In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
Data-independent acquisition (DIA) is an emerging technology in proteomics but it typically relies on spectral libraries built by data-dependent acquisition (DDA). Here, the authors use deep learning to generate in silico spectral libraries directly from protein sequences that enable more comprehens...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/77e3c40bed5d4202a8599e1247e8d409 |
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| Sumario: | Data-independent acquisition (DIA) is an emerging technology in proteomics but it typically relies on spectral libraries built by data-dependent acquisition (DDA). Here, the authors use deep learning to generate in silico spectral libraries directly from protein sequences that enable more comprehensive DIA experiments than DDA-based libraries. |
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