کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9951566 | 1416960 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Strategies for controlling CRISPR/Cas9 off-target effects and biological variations in mammalian genome editing experiments
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کلمات کلیدی
CRISPRHDRgRNANHEJOTEOff-target effects - اثرات غیر هدفGoI - بروclustered regularly interspaced short palindromic repeats - به طور منظم تکرار پینگندرومی کوتاه مدت میان دو طرف تقسیم می شودknock in - دست کشیدنguide RNA - راهنمای RNAnon-homologous end joining - عدم پیوستن انتهای غیر همولوگLOI - قانونknock out - ناک اوتhomology directed repair - هماهنگی کارگردانی تعمیرgene of interest - ژن مورد علاقه
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The CRISPR/Cas9 system has enabled efficient modification of genes in a variety of cellular systems for studying phenotypic effects of genetic perturbations. However, with this technology comes the inherent risk of generating off-target effects (OTEs) in addition to the desired modifications. As such, it can be difficult to conclusively determine that the observed phenotypic changes are in fact due to the intended modification of the target gene and not from random mutations elsewhere in the genome. In addition, biological variations observed within cultured cells or laboratory animals can also confound results and need to be addressed. In this article, we review potential sources of experimental and biological variation as well as propose experimental options to minimize and control OTEs and other variations in CRISPR genome editing experiments for exploratory research applications. Confirmation of on-target KO effect by orthogonal approaches is also discussed.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Biotechnology - Volume 284, 20 October 2018, Pages 91-101
Journal: Journal of Biotechnology - Volume 284, 20 October 2018, Pages 91-101
نویسندگان
Michelle L. Kimberland, Wangfang Hou, Adolfo Alfonso-Pecchio, Stephen Wilson, Yanhua Rao, Shu Zhang, Quinn Lu,