Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Hoffman, Michael M.; Buske, Orion J.; Wang, Jie; Weng, Zhiping; Bilmes, Jeff; Noble, William Stafford

doi:10.1145/2506583.2506701

Cited by 298 publications

(169 citation statements)

References 32 publications

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“…Annotation involved processing the raw data for chromatin accessibility (DNAse hypersensitivity) along with ChIP-seq data for six histone modifications (H3K4me1, H3K27me3, H3K27ac, H3K4me3, H3K36me3 and H3K9me3), followed by use of the Segway algorithm [27] to predict seven features as previously described [10]. Annotation was generated by analysing enrichment of each feature for the different chromatin tracks with a one-way proportion test performed with continuity correction of the observed vs expected proportion.…”

Section: Methodsmentioning

confidence: 99%

DNA hypermethylation of CD3+ T cells from cord blood of infants exposed to intrauterine growth restriction

et al. 2016

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Aims/hypothesis Intrauterine growth restriction (IUGR) is associated with increased susceptibility to obesity, metabolic syndrome and type 2 diabetes. Although the mechanisms underlying the developmental origins of metabolic disease are poorly understood, evidence suggests that epigenomic alterations play a critical role. We sought to identify changes in DNA methylation patterns that are associated with IUGR in CD3 + T cells purified from umbilical cord blood obtained from male newborns who were appropriate for gestational age (AGA) or who had been exposed to IUGR. Methods CD3 + T cells were isolated from cord blood obtained from IUGR and AGA infants. The genome-wide methylation profile in eight AGA and seven IUGR samples was determined using the HELP tagging assay. Validation analysis using targeted bisulfite sequencing and bisulfite massARRAY was performed on the original cohort as well as biological replicates consisting of two AGA and four IUGR infants. The Segway algorithm was used to identify methylation changes within regulatory regions of the genome. Results A global shift towards hypermethylation in IUGR was seen compared with AGA (89.8% of 4,425 differentially methylated loci), targeted to regulatory regions of the genome, specifically promoters and enhancers. Pathway analysis identified dysregulation of pathways involved in metabolic disease (type 2 diabetes mellitus, insulin signalling, mitogen-activated protein kinase signalling) and T cell development, regulation and activation (T cell receptor signalling), as well as transcription factors (TCF3, LEF1 and NFATC) that regulate T cells. Furthermore, bump-hunting analysis revealed differentially methylated regions in PRDM16 and HLA-DPB1, genes important for adipose tissue differentiation, stem cell maintenance and function and T cell activation. Conclusions/interpretation Our findings suggest that the alterations in methylation patterns observed in IUGR CD3 + T cells may have functional consequences in targeted genes, regulatory regions and transcription factors. These may serve as biomarkers to identify those at 'high risk' for diminished attainment of full health potential who can benefit from early interventions.

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Section: Methodsmentioning

confidence: 99%

DNA hypermethylation of CD3+ T cells from cord blood of infants exposed to intrauterine growth restriction

et al. 2016

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“…Regulatory elements such as promoters, enhancers, insulators, transcribed, and repressed regions are marked by distinct patterns of histone modifications (1), including histone H3 lysine 27 acetylation (H3K27ac), H3K27 trimethylation (H3K27me3), H3K36me3, H3K4 monomethylation (H3K4me1), H3K4me3, and the CCCTC-binding factor (CTCF). Systematic chromatin state identification has recently emerged as a powerful technique to interpret and compare regulatory landscapes within and between cell types (2)(3)(4)(5)(6)(7). Such methods use an unsupervised approach to identify recurrent combinations of histone modifications across the genome, thereby producing a map of representative chromatin states that are likely to be biologically relevant.…”

mentioning

confidence: 99%

Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants

Parker

Stitzel

Taylor

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

637

672

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Chromatin-based functional genomic analyses and genomewide association studies (GWASs) together implicate enhancers as critical elements influencing gene expression and risk for common diseases. Here, we performed systematic chromatin and transcriptome profiling in human pancreatic islets. Integrated analysis of islet data with those from nine cell types identified specific and significant enrichment of type 2 diabetes and related quantitative trait GWAS variants in islet enhancers. Our integrated chromatin maps reveal that most enhancers are short (median = 0.8 kb). Each cell type also contains a substantial number of more extended (≥3 kb) enhancers. Interestingly, these stretch enhancers are often tissue-specific and overlap locus control regions, suggesting that they are important chromatin regulatory beacons. Indeed, we show that (i) tissue specificity of enhancers and nearby gene expression increase with enhancer length; (ii) neighborhoods containing stretch enhancers are enriched for important cell type-specific genes; and (iii) GWAS variants associated with traits relevant to a particular cell type are more enriched in stretch enhancers compared with short enhancers. Reporter constructs containing stretch enhancer sequences exhibited tissue-specific activity in cell culture experiments and in transgenic mice. These results suggest that stretch enhancers are critical chromatin elements for coordinating cell type-specific regulatory programs and that sequence variation in stretch enhancers affects risk of major common human diseases.

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“…ChIP is also widely used to study patterns of histone modifications and chromatin modifiers [63,76]. It can be integrated to other data sets, as with Segway [77], helping development of chromatin model [78]. ChIP coupled with quantitative real-time PCR allows the study of the dynamics of DNA and proteins interactions in living cells for up to several minutes, and has now been adapted to microfluidics technology reducing the number of cells and time required [79].…”

Section: Epigenomicsmentioning

confidence: 99%

Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

et al. 2013

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This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describes approaches used in genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis. Methods for clustering, feature selection, prediction analysis, text mining and pathway analysis used to analyse and integrate the data produced are then presented.

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Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Cited by 298 publications

References 32 publications

DNA hypermethylation of CD3+ T cells from cord blood of infants exposed to intrauterine growth restriction

DNA hypermethylation of CD3+ T cells from cord blood of infants exposed to intrauterine growth restriction

Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants

Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

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