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Creators/Authors contains: "Zhang, Ruochi"

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  1. Abstract Motivation

    The three dimensional organization of chromosomes within the cell nucleus is highly regulated. It is known that CCCTC-binding factor (CTCF) is an important architectural protein to mediate long-range chromatin loops. Recent studies have shown that the majority of CTCF binding motif pairs at chromatin loop anchor regions are in convergent orientation. However, it remains unknown whether the genomic context at the sequence level can determine if a convergent CTCF motif pair is able to form a chromatin loop.

    Results

    In this article, we directly ask whether and what sequence-based features (other than the motif itself) may be important to establish CTCF-mediated chromatin loops. We found that motif conservation measured by ‘branch-of-origin’ that accounts for motif turn-over in evolution is an important feature. We developed a new machine learning algorithm called CTCF-MP based on word2vec to demonstrate that sequence-based features alone have the capability to predict if a pair of convergent CTCF motifs would form a loop. Together with functional genomic signals from CTCF ChIP-seq and DNase-seq, CTCF-MP is able to make highly accurate predictions on whether a convergent CTCF motif pair would form a loop in a single cell type and also across different cell types. Our work represents an important step further to understand the sequence determinants that may guide the formation of complex chromatin architectures.

    Availability and implementation

    The source code of CTCF-MP can be accessed at: https://github.com/ma-compbio/CTCF-MP

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  2. Abstract Motivation

    A large number of distal enhancers and proximal promoters form enhancer–promoter interactions to regulate target genes in the human genome. Although recent high-throughput genome-wide mapping approaches have allowed us to more comprehensively recognize potential enhancer–promoter interactions, it is still largely unknown whether sequence-based features alone are sufficient to predict such interactions.

    Results

    Here, we develop a new computational method (named PEP) to predict enhancer–promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. The two modules in PEP (PEP-Motif and PEP-Word) use different but complementary feature extraction strategies to exploit sequence-based information. The results across six different cell types demonstrate that our method is effective in predicting enhancer–promoter interactions as compared to the state-of-the-art methods that use functional genomic signals. Our work demonstrates that sequence-based features alone can reliably predict enhancer–promoter interactions genome-wide, which could potentially facilitate the discovery of important sequence determinants for long-range gene regulation.

    Availability and Implementation

    The source code of PEP is available at: https://github.com/ma-compbio/PEP.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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