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.

Bibliographic Details
Main Authors:	Kim F. Marquart, Ahmed Allam, Sharan Janjuha, Anna Sintsova, Lukas Villiger, Nina Frey, Michael Krauthammer, Gerald Schwank
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-08-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-25375-z

id	doaj-c9632ae8e3554eb0a6f6c3e3694e3ef8
record_format	Article
spelling	doaj-c9632ae8e3554eb0a6f6c3e3694e3ef82021-08-29T11:38:19ZengNature Publishing GroupNature Communications2041-17232021-08-011211910.1038/s41467-021-25375-zPredicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screensKim F. Marquart0Ahmed Allam1Sharan Janjuha2Anna Sintsova3Lukas Villiger4Nina Frey5Michael Krauthammer6Gerald Schwank7Institute of Molecular Health Sciences, ETH ZurichDepartment of Quantitative Biomedicine, University of ZurichDepartment of Pharmacology and Toxicology, University of ZurichDepartment of Quantitative Biomedicine, University of ZurichDepartment of Pharmacology and Toxicology, University of ZurichInstitute of Molecular Health Sciences, ETH ZurichDepartment of Quantitative Biomedicine, University of ZurichDepartment of Pharmacology and Toxicology, University of ZurichBase 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.https://doi.org/10.1038/s41467-021-25375-z
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Kim F. Marquart Ahmed Allam Sharan Janjuha Anna Sintsova Lukas Villiger Nina Frey Michael Krauthammer Gerald Schwank
spellingShingle	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 Nature Communications
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 Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-08-01
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.
url	https://doi.org/10.1038/s41467-021-25375-z
work_keys_str_mv	AT kimfmarquart predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens AT ahmedallam predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens AT sharanjanjuha predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens AT annasintsova predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens AT lukasvilliger predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens AT ninafrey predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens AT michaelkrauthammer predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens AT geraldschwank predictingbaseeditingoutcomeswithanattentionbaseddeeplearningalgorithmtrainedonhighthroughputtargetlibraryscreens
_version_	1721186631066583040

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

Similar Items