Precise execution of cell-type-specific gene transcription is critical for cell differentiation and development. The accurate lineage-specific gene regulation lies in the proper combinatorial binding of transcription factors (TFs) to the cis-regulatory elements. TFs bind to the proximal DNA sequences around the genes to exert control over gene transcription. Recently, experimental studies revealed that enhancers also recruit TFs to stimulate gene expression by forming long-range chromatin interactions, suggesting the interplay between gene, enhancer, and TFs in the 3D space in specifying cell fates. Identification of transcription factor binding sites (TFBSs) as well as pinpointing the long-range chromatin interactions is pivotal for understanding the transcriptional regulatory circuits. Experimental approaches have been developed to profile protein binding as well as 3D genome but have their limitations. Therefore, accurate and highly scalable computation methods are needed to comprehensively delineate the gene regulatory landscape. Accordingly, I have developed a supervised machine learning model, TF- wave, to predict TFBSs based on DNase-Seq data. By incorporating multi-resolutions features generated by applying Wavelet Transform to DNase-Seq data, TF-wave can accurately predict TFBSs at the genome-wide level in a tissue-specific way. I further designed a matrix factorization model, EP3ICO, to jointly infer enhancer-promoter interactions based on protein-protein interactions (PPIs) between TFs with combined orders. Compared with existing algorithms, EP3ICO not only identifies underlying mechanistic regulators that mediate the 3D chromatin interactions but also achieves superior performance in predicting long-range enhancer-promoter links. In conclusion, our models provide new computational approaches for profiling the cell-type specific TF bindings and high-resolution chromatin interactions.
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