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:	Yi Yang, Xiaohui Liu, Chengpin Shen, Yu Lin, Pengyuan Yang, Liang Qiao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/77e3c40bed5d4202a8599e1247e8d409
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id	oai:doaj.org-article:77e3c40bed5d4202a8599e1247e8d409
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spelling	oai:doaj.org-article:77e3c40bed5d4202a8599e1247e8d4092021-12-02T17:31:41ZIn silico spectral libraries by deep learning facilitate data-independent acquisition proteomics10.1038/s41467-019-13866-z2041-1723https://doaj.org/article/77e3c40bed5d4202a8599e1247e8d4092020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13866-zhttps://doaj.org/toc/2041-1723Data-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.Yi YangXiaohui LiuChengpin ShenYu LinPengyuan YangLiang QiaoNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yi Yang Xiaohui Liu Chengpin Shen Yu Lin Pengyuan Yang Liang Qiao In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
description	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.
format	article
author	Yi Yang Xiaohui Liu Chengpin Shen Yu Lin Pengyuan Yang Liang Qiao
author_facet	Yi Yang Xiaohui Liu Chengpin Shen Yu Lin Pengyuan Yang Liang Qiao
author_sort	Yi Yang
title	In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
title_short	In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
title_full	In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
title_fullStr	In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
title_full_unstemmed	In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
title_sort	in silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/77e3c40bed5d4202a8599e1247e8d409
work_keys_str_mv	AT yiyang insilicospectrallibrariesbydeeplearningfacilitatedataindependentacquisitionproteomics AT xiaohuiliu insilicospectrallibrariesbydeeplearningfacilitatedataindependentacquisitionproteomics AT chengpinshen insilicospectrallibrariesbydeeplearningfacilitatedataindependentacquisitionproteomics AT yulin insilicospectrallibrariesbydeeplearningfacilitatedataindependentacquisitionproteomics AT pengyuanyang insilicospectrallibrariesbydeeplearningfacilitatedataindependentacquisitionproteomics AT liangqiao insilicospectrallibrariesbydeeplearningfacilitatedataindependentacquisitionproteomics
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In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics

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