Dataset Information

Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

ABSTRACT: Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated "base editing" being limited to certain nucleotide changes, and only achievable within a small window around CRISPR-Cas target sites. The more versatile alternative, HDR (homology directed repair), has a 3-fold lower efficiency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efficiency-governing factors on a novel mouse dataset using machine learning. We found the sequence composition of the single-stranded oligodeoxynucleotide (ssODN), i.e. the repair template, to be a governing factor. Furthermore, different regions of the ssODN have variable influence, which reflects the underlying mechanism of the repair process. Our model improves HDR efficiency by 83% compared to traditionally chosen targets. Using our findings, we developed CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy using traditional base editing or?-?for-the-first-time?-?HDR-mediated nucleotide changes.

SUBMITTER: O'Brien AR

PROVIDER: S-EPMC6391469 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

O'Brien Aidan R AR Wilson Laurence O W LOW Burgio Gaetan G Bauer Denis C DC Bauer Denis C DC

Scientific reports 20190226 1

Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated "base editing" being limited to certain nucleotide changes, and only achievable within a small window around CRISPR-Cas target sites. The more versatile alternative, HDR (homology directed repair), has a 3-fold lower efficiency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efficiency-governin ...[more]

PMID: 30808944

Similar Datasets

Project description:MotivationCRISPR/Cas9 technology has been revolutionizing the field of gene editing in recent years. Guide RNAs (gRNAs) enable Cas9 proteins to target specific genomic loci for editing. However, editing efficiency varies between gRNAs. Thus, computational methods were developed to predict editing efficiency for any gRNA of interest. High-throughput datasets of Cas9 editing efficiencies were produced to train machine-learning models to predict editing efficiency. However, these high-throughput datasets have low correlation with functional and endogenous editing. Another difficulty arises from the fact that functional and endogenous editing efficiency is more difficult to measure, and as a result, functional and endogenous datasets are too small to train accurate machine-learning models on.ResultsWe developed DeepCRISTL, a deep-learning model to predict the on-target efficiency given a gRNA sequence. DeepCRISTL takes advantage of high-throughput datasets to learn general patterns of gRNA on-target editing efficiency, and then uses transfer learning (TL) to fine-tune the model and fit it to the functional and endogenous prediction task. We pre-trained the DeepCRISTL model on more than 150 000 gRNAs, produced through the DeepHF study as a high-throughput dataset of three Cas9 enzymes. We improved the DeepHF model by multi-task and ensemble techniques and achieved state-of-the-art results over each of the three enzymes: up to 0.89 in Spearman correlation between predicted and measured on-target efficiencies. To fine-tune model weights to predict on-target efficiency of functional or endogenous datasets, we tested several TL approaches, with gradual learning being the overall best performer, both when pre-trained on DeepHF and when pre-trained on CRISPROn, another high-throughput dataset. DeepCRISTL outperformed state-of-the-art methods on all functional and endogenous datasets. Using saliency maps, we identified and compared the important features learned by the model in each dataset. We believe DeepCRISTL will improve prediction performance in many other CRISPR/Cas9 editing contexts by leveraging TL to utilize both high-throughput datasets, and smaller and more biologically relevant datasets, such as functional and endogenous datasets.Availability and implementationDeepCRISTL is available via github.com/OrensteinLab/DeepCRISTL.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

Publications

Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets