Transcriptional regulation is critical to cellular processes of all organisms. Regulatory mechanisms often involve more than one transcription factor (TF) from different families, binding together and attaching to the DNA as a single complex. However, only a fraction of the regulatory partners of each TF is currently known. In this paper, we present the Transcriptional Interaction and Coregulation Analyzer (TICA), a novel methodology for predicting heterotypic physical interaction of TFs. TICA employs a data-driven approach to infer interaction phenomena from chromatin immunoprecipitation and sequencing (ChIP-seq) data. Its prediction rules are based on the distribution of minimal distance couples of paired binding sites belonging to different TFs which are located closest to each other in promoter regions. Notably, TICA uses only binding site information from input ChIP-seq experiments, bypassing the need to do motif calling on sequencing data. We present our method and test it on ENCODE ChIP-seq datasets, using three cell lines as reference including HepG2, GM12878, and K562. TICA positive predictions on ENCODE ChIP-seq data are strongly enriched when compared to protein complex (CORUM) and functional interaction (BioGRID) databases. We also compare TICA against both motif/ChIP-seq based methods for physical TF–TF interaction prediction and published literature. Based on our results, TICA offers significant specificity (average 0.902) while maintaining a good recall (average 0.284) with respect to CORUM, providing a novel technique for fast analysis of regulatory effect in cell lines. Furthermore, predictions by TICA are complementary to other methods for TF–TF interaction prediction (in particular, TACO and CENTDIST). Thus, combined application of these prediction tools results in much improved sensitivity in detecting TF–TF interactions compared to TICA alone (sensitivity of 0.526 when combining TICA with TACO and 0.585 when combining with CENTDIST) with little compromise in specificity (specificity 0.760 when combining with TACO and 0.643 with CENTDIST). TICA is publicly available at http://geco.deib.polimi.it/tica/.
对于生物体来说，转录调控是细胞活动至关重要的部分。而转录因子（TF）是参与基因转录起始和调控的蛋白质。通常情况下，转录调控作用不只是由一个转录因子完成，而是涉及到来自不同家族的多个转录因子，他们会结合在一起，形成一个复合物附着在DNA上，从而影响基因的转录活动。然而，目前仅有小部分TF相互作用关系被人类所知晓。因此，本文提出了一种新的预测TF物理相互作用的方法——转录互作及共调控关系分析(TICA)。TICA利用染色质免疫共沉淀技术测序(ChIP-seq)数据，并从中推断TF间相互作用。这些相互作用包括TFs之间的直接绑定、同一复合体中没有直接接触的TFs间相互作用、以及相互作用后阻碍其他TFs与另一半结合的相互作用。该方法的预测原理是，在启动子区域内寻找相邻两个TF的基因组距离显著小于随机TF组合的基因组距离作为预测结果。值得一提的是，TICA只使用来自ChIP-seq实验的数据作为输入信息，而不需要对数据进行motif预测。在这项研究中，我们使用HepG2、GM12878、K562三种细胞系的ENCODE ChIP-seq 数据集对软件进行测试，并将预测结果与蛋白质复合物(CORUM)以及功能相互作用(BioGRID)数据库中的数据进行比较，比较结果说明TICA预测结果具有很高的准确性。同时，我们也将TICA与其他预测TF互作的方法进行比较。比较结果显示TICA在保持与CORUM较好地一致性（平均0.284）的同时，也具有很高的特异性(平均0.902)，因此TICA可以作为一种快速分析细胞系的调控网络的新方法或其他预测TF相互作用软件的补充。TICA可以在 http://geco.deib.polimi.it/tica/ 上公开获取。





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