A complex network framework for unbiased statistical analyses of DNA–DNA contact maps

Kruse, Kai; Sewitz, Sven; Babu, M. Madan

doi:10.1093/nar/gks1096

Cited by 33 publications

(40 citation statements)

References 48 publications

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“…Then, using active transcription, Pol II would distribute cohesin on chromosomes, extruding DNA in the process to form loops and TADs (Busslinger et al 2017). This model is corroborated by other network analyses suggesting a primary role for active Pol II and cohesin in the formation of chromatin-chromatin interactions (Kruse et al 2013;Pancaldi et al 2016;Azofeifa and Dowell 2017). Interestingly, NIPBL seems to favor promoter-promoter interactions (Pancaldi et al 2016).…”

Section: Discussionsupporting

confidence: 60%

Connected Gene Communities Underlie Transcriptional Changes in Cornelia de Lange Syndrome

et al. 2017

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Cornelia de Lange syndrome (CdLS) is a complex multisystem developmental disorder caused by mutations in cohesin subunits and regulators. While its precise molecular mechanisms are not well defined, they point toward a global deregulation of the transcriptional gene expression program. Cohesin is associated with the boundaries of chromosome domains and with enhancer and promoter regions connecting the three-dimensional genome organization with transcriptional regulation. Here, we show that connected gene communities, structures emerging from the interactions of noncoding regulatory elements and genes in the three-dimensional chromosomal space, provide a molecular explanation for the pathoetiology of CdLS associated with mutations in the cohesin-loading factor and the cohesin subunit NIPBL and cohesin are important constituents of connected gene communities that are centrally positioned at noncoding regulatory elements. Accordingly, genes deregulated in CdLS are positioned within reach of NIPBL- and cohesin-occupied regions through promoter-promoter interactions. Our findings suggest a dynamic model where NIPBL loads cohesin to connect genes in communities, offering an explanation for the gene expression deregulation in the CdLS.

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Section: Discussionsupporting

confidence: 60%

Connected Gene Communities Underlie Transcriptional Changes in Cornelia de Lange Syndrome

et al. 2017

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“…CUFS correlates better with 3DGD than the genes' GC content. In SC, GC content was reported to be correlated with recombination frequency 47 , while crossover recombination sites were reported to be enriched in Hi-C contacts 20 . In addition, centromeres have been reported to be strongly co-localized 12 , and have also been characterized as having low GC content 48 .…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, recently a correlation has been suggested between transcription factor network models and distances between average chromosome positions in human cells 19 . A series of studies further reported the co-localization of each of the following groups in SC: cohesin binding sites 20 , co-expressed genes, some identified GO terms 21 and gene targets of the same transcription factor 22 . Recently, an analysis of the genomic organization of the unique P. falciparum parasite throughout its cell cycle confirmed a relation between chromosome conformation and gene expression 18 .…”

mentioning

confidence: 99%

Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function

2014

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It has been shown that the distribution of genes in eukaryotic genomes is not random; however, formerly reported relations between gene function and genomic organization were relatively weak. Previous studies have demonstrated that codon usage bias is related to all stages of gene expression and to protein function. Here we apply a novel tool for assessing functional relatedness, codon usage frequency similarity (CUFS), which measures similarity between genes in terms of codon and amino acid usage. By analyzing chromosome conformation capture data, describing the three-dimensional (3D) conformation of the DNA, we show that the functional similarity between genes captured by CUFS is directly and very strongly correlated with their 3D distance in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, mouse and human. This emphasizes the importance of threedimensional genomic localization in eukaryotes and indicates that codon usage is tightly linked to genome architecture.

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“…Although some studies about the analysis of long-range interaction networks have been presented [34,35], current approaches to Hi-C data analysis mostly rely on the conversion of information into contact maps, which are matrices of pair wise contact frequencies along the genome. Also data normalization is performed directly on the contact maps, with the aim of filtering out biases caused by fragment length, mappability, and GC-content.…”

Section: Introductionmentioning

confidence: 99%

NuChart: An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations

2013

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Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expression and important links have been highlighted with other genomic features involved in DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between colocalization and coregulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. Here, we describe NuChart, an R package that allows the user to annotate and statistically analyse a list of input genes with information relying on Hi-C data, integrating knowledge about genomic features that are involved in the chromosome spatial organization. NuChart works directly with sequenced reads to identify the related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. Predictions about CTCF binding sites, isochores and cryptic Recombination Signal Sequences are provided directly with the package for mapping, although other annotation data in bed format can be used (such as methylation profiles and histone patterns). Gene expression data can be automatically retrieved and processed from the Gene Expression Omnibus and ArrayExpress repositories to highlight the expression profile of genes in the identified neighbourhood. Moreover, statistical inferences about the graph structure and correlations between its topology and multi-omics features can be performed using Exponential-family Random Graph Models. The Hi-C fragment visualisation provided by NuChart allows the comparisons of cells in different conditions, thus providing the possibility of novel biomarkers identification. NuChart is compliant with the Bioconductor standard and it is freely available at ftp://fileserver.itb.cnr.it/nuchart.

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A complex network framework for unbiased statistical analyses of DNA–DNA contact maps

Cited by 33 publications

References 48 publications

Connected Gene Communities Underlie Transcriptional Changes in Cornelia de Lange Syndrome

Connected Gene Communities Underlie Transcriptional Changes in Cornelia de Lange Syndrome

Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function

NuChart: An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations

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