Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity
By Hui Kwon Kim, Seonwoo Min, Myungjae Song, Soobin Jung, J. W. Choi, Younggwan Kim, Sangeun Lee, Sungroh Yoon, H. Kim. Nature Biotechnology, 2018
347 citations recorded by the source corpus.
We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/70cb60793c160708286fe0182b4552edeb001ccb
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In Molecular Biology
Citations in the corpus
- DeepCRISPR: optimized CRISPR guide RNA design by deep learning (2018, background): “Recently, several studies have tried sophisticated learning models for on-target or off-target prediction [28 – 30], but none of them have addressed these issues thoroughly.”
- Artificial intelligence and machine learning in precision and genomic medicine (2021, methodology): “ML is being used on these datasets in genomics for a variety of reasons, including defining disease subtypes, finding biomarkers of diseases, drug discovery [206] and repurposing [207], and medication response prediction [208].”
- CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing (2019, methodology): “In addition, we have added efficiency scores for Cas12a/Cpf1 (21) and updated the ‘Doench’ efficiency score to the newest version (15), which is now the default scoring metric for Cas9 genomic targeting.”