BABEL: using deep learning to translate between single-cell datasets

Recent advances in sequencing and barcoding technologies have enabled researchers to simultaneously profile gene expression, chromatin accessibility, and/or protein levels in single cells. However, these multiomic techniques often pose technical and financial barriers that limit their practicality....

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Autor principal:	George Andrew S. Inglis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4e0b2ba8f4ed4bae8a12fec4eec6f3c5
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spelling	oai:doaj.org-article:4e0b2ba8f4ed4bae8a12fec4eec6f3c52021-12-02T15:55:21ZBABEL: using deep learning to translate between single-cell datasets10.1038/s42003-021-02135-92399-3642https://doaj.org/article/4e0b2ba8f4ed4bae8a12fec4eec6f3c52021-05-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02135-9https://doaj.org/toc/2399-3642Recent advances in sequencing and barcoding technologies have enabled researchers to simultaneously profile gene expression, chromatin accessibility, and/or protein levels in single cells. However, these multiomic techniques often pose technical and financial barriers that limit their practicality. Kevin Wu and colleagues recently developed BABEL, a deep learning algorithm that can effectively translate between transcriptomic and chromatin profiles in single cells, thereby enabling researchers to perform multiomic analyses from an individual dataset.George Andrew S. InglisNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 George Andrew S. Inglis BABEL: using deep learning to translate between single-cell datasets
description	Recent advances in sequencing and barcoding technologies have enabled researchers to simultaneously profile gene expression, chromatin accessibility, and/or protein levels in single cells. However, these multiomic techniques often pose technical and financial barriers that limit their practicality. Kevin Wu and colleagues recently developed BABEL, a deep learning algorithm that can effectively translate between transcriptomic and chromatin profiles in single cells, thereby enabling researchers to perform multiomic analyses from an individual dataset.
format	article
author	George Andrew S. Inglis
author_facet	George Andrew S. Inglis
author_sort	George Andrew S. Inglis
title	BABEL: using deep learning to translate between single-cell datasets
title_short	BABEL: using deep learning to translate between single-cell datasets
title_full	BABEL: using deep learning to translate between single-cell datasets
title_fullStr	BABEL: using deep learning to translate between single-cell datasets
title_full_unstemmed	BABEL: using deep learning to translate between single-cell datasets
title_sort	babel: using deep learning to translate between single-cell datasets
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4e0b2ba8f4ed4bae8a12fec4eec6f3c5
work_keys_str_mv	AT georgeandrewsinglis babelusingdeeplearningtotranslatebetweensinglecelldatasets
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BABEL: using deep learning to translate between single-cell datasets

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