| 700 | 1 | |a He, Guoqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiang |e verfasserin |4 aut | |
| 700 | 1 | |a Shuai, Jianwei |e verfasserin |4 aut | |
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