• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于图的无监督学习模型的 CRISPR 和 shRNA 筛选技术对必需基因的系统比较

Systematic Comparison of CRISPR and shRNA Screens to Identify Essential Genes Using a Graph-Based Unsupervised Learning Model.

机构信息

Central for High-Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.

出版信息

Cells. 2024 Oct 4;13(19):1653. doi: 10.3390/cells13191653.

DOI:10.3390/cells13191653
PMID:39404416
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11475473/
Abstract

Generally, essential genes identified using shRNA and CRISPR are not always the same, raising questions about the choice between these two screening platforms. To address this, we systematically compared the performance of CRISPR and shRNA to identify essential genes across different gene expression levels in 254 cell lines. As both platforms have a notable false positive rate, to correct this confounding factor, we first developed a graph-based unsupervised machine learning model to predict common essential genes. Furthermore, to maintain the unique characteristics of individual cell lines, we intersect essential genes derived from the biological experiment with the predicted common essential genes. Finally, we employed statistical methods to compare the ability of these two screening platforms to identify essential genes that exhibit differential expression across various cell lines. Our analysis yielded several noteworthy findings: (1) shRNA outperforms CRISPR in the identification of lowly expressed essential genes; (2) both screening methodologies demonstrate strong performance in identifying highly expressed essential genes but with limited overlap, so we suggest using a combination of these two platforms for highly expressed essential genes; (3) notably, we did not observe a single gene that becomes universally essential across all cancer cell lines.

摘要

通常情况下,使用 shRNA 和 CRISPR 鉴定的必需基因并不总是相同的,这就引发了对这两种筛选平台之间的选择的质疑。为了解决这个问题,我们系统地比较了 CRISPR 和 shRNA 在 254 种细胞系中不同基因表达水平下识别必需基因的性能。由于这两种平台都有显著的假阳性率,为了纠正这个混杂因素,我们首先开发了一种基于图的无监督机器学习模型来预测常见的必需基因。此外,为了保持单个细胞系的独特特征,我们将从生物学实验中得出的必需基因与预测的常见必需基因进行交叉。最后,我们采用统计方法比较了这两种筛选平台识别在不同细胞系中表达差异的必需基因的能力。我们的分析得出了几个值得注意的发现:(1)shRNA 在识别低表达必需基因方面优于 CRISPR;(2)两种筛选方法在识别高表达必需基因方面都表现出了很强的性能,但重叠有限,因此我们建议将这两种平台结合起来用于高表达必需基因;(3)值得注意的是,我们没有观察到一个在所有癌细胞系中普遍必需的基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/3469f07434ee/cells-13-01653-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/c1290e5153e8/cells-13-01653-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/a39f3f30aa91/cells-13-01653-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/24e142fec39e/cells-13-01653-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/bf56f29139b3/cells-13-01653-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/3469f07434ee/cells-13-01653-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/c1290e5153e8/cells-13-01653-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/a39f3f30aa91/cells-13-01653-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/24e142fec39e/cells-13-01653-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/bf56f29139b3/cells-13-01653-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c6f/11475473/3469f07434ee/cells-13-01653-g007.jpg

相似文献

1
Systematic Comparison of CRISPR and shRNA Screens to Identify Essential Genes Using a Graph-Based Unsupervised Learning Model.基于图的无监督学习模型的 CRISPR 和 shRNA 筛选技术对必需基因的系统比较
Cells. 2024 Oct 4;13(19):1653. doi: 10.3390/cells13191653.
2
Identification of Essential Genes Using Sequential CRISPR and siRNA Screens.使用连续CRISPR和siRNA筛选鉴定必需基因
Methods Mol Biol. 2022;2377:89-107. doi: 10.1007/978-1-0716-1720-5_5.
3
Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes.对用于必需基因的CRISPR/Cas9和RNA干扰筛选进行系统比较。
Nat Biotechnol. 2016 Jun;34(6):634-6. doi: 10.1038/nbt.3567. Epub 2016 May 9.
4
CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes.CRISPR 敲除筛选在鉴定必需基因方面优于 shRNA 和 CRISPRi。
Nat Biotechnol. 2016 Jun;34(6):631-3. doi: 10.1038/nbt.3536. Epub 2016 Apr 25.
5
CRISPR Screens Provide a Comprehensive Assessment of Cancer Vulnerabilities but Generate False-Positive Hits for Highly Amplified Genomic Regions.CRISPR 筛选提供了对癌症易损性的全面评估,但对高度扩增的基因组区域产生了假阳性结果。
Cancer Discov. 2016 Aug;6(8):900-13. doi: 10.1158/2159-8290.CD-16-0178. Epub 2016 Jun 3.
6
SeqCor: correct the effect of guide RNA sequences in clustered regularly interspaced short palindromic repeats/Cas9 screening by machine learning algorithm.SeqCor:通过机器学习算法纠正簇状规则间隔短回文重复序列/Cas9 筛选中引导 RNA 序列的影响。
J Genet Genomics. 2020 Nov 20;47(11):672-680. doi: 10.1016/j.jgg.2020.10.007. Epub 2020 Nov 28.
7
CRISPR-Cas9 screens reveal common essential miRNAs in human cancer cell lines.CRISPR-Cas9 筛选揭示了人类癌细胞系中常见的必需 miRNA。
Genome Med. 2024 Jun 17;16(1):82. doi: 10.1186/s13073-024-01341-4.
8
CEDA: integrating gene expression data with CRISPR-pooled screen data identifies essential genes with higher expression.CEDA:将基因表达数据与 CRISPR 池筛选数据相结合,可识别出表达水平较高的必需基因。
Bioinformatics. 2022 Nov 30;38(23):5245-5252. doi: 10.1093/bioinformatics/btac668.
9
In vivo CRISPR screening for novel noncoding RNA functional targets in glioblastoma models.在胶质母细胞瘤模型中进行新型非编码 RNA 功能靶标体内 CRISPR 筛选。
J Neurosci Res. 2021 Sep;99(9):2029-2045. doi: 10.1002/jnr.24850. Epub 2021 May 10.
10
Prediction of potent shRNAs with a sequential classification algorithm.使用序列分类算法预测有效的短发夹RNA
Nat Biotechnol. 2017 Apr;35(4):350-353. doi: 10.1038/nbt.3807. Epub 2017 Mar 6.

引用本文的文献

1
A novel role for Neurog2 in MYCN driven neuroendocrine plasticity of prostate cancer.Neurog2在MYCN驱动的前列腺癌神经内分泌可塑性中的新作用。
Oncogene. 2025 Apr 29. doi: 10.1038/s41388-025-03413-0.

本文引用的文献

1
Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms.利用机器学习算法鉴定与 SARS-CoV-2 感染相关的必需基因作为潜在的药物靶点候选物。
Sci Rep. 2023 Sep 13;13(1):15141. doi: 10.1038/s41598-023-42127-9.
2
Identification of Novel Regulators of Radiosensitivity Using High-Throughput Genetic Screening.利用高通量遗传筛选鉴定新型放射敏感性调控因子。
Int J Mol Sci. 2022 Aug 7;23(15):8774. doi: 10.3390/ijms23158774.
3
Optimized RNA-targeting CRISPR/Cas13d technology outperforms shRNA in identifying functional circRNAs.
优化的 RNA 靶向 CRISPR/Cas13d 技术在鉴定功能性 circRNAs 方面优于 shRNA。
Genome Biol. 2021 Jan 21;22(1):41. doi: 10.1186/s13059-021-02263-9.
4
New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform.基于整合分析和 HEGIAP 网络平台构建的人类必需基因新见解。
Brief Bioinform. 2020 Jul 15;21(4):1397-1410. doi: 10.1093/bib/bbz072.
5
Next-generation characterization of the Cancer Cell Line Encyclopedia.下一代癌症细胞系百科全书的特征描述。
Nature. 2019 May;569(7757):503-508. doi: 10.1038/s41586-019-1186-3. Epub 2019 May 8.
6
Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens.利用 CRISPR-Cas9 筛选技术对癌症治疗靶点进行优先级排序。
Nature. 2019 Apr;568(7753):511-516. doi: 10.1038/s41586-019-1103-9. Epub 2019 Apr 10.
7
Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting.无监督校正基因独立的 CRISPR-Cas9 靶向细胞反应。
BMC Genomics. 2018 Aug 13;19(1):604. doi: 10.1186/s12864-018-4989-y.
8
An RNA Origami Octahedron with Intrinsic siRNAs for Potent Gene Knockdown.一种具有内在 siRNA 的 RNA 折纸八面体,可有效进行基因敲低。
Biotechnol J. 2019 Jan;14(1):e1700634. doi: 10.1002/biot.201700634. Epub 2018 Jun 13.
9
Defining a Cancer Dependency Map.定义癌症依赖图谱。
Cell. 2017 Jul 27;170(3):564-576.e16. doi: 10.1016/j.cell.2017.06.010.
10
Gene Essentiality Profiling Reveals Gene Networks and Synthetic Lethal Interactions with Oncogenic Ras.基因必需性分析揭示基因网络以及与致癌性Ras的合成致死相互作用。
Cell. 2017 Feb 23;168(5):890-903.e15. doi: 10.1016/j.cell.2017.01.013. Epub 2017 Feb 2.