Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with motif analysis and deep learning

ABSTRACT: Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or cancer genome, to identify only those variants that impact enhancer function, remains a major challenge. Prioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact of a mutation on gene regulation. Here we apply a specialized deep learning model to 10 phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

ORGANISM(S): Homo sapiens

PROVIDER: GSE159965 | GEO | 2021/03/03

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

The EN-TEx Resource for Personal Functional Genomics

Project description:An overall goal of functional genomics has been to measure the impact of variants on molecular endophenotypes (e.g. gene expression levels or the degree of TF binding) and relate this to organismal traits and disease phenotypes. However, all the experiments to date have been described relative to a generic reference genome, significantly hobbling their interpretation. Here, we describe a strategy for finding significant relationships between disease variation and genomic annotation via personal functional genomics, by performing personal genome sequencing and paired functional genomics experiments, on the same individual.

2024-05-22 | PXD022787 | Pride

Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with motif analysis and deep learning

Project description:Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with motif analysis and deep learning

| PRJNA670968 | ENA

Machine learning sequence prioritization for cell type-specific enhancer design

Project description:Machine learning sequence prioritization for cell type-specific enhancer design

| PRJNA720048 | ENA

Individuality and variation of personal regulomes in human T cells

Project description:We surveyed the variation and dynamics of active regulatory elements genome-wide in CD4+ T cells, using Assay of Transposase Accessible Chromatin with sequencing (ATAC-seq) in longitudinal samples from healthy volunteers and during T cell activation. We created robust pipelines that enable accurate single molecule counting and allelic discrimination from clinical material. Over 4000 regulatory elements (7.2%) showed reproducible personal variation in activity. Gender was the most significant attributable source of regulome variation. ATAC-seq revealed novel elements that escape X chromosome inactivation and predicted gender-specific gene regulatory networks across autosomes, which coordinately impact genes with immune function. Noisy regulatory elements with personal variation in accessibility are significantly enriched for autoimmune disease loci. Over one third of regulome variation lacked genetic variation in cis, suggesting contributions from environmental or epigenetic factors. These results refine concepts of human individuality and provide foundational reference to compare disease-associated regulomes.

2015-06-23 | GSE60682 | GEO

Characterization of sequence determinants of enhancer function using natural genetic variation [ChIP-seq]

Project description:Sequence variation in enhancers, a class of cis-regulatory elements that control cell type-specific gene transcription, contributes significantly to phenotypic variation within human populations. Enhancers are short DNA sequences (∼200 bp) composed of multiple binding sites (4-10 bp) for transcription factors (TFs). The transcriptional regulatory activity of an enhancer is encoded by the type, number, and distribution of TF binding sites that it contains. However, the sequence determinants of TF binding to enhancers and the relationship between TF binding and enhancer activity are complex, and thus it remains difficult to predict the effect of any given sequence variant on enhancer function. Here, we generate allele-specific maps of TF binding and enhancer activity in fibroblasts from a panel of F1-hybrid mice that have a high frequency of sequence variants. We identified thousands of enhancers that exhibit differences in TF binding and/or activity between alleles and use these data to define features of sequence variants that are most likely to impact enhancer function. Our data demonstrate a critical role for AP-1 TFs at many fibroblast enhancers, reveal a hierarchical relationship between AP-1 and TEAD TF binding at enhancers, and delineate the nature of sequence variants that contribute to AP-1 TF binding. These data represent one of the most comprehensive assessments to date of the impact of sequence variation on enhancer function in chromatin, with implications for identifying functional cis-regulatory variation in human populations.

2022-01-17 | GSE193727 | GEO