fourSig: a method for determining chromosomal interactions in 4C-Seq data

Williams, Rex L.; Starmer, Joshua; Mugford, Joshua W.; Calabrese, J. Mauro; Mieczkowski, Piotr A.; Yee, Della; Magnuson, Terry

doi:10.1093/nar/gku156

Cited by 42 publications

(56 citation statements)

References 43 publications

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“…It uses BAM alignment files as input, and performs aligned reads counting, read count normalization, statistical analysis of interactions and data visualization or data export of contacting regionsHiTCServant, 2012 [130]R package. It allows the use of both 5C and Hi-C data and offers quality control, normalization and visualization of heatmapsfourSigWilliams, 2014 [131]R package. Includes fragment filtering optionsBasic4CseqWalter, 2014 [132]R package.…”

Section: Bioinformatics Challengesmentioning

confidence: 99%

In the loop: promoter–enhancer interactions and bioinformatics

Mora¹,

Sandve²,

Gabrielsen³

et al. 2015

Brief Bioinform

109

104

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Enhancer–promoter regulation is a fundamental mechanism underlying differential transcriptional regulation. Spatial chromatin organization brings remote enhancers in contact with target promoters in cis to regulate gene expression. There is considerable evidence for promoter–enhancer interactions (PEIs). In the recent years, genome-wide analyses have identified signatures and mapped novel enhancers; however, being able to precisely identify their target gene(s) requires massive biological and bioinformatics efforts. In this review, we give a short overview of the chromatin landscape and transcriptional regulation. We discuss some key concepts and problems related to chromatin interaction detection technologies, and emerging knowledge from genome-wide chromatin interaction data sets. Then, we critically review different types of bioinformatics analysis methods and tools related to representation and visualization of PEI data, raw data processing and PEI prediction. Lastly, we provide specific examples of how PEIs have been used to elucidate a functional role of non-coding single-nucleotide polymorphisms. The topic is at the forefront of epigenetic research, and by highlighting some future bioinformatics challenges in the field, this review provides a comprehensive background for future PEI studies.

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Section: Bioinformatics Challengesmentioning

confidence: 99%

In the loop: promoter–enhancer interactions and bioinformatics

Mora¹,

Sandve²,

Gabrielsen³

et al. 2015

Brief Bioinform

109

104

View full text Add to dashboard Cite

show abstract

“…The methods used for statistical analysis are detailed elsewhere (WILLIAMS JR. et al 2014), though they are based upon previously published methods (SPLINTER et al 2011). What follows is a basic outline of our method.…”

Section: Statistical Analysis Of Mapped Readsmentioning

confidence: 99%

Evidence for Local Regulatory Control of Escape from Imprinted X Chromosome Inactivation

Mugford¹,

Starmer²,

Williams

et al. 2014

Genetics

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X chromosome inactivation (XCI) is an epigenetic process that almost completely inactivates one of two X chromosomes in somatic cells of mammalian females. A few genes are known to escape XCI and the mechanism for this escape remains unclear. Here, using mouse trophoblast stem (TS) cells, we address whether particular chromosomal interactions facilitate escape from imprinted XCI. We demonstrate that promoters of genes escaping XCI do not congregate to any particular region of the genome in TS cells. Further, the escape status of a gene was uncorrelated with the types of genomic features and gene activity located in contacted regions. Our results suggest that genes escaping imprinted XCI do so by using the same regulatory sequences as their expressed alleles on the active X chromosome. We suggest a model where regulatory control of escape from imprinted XCI is mediated by genomic elements located in close linear proximity to escaping genes.T HE three-dimensional shape of chromosomes has a direct impact upon gene regulation, as chromatin looping mediates the interaction of enhancers with transcriptional start sites (TSSs) (Lieberman-Aiden et al. 2009;Li and Reinberg 2011;Krivega and Dean 2012). Analysis of genome-wide interactions suggests that chromosomes self-organize into topologically associated domains (TADs) that are 0.8-1 Mb in linear length (Dixon et al. 2012). Loci within a TAD are more likely to interact with each other as opposed to forming interactions with loci residing in other TADs.Chromosomal interactions are thought to play a pivotal role in the epigenetic process of X chromosome inactivation (XCI) (Splinter et al. 2011). During XCI, mammalian females transcriptionally inactivate one X chromosome (Xi) per somatic cell to balance X-linked gene dosage with males (Chow and Heard 2009). Whereas genes on the active X chromosome (Xa) are thought to form stable interactions with other loci on the Xa (cis) and other chromosomes (trans), the interactions formed by Xi-linked loci are relatively less established, suggesting that Xi chromatin folds in a random manner (Splinter et al. 2011).In the mouse, two forms of XCI are observed: imprinted XCI and random XCI. Imprinted XCI occurs within extra-embryonic tissues and is characterized by the exclusive inactivation of the paternally derived X chromosome (Xp) (Takagi and Sasaki 1975). Random XCI occurs within somatic tissues of the developing embryo and adult (Lyon 1961). While imprinted and random XCI may initiate via distinct mechanisms (Kalantry et al. 2009), the genetic programs required for the maintenance of both forms appear similar (Marahrens et al. 1997;Kalantry et al. 2006;Jonkers et al. 2009; Shin et al. 2010).Interestingly, a few genes are known to escape both imprinted and random XCI and are expressed from both X chromosomes (Berletch et al. 2011;. Profiles of XCI escape vary among different cell types; the number of escape genes, termed escapers, ranges from 3% to 25% of all X-linked genes (Berletch et al. 2011 To date, no study has c...

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“…To deal with this issue some of the available pipelines transform the data to a binary signal (a score of zero or one) based on the presence or absence of a read at each restriction enzyme fragment[6, 11, 12]. To investigate the extent of the PCR amplification bias a recent study incorporated random barcodes in their experimental strategy and found no selective bias in their usage[13]. This demonstrates that binary transformation removes information that can be obtained from the number of captured interactions at a given restriction enzyme fragment.…”

mentioning

confidence: 99%

“…To account for this, a genomic window is typically used to analyze interactions as opposed to dealing with individual fragments. [6, 11, 13, 15]. However, it should be noted that the use of arbitrary window sizes may obscure the boundary of interacting domains that can be detected so it is important to first determine the appropriate size.…”

mentioning

confidence: 99%