Dataset Information

Exploring spatially adjacent TFBS-clustered regions with Hi-C data.

ABSTRACT:

Motivation

Transcription factor binding sites (TFBSs) are clustered in the human genome, forming the TFBS-clustered regions that regulate gene transcription, which requires dynamic chromatin configurations between promoters and distal regulatory elements. Here, we propose a regulatory model called spatially adjacent TFBS-clustered regions (SATs), in which TFBS-clustered regions are connected by spatial proximity as identified by high-resolution Hi-C data.

Results

TFBS-clustered regions forming SATs appeared less frequently in gene promoters than did isolated TFBS-clustered regions, whereas SATs as a whole appeared more frequently. These observations indicate that multiple distal TFBS-clustered regions combined to form SATs to regulate genes. Further examination confirmed that a substantial portion of genes regulated by SATs were located between the paired TFBS-clustered regions instead of the downstream. We reconstructed the chromosomal conformation of the H1 human embryonic stem cell line using the ShRec3D algorithm and proposed the SAT regulatory model.

Contact

ylu.phd@gmail.com or boxc@bmi.ac.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Chen H

PROVIDER: S-EPMC5860062 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Exploring spatially adjacent TFBS-clustered regions with Hi-C data.

Chen Hebing H Jiang Shuai S Zhang Zhuo Z Li Hao H Lu Yiming Y Bo Xiaochen X

Bioinformatics (Oxford, England) 20170901 17

<h4>Motivation</h4>Transcription factor binding sites (TFBSs) are clustered in the human genome, forming the TFBS-clustered regions that regulate gene transcription, which requires dynamic chromatin configurations between promoters and distal regulatory elements. Here, we propose a regulatory model called spatially adjacent TFBS-clustered regions (SATs), in which TFBS-clustered regions are connected by spatial proximity as identified by high-resolution Hi-C data.<h4>Results</h4>TFBS-clustered re ...[more]

PMID: 28472433

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Project description:BackgroundComplex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics.ResultsHere we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics.Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks.ConclusionClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems.

Dataset Information

Exploring spatially adjacent TFBS-clustered regions with Hi-C data.

Motivation

Results

Contact

Supplementary information

Publications

Exploring spatially adjacent TFBS-clustered regions with Hi-C data.

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