ArticlePLoS computational biology2016
Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Single-cell multiomics analysis reveals CTCF as a key regulator of lung morphogenesis and progenitor maintenance.Nature communications · 2025Article
- ZNF143 is a transcriptional regulator of nuclear-encoded mitochondrial genes that acts independently of looping and CTCF.Molecular cell · 2025Article
- Putative looping factor ZNF143/ZFP143 is an essential transcriptional regulator with no looping function.Molecular cell · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A Functional Survey of the Regulatory Landscape of Estrogen Receptor-Positive Breast Cancer Evolution.Cancer discovery · 2024Article
- Article
- Topologically associating domain boundaries are required for normal genome function.Communications biology · 2023Article
- The chromatin factor ROW cooperates with BEAF-32 in regulating long-range inducible genes.EMBO reports · 2022Article
- Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation.Nature communications · 2022Article
- TADreg: a versatile regression framework for TAD identification, differential analysis and rearranged 3D genome prediction.BMC bioinformatics · 2022Article
- Mechanisms of CP190 Interaction with Architectural Proteins in Drosophila Melanogaster.International journal of molecular sciences · 2021Article
- The emerging role of chromatin remodelers in neurodevelopmental disorders: a developmental perspective.Cellular and molecular life sciences : CMLS · 2021Review
- Article
- MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data.PLoS computational biology · 2020Article
- Two paralogous znf143 genes in zebrafish encode transcriptional activator proteins with similar functions but expressed at different levels during early development.BMC molecular and cell biology · 2020Article
- ZNF143 in Chromatin Looping and Gene Regulation.Frontiers in genetics · 2020Review
- Three-dimensional organization and dynamics of the genome.Cell biology and toxicology · 2018Review
- How epigenome drives chromatin folding and dynamics, insights from efficient coarse-grained models of chromosomes.PLoS computational biology · 2018Article
- TAD-free analysis of architectural proteins and insulators.Nucleic acids research · 2018Article
- Brca2, Pds5 and Wapl differentially control cohesin chromosome association and function.PLoS genetics · 2018Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1.
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