Dataset Information

Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features.

ABSTRACT: The CCCTC-binding zinc-finger protein (CTCF)-mediated network of long-range chromatin interactions is important for genome organization and function. Although this network has been considered largely invariant, we find that it exhibits extensive cell-type-specific interactions that contribute to cell identity. Here, we present Lollipop, a machine-learning framework, which predicts CTCF-mediated long-range interactions using genomic and epigenomic features. Using ChIA-PET data as benchmark, we demonstrate that Lollipop accurately predicts CTCF-mediated chromatin interactions both within and across cell types, and outperforms other methods based only on CTCF motif orientation. Predictions are confirmed computationally and experimentally by Chromatin Conformation Capture (3C). Moreover, our approach identifies other determinants of CTCF-mediated chromatin wiring, such as gene expression within the loops. Our study contributes to a better understanding about the underlying principles of CTCF-mediated chromatin interactions and their impact on gene expression.

SUBMITTER: Kai Y

PROVIDER: S-EPMC6181989 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features.

Kai Yan Y Andricovich Jaclyn J Zeng Zhouhao Z Zhu Jun J Tzatsos Alexandros A Peng Weiqun W

Nature communications 20181011 1

The CCCTC-binding zinc-finger protein (CTCF)-mediated network of long-range chromatin interactions is important for genome organization and function. Although this network has been considered largely invariant, we find that it exhibits extensive cell-type-specific interactions that contribute to cell identity. Here, we present Lollipop, a machine-learning framework, which predicts CTCF-mediated long-range interactions using genomic and epigenomic features. Using ChIA-PET data as benchmark, we de ...[more]

PMID: 30310060

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Project description:MotivationMicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction. However, most of current computational methods that combine sequence and expression information do not effectively integrate full spectrum of these features; instead, they perceive putative miRNA-mRNA interactions from sequence-based prediction as equally meaningful. Therefore, these sequence features have not been fully utilized for improving miRNA target prediction.ResultsWe propose a novel regularized regression approach that is based on the adaptive Lasso procedure for detecting functional miRNA-mRNA interactions. Our method fully takes into account the gene sequence features and the miRNA and mRNA expression profiles. Given a set of sequence features for each putative miRNA-mRNA interaction and their expression values, our model quantifies the down-regulation effect of each miRNA on its targets while simultaneously estimating the contribution of each sequence feature to predicting functional miRNA-mRNA interactions. By applying our model to the expression datasets from two cancer studies, we have demonstrated our prediction results have achieved better sensitivity and specificity and are more biologically meaningful compared with those based on other methods.Availability and implementationThe source code is available at: http://nba.uth.tmc.edu/homepage/liu/miRNALasso.Supplementary informationSupplementary data are available at Bioinformatics online.ContactYin.Liu@uth.tmc.edu.

Dataset Information

Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features.

Publications

Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features.

Similar Datasets

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