A statistical framework for predicting critical regions of p53-dependent enhancers.
A statistical framework for predicting critical regions of p53-dependent enhancers.
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预测 p53 依赖性增强子关键区域的统计框架
DOI：
10.1093/bib/bbaa053
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发表时间：
2021-05-20
影响因子：
9.5
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Niu X;Deng K;Liu L;Yang K;Hu X
p53被称为“基因组守护者”，负责调控细胞周期与细胞凋亡。基因组中p53结合区域，即激活转录因子和诸如p300等辅因子同时结合的区域，被称作“p53依赖性增强子”，其在肿瘤发生过程中发挥着重要作用。目前的实验检测方法通常只能给出每个增强子元件的宽泛峰值，这使得我们对关键增强子区域（CERs）的了解十分有限。受CRISPR - Cas9筛选文库在全基因组p53结合位点上进行增强子剖析的启发，在此我们引入一种名为“计算型CRISPR策略”（CCS）的统计框架，通过以7聚体作为特征提取方式，并采用随机森林作为回归算法，来预测给定的DNA片段是否为p53依赖性CER。在p53 CRISPR增强子数据集上进行训练时，CCS不仅能准确拟合排名靠前的富集型单向导RNA（sgRNAs），还成功复现了两个经实验验证的已知CER。当将训练好的模型应用于CRISPR - deCDKN1A - Lib的2K - b基因组区域平铺的独立测试数据集时，该模型通过识别出一个包含五个排名靠前的sgRNAs的CER，展现出了很强的泛化能力。特征重要性分析进一步表明，排名靠前的7聚体可映射到包括POU5F1和SOX5在内的信息性转录因子基序上，这些基序在p53依赖性CER中差异富集，是促使普通p53结合位点形成p53依赖性CER的潜在因素，这也为训练好的模型提供了解释性。我们的研究结果表明，CCS是一种可替代CRISPR实验在基因组中筛选并定位p53依赖性CER的方法。
P53 is the ‘guardian of the genome’ and is responsible for regulating cell cycle and apoptosis. The genomic p53 binding regions, where activating transcriptional factors and cofactors like p300 simultaneously bind, are called ‘p53-dependent enhancers’, which play an important role in tumorigenesis. Current experimental assays generally provide a broad peak of each enhancer element, leaving our knowledge about critical enhancer regions (CERs) limited. Under the inspiration of enhancer dissection by CRISPR-Cas9 screen library on genome-wide p53 binding sites, here we introduce a statistical framework called ‘Computational CRISPR Strategy’ (CCS), to predict whether a given DNA fragment will be a p53-dependent CER by employing 7-mer as feature extractions along with random forest as the regressor. When training on a p53 CRISPR enhancer dataset, CCS not only accurately fitted the top-ranked enriched single guide RNAs (sgRNAs) but also successfully reproduced two known CERs that were validated by experiments. When applying it to an independent testing dataset on a tilling of a 2K-b genomic region of CRISPR-deCDKN1A-Lib, the trained model shows great generalizability by identifying a CER containing five top-ranked sgRNAs. A feature importance analysis further indicates that top-ranked 7-mers are mapped onto informative TF motifs including POU5F1 and SOX5, which are differentially enriched in p53-dependent CERs and are potential factors to make a general p53 binding site to form a p53-dependent CER, providing the interpretability of the trained model. Our results demonstrate that CCS is an alternative way of the CRISPR experiment to screen the genome for mapping p53-dependent CERs.
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影响因子：
64.8
作者：
Heintzman, Nathaniel D.;Hon, Gary C.;Hawkins, R. David;Kheradpour, Pouya;Stark, Alexander;Harp, Lindsey F.;Ye, Zhen;Lee, Leonard K.;Stuart, Rhona K.;Ching, Christina W.;Ching, Keith A.;Antosiewicz-Bourget, Jessica E.;Liu, Hui;Zhang, Xinmin;Green, Roland D.;Lobanenkov, Victor V.;Stewart, Ron;Thomson, James A.;Crawford, Gregory E.;Kellis, Manolis;Ren, Bing
通讯作者：
Ren, Bing
影响因子：
7
作者：
Kwasnieski JC;Fiore C;Chaudhari HG;Cohen BA
通讯作者：
Cohen BA
影响因子：
48
作者：
Diao Y;Fang R;Li B;Meng Z;Yu J;Qiu Y;Lin KC;Huang H;Liu T;Marina RJ;Jung I;Shen Y;Guan KL;Ren B
通讯作者：
Ren B
DOI：
10.1126/science.1222794
发表时间：
2012-09-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Maurano MT;Humbert R;Rynes E;Thurman RE;Haugen E;Wang H;Reynolds AP;Sandstrom R;Qu H;Brody J;Shafer A;Neri F;Lee K;Kutyavin T;Stehling-Sun S;Johnson AK;Canfield TK;Giste E;Diegel M;Bates D;Hansen RS;Neph S;Sabo PJ;Heimfeld S;Raubitschek A;Ziegler S;Cotsapas C;Sotoodehnia N;Glass I;Sunyaev SR;Kaul R;Stamatoyannopoulos JA
通讯作者：
Stamatoyannopoulos JA
DOI：
10.1093/database/bax028
发表时间：
2017-01-01
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Fishilevich S;Nudel R;Rappaport N;Hadar R;Plaschkes I;Iny Stein T;Rosen N;Kohn A;Twik M;Safran M;Lancet D;Cohen D
通讯作者：
Cohen D