Publication | Open Access
SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance
234
Citations
42
References
2019
Year
EngineeringMachine LearningGeneticsActivity RecognitionHigh Generalization PerformanceTranscriptomics TechnologyGenomicsSpcas9 ActivitiesBioinformatics DatabaseData ScienceComputational GenomicsLarge DatasetTranslational BioinformaticsPredictive AnalyticsSequence AnalysisComputer ScienceDeep LearningBioinformaticsFunctional GenomicsBiologyComputational BiologySpcas9 Activity PredictionData-driven PredictionSystems BiologyMedicineDeep Learning–based ModelGenome EditingTarget Sequences
We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA-encoding and target sequence pairs. Deep learning-based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity-predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.
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