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First author: Xuelei Lai; Affiliations: Univ. Grenoble Alpes (格勒诺布尔-阿尔卑斯大学): Grenoble, France
Corresponding author: François Parcy
Transcription factors (TF) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBS), and thus are targeted to specific regions of the genome where they can recruit transcriptional cofactors and/or chromatin regulators for fine-tuning spatiotemporal gene regulation. Therefore, the identification of TFBS in genomic sequences and their subsequent quantitative modeling (定量建模) is of crucial importance for understanding and predicting gene expression. Here, we review how TFBS can be determined experimentally, how the TFBS models can be constructed in silico (生物信息学), and how they can be optimized by taking into account features such as position interdependence (相互依存) within TFBSs, DNA shape and/or by introducing state-of-the-art computational algorithms such as deep learning methods. In addition, we discuss the integration of context variables into the TFBS modeling, including nucleosome positioning (核小体定位), chromatin states, methylation patterns, 3D genome architectures and TF cooperative binding, in order to better predict TF binding under cellular contexts. Finally, we explore the possibilities of combining the optimized TFBS model with technological advances such as targeted TFBS perturbation (扰动) by CRISPR to better understand gene regulation, evolution and plant diversity.
转录因子是控制基因表达的重要细胞组份。转录因子识别特异的DNA序列,叫做转录因子结合位点TFBS,因此能够靶定到基因组特异的区域,并招募转录共因子或者染色质调控子以精细调控基因的时空表达。因此,鉴定基因组序列上的TFBS及随后的定量建模对于理解和预测基因的表达是非常重要的。本文,作者综述了TFBS是如何通过试验来验证的,TFBS是如何通过生物信息学预测的,以及这些都是如何通过结合TFBS间相互依存、DNA特征和引入深度学习模型等最先进计算算法进一步优化的。另外,作者讨论了用于TFBS建模的基因组特征整合,主要包括核小体定位、染色质状态、甲基化模式、3D基因组结构及TF协同结合,通过这样来更好的预测细胞中的转录因子结合。最终,作者探索了结合最优TFBS模型和CRISPR等靶向干扰TFBS技术进步来更好地进行基因调控、演化及植物多样性研究。
通讯:François Parcy (http://big.cea.fr/drf/big/english/Pages/PCV/RDF/Welcome.aspx)
研究方向:利用花发育作为模式生物学进程研究转录因子互作、染色质重塑蛋白、转录因子对特异DNA序列的识别建模以及调控网络演化等基础生物学问题。
doi: https://doi.org/10.1016/j.molp.2018.10.010
Journal: Molecular Plant
Published date: 16 November, 2018
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