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In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
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In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
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In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
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Journal Article
In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
2020
Overview
Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA 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 comprehensive DIA experiments than DDA-based libraries.
