In mammalian cells, transcribed enhancers (TrEns) play important roles in the initiation of gene expression and maintenance of gene expression levels in a spatiotemporal manner. One of the most challenging questions is how the genomic characteristics of enhancers relate to enhancer activities. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers’ DNA code in a more systematic way. To address this problem, we developed a novel computational framework, Transcribed Enhancer Landscape Search (TELS), aimed at identifying predictive cell type/tissue-specific motif signatures of TrEns. As a case study, we used TELS to compile a comprehensive catalog of motif signatures for all known TrEns identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that combinations of different short motifs characterize in an optimized manner cell type/tissue-specific TrEns. Our study is the first to report combinations of motifs that maximize classification performance of TrEns exclusively transcribed in one cell type/tissue from TrEns exclusively transcribed in different cell types/tissues. Moreover, we also report 31 motif signatures predictive of enhancers’ broad activity. TELS codes and material are publicly available at http://www.cbrc.kaust.edu.sa/TELS.
在哺乳动物细胞中，转录增强子(TrEns)在起始基因表达和维持基因表达水平的过程中发挥着重要作用。由于增强子与启动子在转录激活方面行使功能的相似性，科学家很难将增强子和启动子清晰地分开。因此，破译增强子的基因组特征有助于更好地理解增强子的功能以及增强子与启动子间的差异。到目前为止，对于增强子序列特征的研究十分匮乏。为了解决这个问题，我们开发了一种基于机器学习的方法——转录增强子模式搜索(TELS)，用以识别人类基因组中转录增强子短序列motif最优的组合模式，该方法使用逻辑回归(LR)和降维算法。在本研究中，我们使用由CAGE实验确定的涵盖了112个人类初级细胞和组织的转录增强子的序列作为训练样本。该方法只利用已知转录增强子的motifs序列信息，而不需要增强子的活性信息，就可以区分细胞特异性增强子以及非特异性增强子。此外，我们还发现了具有非细胞特异性增强子活性预测性能的31个motif信号。TELS代码和数据可在http://www.cbrc.kaust.edu.sa/TELS 上公开获取。





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