Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912624294232064 |
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| author | Ma, Zheng Mao, Zeping Zhang, Ruixue Chen, Jiazhen Xin, Lei Shan, Baozhen Ghodsi, Ali Li, Ming |
| author_facet | Ma, Zheng Mao, Zeping Zhang, Ruixue Chen, Jiazhen Xin, Lei Shan, Baozhen Ghodsi, Ali Li, Ming |
| contents | Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a new deep learning method DIANovo, and address each of these difficulties, and improves the previous established systems by a large margin, via equipping the model with a deeper understanding of coeluted DIA spectra. This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode. We find that while DIA excels with narrow isolation windows on older-generation instruments, it loses its advantage with wider windows. However, with Orbitrap Astral, DIA consistently outperforms DDA due to narrow window mode enabled. We also provide a theoretical explanation of this phenomenon, emphasizing the critical role of the signal-to-noise profile in the successful application of de novo sequencing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15684 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing Ma, Zheng Mao, Zeping Zhang, Ruixue Chen, Jiazhen Xin, Lei Shan, Baozhen Ghodsi, Ali Li, Ming Biomolecules Machine Learning Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a new deep learning method DIANovo, and address each of these difficulties, and improves the previous established systems by a large margin, via equipping the model with a deeper understanding of coeluted DIA spectra. This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode. We find that while DIA excels with narrow isolation windows on older-generation instruments, it loses its advantage with wider windows. However, with Orbitrap Astral, DIA consistently outperforms DDA due to narrow window mode enabled. We also provide a theoretical explanation of this phenomenon, emphasizing the critical role of the signal-to-noise profile in the successful application of de novo sequencing. |
| title | Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2411.15684 |