Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Direct and tissue-specific assessment of the damaging potential of regulatory SNPs

ABSTRACT: In the era of GWAS and personalized medicine systematically deciphering the impact of non-coding sequence variation on the regulatory genome is a critical bottleneck. Here we present Sasquatch a novel computational approach to estimate and visualize the effects of noncoding-variants on transcription factor (TF) binding using DNase I footprint data. Developed to provide an unbiased approach to prioritise non-coding variants for functional analysis, Sasquatch performs exhaustive, k-mer based analysis of average footprints to determine any k-mers potential for TF binding and quantifies how this is changed by sequence variants. Importantly the approach only requires one DNase-seq dataset per cell-type, from any genotype and is resilient across differing experimental procedures and sequence depths. We have made Sasquatch available as a versatile, high-throughput, webtool incorporating pre-processed human data from the Encode project and we demonstrate its effectiveness using validated causal GWAS SNPs and known polymorphic TF binding sites in erythroid tissues.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE86393 | GEO | 2017/06/15

SECONDARY ACCESSION(S): PRJNA341669

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Identification of Breast Cancer Associated Variants That Modulate Transcription Factor Binding

Project description:GWAS have discovered thousands of genomic loci that are associated with disease risk and quantitative traits, but most of the variants responsible for risk remain uncharacterized. The vast majority of GWAS-identified loci contain non-coding SNPs and defining molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter Transcription Factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. Regulatory elements for MCF10A were mapped with ATAC-seq.We identified top candidate causal SNPs that are predicted to alter TF binding, within breast cancer-relevant regulatory regions, and in strong linkage disequilibrium with the GWAS SNPs. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.

2016-12-19 | GSE89013 | GEO

Decoding the regulatory architecture of the maize leaf

Project description:Transcription factors (TF) binding is key to understanding and characterizing the effect of genetic variability on phenotypic differences. Here, we used a novel scalable ChIP-seq approach to annotate the regulatory landscape of the maize genome with binding data from 104 leaf TFs. TF binding regions co-localized with open chromatin regions, with ~70% of TF binding nearby genes. TF binding sites are evolutionarily conserved and show enrichment for GWAS-hits, cis-expression QTLs. Furthermore, the regulatory network shows characteristics of real-word networks such as scale-free topology and larger modularity than random graphs. Finally, machine-learning analyses reveal that sequence preferences are alike within TF families, and that TF co-localization is key for TF binding specificity. Our comprehensive TF-DNA interaction approach provides the starting point to decipher the gene regulatory system in plant leaves.

2020-03-01 | GSE137972 | GEO

DNase I hypersensitivity and algorithmic prediction of TF binding in early pancreatic mES directed differentiation

Project description:This dataset uses DNase-seq to profile the genome-wide DNase I hypersensitivity of mES and mES-derived cells along an early pancreatic lineage and provides the locations of putative Transcription Factor (TF) binding sites using the PIQ algorithm. DNase-seq takes advantage of the preferential cutting of DNase I in open chromatin and steric blockage of of DNase I by tightly bound TFs that protect associated genomic DNA sequences. After deep sequencing of DNase I–digested genomic DNA from intact nuclei, genome-wide data on chromatin accessibility as well as TF-specific DNase I protection profiles that reveal the genomic binding locations of a majority of TFs are obtained. Such TF signature ‘DNase profiles’ reflect the effect of the TF on DNA shape and local chromatin architecture, extending hundreds of base pairs from a TF binding site, and these profiles are centered on ‘DNase footprints’ at the binding motif itself, which reflects the biophysics of protein-DNA binding. An algorithm, PIQ, is then applied that models the specific profile of each factor, and in combination with sequence information predicts the likely binding locations of over 700 TFs genome wide.

2014-01-19 | GSE53776 | GEO

DNase I hypersensitivity and algorithmic prediction of TF binding in early pancreatic mES directed differentiation

Project description:This dataset uses DNase-seq to profile the genome-wide DNase I hypersensitivity of mES and mES-derived cells along an early pancreatic lineage and provides the locations of putative Transcription Factor (TF) binding sites using the PIQ algorithm. DNase-seq takes advantage of the preferential cutting of DNase I in open chromatin and steric blockage of of DNase I by tightly bound TFs that protect associated genomic DNA sequences. After deep sequencing of DNase IM-bM-^@M-^Sdigested genomic DNA from intact nuclei, genome-wide data on chromatin accessibility as well as TF-specific DNase I protection profiles that reveal the genomic binding locations of a majority of TFs are obtained. Such TF signature M-bM-^@M-^XDNase profilesM-bM-^@M-^Y reflect the effect of the TF on DNA shape and local chromatin architecture, extending hundreds of base pairs from a TF binding site, and these profiles are centered on M-bM-^@M-^XDNase footprintsM-bM-^@M-^Y at the binding motif itself, which reflects the biophysics of protein-DNA binding. An algorithm, PIQ, is then applied that models the specific profile of each factor, and in combination with sequence information predicts the likely binding locations of over 700 TFs genome wide. This dataset includes DNase-seq hypersensitivity data from 6 mES-derived cell types: mESC, Mesendoderm, Mesoderm, Endoderm, Intestinal Endoderm, and Prepancreatic Endoderm. For each cell type, TF binding site predictions are made based on the identification TF-specific DNase-seq profiles over any of 1331 possible binding motifs. After significance thesholding, genome-wide binding site predictions for <700 TFs are included.

2014-01-19 | E-GEOD-53776 | biostudies-arrayexpress

Explicit DNase sequence bias modeling enables high resolution transcription factor footprint detection

Project description:DNase I footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. A number of computational approaches have been developed to accurately identify DNase-seq footprints and these methods have been used as a predictor of TF-DNA interactions by itself or in combination with other epigenetic features. However, recent studies have pointed to a substantial cleavage bias of DNase and its impact on footprinting, casting doubts on its predictive performance. To assess the potential for using DNaseI to identify individual binding sites, we performed DNase-seq experiments on deproteinized naked genomic DNA isolated from two different cell types and determined sequence cleavage bias associated with the DNase-seq protocol. This allowed us to build cleavage bias corrected footprint models specific to individual transcription factors. The predictive performance of these DNase-seq-based binding site models demonstrated that predicted footprints corresponded to high confidence TF-DNA interactions. To quantify the DNase I sequence-dependent cleavage bias, we performed DNase-seq experiments using deproteinized DNA from K562 and MCF7 cell lines.

2014-09-04 | E-GEOD-61105 | biostudies-arrayexpress

Systematic Analysis of Transcription Factors Binding to Noncoding Variants

Project description:A large number of sequence variants have been linked to complex human traits and diseases, but deciphering their biological functions is still challenging since most of them reside in the noncoding DNA. To fill this gap, we have systematically assessed the binding of 270 human transcription factors (TF) to 95,886 noncoding variants in the human genome using an ultra-high-throughput multiplex protein-DNA binding assay, termed SNP evaluation by Systematic Evolution of Ligands by EXponential enrichment (SNP-SELEX). The resulting 828 million measurements of TF-DNA interactions enable estimation of the relative affinity of these TFs to each variant in vitro and allow for evaluation of the current methods to predict the impact of noncoding variants on TF binding. We show that the Position Weight Matrices (PWMs) of most TFs lack sufficient predictive power, while the Support Vector Machine (SVM) combined with the gapped k-mer representation show much improved performance, when assessed on results from independent SNP-SELEX experiments involving a new set of 61,020 sequence variants. We report highly predictive models for 94 human TFs and demonstrate their utility in genome-wide association studies (GWAS) and understanding of the molecular pathways involved in diverse human traits and diseases.

2020-10-28 | GSE118725 | GEO

The non-coding variant rs1800734 enhances DCLK3 expression through long-range interaction and promotes colorectal cancer progression

Project description:Genome-wide association studies (GWAS) have identified a great number of non-coding risk variants for colorectal cancer (CRC). To date, the majority of these variants have not been functionally studied. Identification of allele-specific transcription factor (TF) binding is of great importance to understand regulatory consequences of such variants. A recently developed proteome-wide analysis of disease-associated SNPs (PWAS) enables identification of DNA-TF interactions in an unbiased fashion. Here, we performed a large-scale PWAS studies to comprehensively characterize TF binding related to CRC, which identified 731 allele-specific TF binding at 116 CRC risk loci. This screen identified rs1800734 within the promoter region of MLH1 as perturbing the binding of TFAP4 and consequently increaseing DCLK3 expression through a long-range interaction, which promotes cancer progression through enhancing expression of the genes related to epithelial-to-mesenchymal transition (EMT).

2017-02-15 | GSE83968 | GEO

Characterization of sequence determinants of enhancer function using natural genetic variation [ATAC-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 | GSE193420 | GEO

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

2022-01-17 | GSE193081 | GEO

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

2022-01-17 | GSE193727 | GEO