Genome biology · 2019 · 109 citations · 35 references
Life SciencesBiotechnologyGeneticsGenetic EngineeringGenome EditingGenomicsAllele-specific EditingCrispr-cas SystemsGene Knockout StrategiesConditional Gene KnockoutConditional Knock-in AllelesConditional Lethal AllelesCrispr-cas9 Genome EditingCrispr-cas9 KnockoutCrispr-cas9 Knockout ModelsIn Vivo Genome Editing
BACKGROUND: CRISPR-Cas9 gene-editing technology has facilitated the generation of knockout mice, providing an alternative to cumbersome and time-consuming traditional embryonic stem cell-based methods. An earlier study reported up to 16% efficiency in generating conditional knockout (cKO or floxed) alleles by microinjection of 2 single guide RNAs (sgRNA) and 2 single-stranded oligonucleotides as donors (referred herein as "two-donor floxing" method). RESULTS: We re-evaluate the two-donor method from a consortium of 20 laboratories across the world. The dataset constitutes 56 genetic loci, 17,887 zygotes, and 1718 live-born mice, of which only 15 (0.87%) mice contain cKO alleles. We subject the dataset to statistical analyses and a machine learning algorithm, which reveals that none of the factors analyzed was predictive for the success of this method. We test some of the newer methods that use one-donor DNA on 18 loci for which the two-donor approach failed to produce cKO alleles. We find that the one-donor methods are 10- to 20-fold more efficient than the two-donor approach. CONCLUSION: We propose that the two-donor method lacks efficiency because it relies on two simultaneous recombination events in cis, an outcome that is dwarfed by pervasive accompanying undesired editing events. The methods that use one-donor DNA are fairly efficient as they rely on only one recombination event, and the probability of correct insertion of the donor cassette without unanticipated mutational events is much higher. Therefore, one-donor methods offer higher efficiencies for the routine generation of cKO animal models.
35
Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Multiplex Genome Engineering Using CRISPR/Cas Systems
Le Cong, F. Ann Ran, David Cox et al. · Science · 2013 · 15.7K citations
Haoyi Wang, Hui Yang, Chikdu Shivalila et al. · Cell · 2013 · 3.6K citations · Full text