Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites.
Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites.
复制标题
整合基因组序列和结构数据进行统计学习以预测转录因子结合位点
DOI：
10.1093/nar/gkaa1134
复制
发表时间：
2020-12-16
影响因子：
14.9
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Long P;Zhang L;Huang B;Chen Q;Liu H
摘要我们报道了一种预测四环素阻遏物（TetR）家族转录调节子（TFR）DNA特异性的方法。首先，基于基因组序列的方法被简化，定义了定量P值以筛选出可靠的预测。然后，引入了一个框架，结合结构数据和训练统计能量函数，以基于序列对TFR和TFR结合位点（TFBS）之间的配对进行评分。以实验为基准，通过基于基因组序列或基于统计能量的方法，30个TFR中有29个的TFBS被正确预测。使用P值或Z值作为指标，我们估计59.6%的TFR被两种方法中至少一种方法的相对可靠的预测所覆盖，而仅基于基因组序列的方法仅覆盖28.7%。我们的方法预测了大量的新的TFB，不能正确地从公共数据库，如FootprintDB检索。高通量实验分析表明，统计能量可以可靠地模拟大量TFR的TFBS。因此，能量函数可以应用于探索相应基因组中的新TFBS。这是可能的，我们的方法扩展到其他转录因子家族与足够的结构信息。
Abstract We report an approach to predict DNA specificity of the tetracycline repressor (TetR) family transcription regulators (TFRs). First, a genome sequence-based method was streamlined with quantitative P-values defined to filter out reliable predictions. Then, a framework was introduced to incorporate structural data and to train a statistical energy function to score the pairing between TFR and TFR binding site (TFBS) based on sequences. The predictions benchmarked against experiments, TFBSs for 29 out of 30 TFRs were correctly predicted by either the genome sequence-based or the statistical energy-based method. Using P-values or Z-scores as indicators, we estimate that 59.6% of TFRs are covered with relatively reliable predictions by at least one of the two methods, while only 28.7% are covered by the genome sequence-based method alone. Our approach predicts a large number of new TFBs which cannot be correctly retrieved from public databases such as FootprintDB. High-throughput experimental assays suggest that the statistical energy can model the TFBSs of a significant number of TFRs reliably. Thus the energy function may be applied to explore for new TFBSs in respective genomes. It is possible to extend our approach to other transcriptional factor families with sufficient structural information.
登录
查看更多内容
影响因子：
14.8
作者：
Meng, Xiangdong;Wolfe, Scot A.
通讯作者：
Wolfe, Scot A.
影响因子：
4.4
作者：
Francke C;Kerkhoven R;Wels M;Siezen RJ
通讯作者：
Siezen RJ
准确识别原核基因组中正确长度的顺式调节基序。
影响因子：
14.9
作者：
Li G;Liu B;Xu Y
通讯作者：
Xu Y
影响因子：
6.8
作者：
Liu LA;Bradley P
通讯作者：
Bradley P
DOI：
10.1073/pnas.1111471108
发表时间：
2011-12-06
影响因子：
11.1
作者：
Morcos, Faruck;Pagnani, Andrea;Weigt, Martin
通讯作者：
Weigt, Martin