Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
Authors
Keywords
-
Journal
Nature Communications
Volume 12, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-08-25
DOI
10.1038/s41467-021-25375-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Massively parallel assessment of human variants with base editor screens
- (2021) Ruth E. Hanna et al. CELL
- High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells
- (2020) Hui Kwon Kim et al. Nature Biomedical Engineering
- Phage-assisted evolution of an adenine base editor with improved Cas domain compatibility and activity
- (2020) Michelle F. Richter et al. NATURE BIOTECHNOLOGY
- Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning
- (2020) Mandana Arbab et al. CELL
- Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors
- (2020) Andrew V. Anzalone et al. NATURE BIOTECHNOLOGY
- Sequence-specific prediction of the efficiencies of adenine and cytosine base editors
- (2020) Myungjae Song et al. NATURE BIOTECHNOLOGY
- Prediction of the sequence-specific cleavage activity of Cas9 variants
- (2020) Nahye Kim et al. NATURE BIOTECHNOLOGY
- CRISPR DNA base editors with reduced RNA off-target and self-editing activities
- (2019) Julian Grünewald et al. NATURE BIOTECHNOLOGY
- SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance
- (2019) Hui Kwon Kim et al. Science Advances
- Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction
- (2018) Luke W Koblan et al. NATURE BIOTECHNOLOGY
- Predictable and precise template-free CRISPR editing of pathogenic variants
- (2018) Max W. Shen et al. NATURE
- Treatment of a metabolic liver disease by in vivo genome base editing in adult mice
- (2018) Lukas Villiger et al. NATURE MEDICINE
- Base editing: precision chemistry on the genome and transcriptome of living cells
- (2018) Holly A. Rees et al. NATURE REVIEWS GENETICS
- Double-slit photoelectron interference in strong-field ionization of the neon dimer
- (2018) Maksim Kunitski et al. Nature Communications
- Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage
- (2017) Nicole M. Gaudelli et al. NATURE
- Improved base excision repair inhibition and bacteriophage Mu Gam protein yields C:G-to-T:A base editors with higher efficiency and product purity
- (2017) Alexis C. Komor et al. Science Advances
- Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage
- (2016) Alexis C. Komor et al. NATURE
- In vivo high-throughput profiling of CRISPR–Cpf1 activity
- (2016) Hui K Kim et al. NATURE METHODS
- Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems
- (2016) K. Nishida et al. SCIENCE
- Improved vectors and genome-wide libraries for CRISPR screening
- (2014) Neville E Sanjana et al. NATURE METHODS
- ClinVar: public archive of relationships among sequence variation and human phenotype
- (2013) Melissa J. Landrum et al. NUCLEIC ACIDS RESEARCH
- Genetic Screens in Human Cells Using the CRISPR-Cas9 System
- (2013) Tim Wang et al. SCIENCE
- Fast gapped-read alignment with Bowtie 2
- (2012) Ben Langmead et al. NATURE METHODS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now