Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors enable precise genetic alterations but vary in efficiency at different loci. Here the authors analyse ABEs and CBEs at over 28,000 integrated sequences to train BE-DICT, a machine learning model capable of predicting base editing outcomes.

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Bibliographic Details
Main Authors:	Kim F. Marquart, Ahmed Allam, Sharan Janjuha, Anna Sintsova, Lukas Villiger, Nina Frey, Michael Krauthammer, Gerald Schwank
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Science Q
Online Access:	https://doaj.org/article/c9632ae8e3554eb0a6f6c3e3694e3ef8
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id	oai:doaj.org-article:c9632ae8e3554eb0a6f6c3e3694e3ef8
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spelling	oai:doaj.org-article:c9632ae8e3554eb0a6f6c3e3694e3ef82021-12-02T15:09:10ZPredicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens10.1038/s41467-021-25375-z2041-1723https://doaj.org/article/c9632ae8e3554eb0a6f6c3e3694e3ef82021-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25375-zhttps://doaj.org/toc/2041-1723Base editors enable precise genetic alterations but vary in efficiency at different loci. Here the authors analyse ABEs and CBEs at over 28,000 integrated sequences to train BE-DICT, a machine learning model capable of predicting base editing outcomes.Kim F. MarquartAhmed AllamSharan JanjuhaAnna SintsovaLukas VilligerNina FreyMichael KrauthammerGerald SchwankNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kim F. Marquart Ahmed Allam Sharan Janjuha Anna Sintsova Lukas Villiger Nina Frey Michael Krauthammer Gerald Schwank Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
description	Base editors enable precise genetic alterations but vary in efficiency at different loci. Here the authors analyse ABEs and CBEs at over 28,000 integrated sequences to train BE-DICT, a machine learning model capable of predicting base editing outcomes.
format	article
author	Kim F. Marquart Ahmed Allam Sharan Janjuha Anna Sintsova Lukas Villiger Nina Frey Michael Krauthammer Gerald Schwank
author_facet	Kim F. Marquart Ahmed Allam Sharan Janjuha Anna Sintsova Lukas Villiger Nina Frey Michael Krauthammer Gerald Schwank
author_sort	Kim F. Marquart
title	Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
title_short	Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
title_full	Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
title_fullStr	Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
title_full_unstemmed	Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
title_sort	predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c9632ae8e3554eb0a6f6c3e3694e3ef8
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Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

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