清脆的
基因组
生物
计算生物学
基因
亚基因组mRNA
基因组工程
背景(考古学)
遗传学
基因组编辑
Cas9
表型
功能(生物学)
古生物学
作者
Boyang Zhao,Yiyun Rao,Scott M. Leighow,Edward P. O’Brien,Luke A. Gilbert,Justin R. Pritchard
标识
DOI:10.1038/s41467-022-28045-w
摘要
Abstract A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human cell lines offer the largest opportunity to understand the biology of cell specificity. The prevailing viewpoint, synthetic lethality, occurs when a genetic alteration creates a unique CRISPR dependency. Here, we use machine learning for an unbiased investigation of cell type specificity. Quantifying model accuracy, we find that most cell type specific phenotypes are predicted by the function of related genes of wild-type sequence, not synthetic lethal relationships. These models then identify unexpected sets of 100-300 genes where reduced CRISPR measurements can produce genome-scale loss-of-function predictions across >18,000 genes. Thus, it is possible to reduce in vitro CRISPR libraries by orders of magnitude—with some information loss—when we remove redundant genes and not redundant sgRNAs.
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