scholarly journals GERV: A Statistical Method for Generative Evaluation of Regulatory Variants for Transcription Factor Binding

2015 ◽  
Author(s):  
Haoyang Zeng ◽  
Tatsunori Hashimoto ◽  
Daniel D. Kang ◽  
David K. Gifford
Keyword(s):  
Transcription Factor ◽  
Specific Binding ◽  
Association Studies ◽  
Transcription Factor Binding ◽  
Computational Method ◽  
Breast Cancer Cell Lines ◽  
Genome Wide Association Studies ◽  
Factor Binding ◽  
Regulatory Variants ◽  
Causal Variants

The majority of disease-associated variants identified in genome-wide association studies (GWAS) reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of GWAS studies. We present GERV (Generative Evaluation of Regulatory Variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif as well as associated co-factor motifs. We show that GERV outperforms existing methods in predicting SNPs associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked SNPs, and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis.

scholarly journals Predicting the effects of SNPs on transcription factor binding affinity

2019 ◽  
Author(s):  
Sierra S Nishizaki ◽  
Natalie Ng ◽  
Shengcheng Dong ◽  
Robert S Porter ◽  
Cody Morterud ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Affinity ◽  
Association Studies ◽  
Transcription Factor Binding ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Factor Binding ◽  
Genome Wide

Abstract Motivation Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). Results SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. Availability and implementation SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding

2018 ◽  
Vol 35 (15) ◽  
pp. 2657-2659 ◽  
Author(s):  
Sunyoung Shin ◽  
Rebecca Hudson ◽  
Christopher Harrison ◽  
Mark Craven ◽  
Sündüz Keleş
Keyword(s):  
Transcription Factor ◽  
Association Studies ◽  
Statistical Significance ◽  
Transcription Factor Binding ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Statistical Significance Testing ◽  
Factor Binding ◽  
Web Resource ◽  
Variant Alleles

AbstractSummaryUnderstanding the regulatory roles of non-coding genetic variants has become a central goal for interpreting results of genome-wide association studies. The regulatory significance of the variants may be interrogated by assessing their influence on transcription factor binding. We have developed atSNP Search, a comprehensive web database for evaluating motif matches to the human genome with both reference and variant alleles and assessing the overall significance of the variant alterations on the motif matches. Convenient search features, comprehensive search outputs and a useful help menu are key components of atSNP Search. atSNP Search enables convenient interpretation of regulatory variants by statistical significance testing and composite logo plots, which are graphical representations of motif matches with the reference and variant alleles. Existing motif-based regulatory variant discovery tools only consider a limited pool of variants due to storage or other limitations. In contrast, atSNP Search users can test more than 37 billion variant-motif pairs with marginal significance in motif matches or match alteration. Computational evidence from atSNP Search, when combined with experimental validation, may help with the discovery of underlying disease mechanisms.Availability and implementationatSNP Search is freely available at http://atsnp.biostat.wisc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding

2019 ◽  
Vol 17 ◽  
pp. 1415-1428 ◽  
Author(s):  
Walter Santana-Garcia ◽  
Maria Rocha-Acevedo ◽  
Lucia Ramirez-Navarro ◽  
Yvon Mbouamboua ◽  
Denis Thieffry ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Regulatory Variants ◽  
Flexible Framework ◽  
The Impact

scholarly journals EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

2019 ◽  
Author(s):  
Arif Harmanci ◽  
Akdes Serin Harmanci ◽  
Jyothishmathi Swaminathan ◽  
Vidya Gopalakrishnan
Keyword(s):  
Transcription Factor ◽  
Regulatory Elements ◽  
Transcription Factor Binding ◽  
Computational Method ◽  
Sensitive Detection ◽  
Supplementary Information ◽  
Chip Sequencing ◽  
Factor Binding ◽  
Nucleotide Resolution ◽  
Systematic Identification

Abstract Motivation Functional genomics experiments generate genomewide signal profiles that are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinctive patterns as they fluctuate along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in assays such as ChIP Sequencing and bisulfite sequencing. The genomic positions of valleys pinpoint locations of cis-regulatory elements such as enhancers and insulators. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements. Nevertheless, the valleys are not reported by majority of the analysis pipelines. Results We describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes. Differential histone valleys exhibit concordance with differential DNase signal at cell line specific valleys. DNA methylation valleys exhibit elevated conservation and high transcription factor binding. Specifically, we observed enriched binding of transcription factors associated with chromatin structure around methyl-valleys. Availability EpiSAFARI is publicly available at https://github.com/harmancilab/EpiSAFARI Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding

2015 ◽  
Vol 32 (4) ◽  
pp. 490-496 ◽  
Author(s):  
Haoyang Zeng ◽  
Tatsunori Hashimoto ◽  
Daniel D. Kang ◽  
David K. Gifford
Keyword(s):  
Transcription Factor ◽  
Statistical Method ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Regulatory Variants

scholarly journals Landscape of allele-specific transcription factor binding in the human genome

2020 ◽  
Author(s):  
Sergey Abramov ◽  
Alexandr Boytsov ◽  
Dariia Bykova ◽  
Dmitry D. Penzar ◽  
Ivan Yevshin ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Molecular Mechanisms ◽  
Specific Binding ◽  
Transcription Factor Binding ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Specific Transcription Factor ◽  
Factor Binding ◽  
Allele Specific

AbstractSequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

scholarly journals Fine-mapping cis-regulatory variants in diverse human populations

2018 ◽  
Author(s):  
Ashley K. Tehranchi ◽  
Brian Hie ◽  
Michael Dacre ◽  
Irene M. Kaplow ◽  
Kade P Pettie ◽  
...  
Keyword(s):  
Long Range ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Chromatin Accessibility ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Diverse Populations ◽  
Regulatory Variants ◽  
Causal Variants ◽  
In Cis

AbstractGenome-wide association studies (GWAS) are a powerful approach for connecting genotype to phenotype. Most GWAS hits are located in cis-regulatory regions, but the underlying causal variants and their molecular mechanisms remain unknown. To better understand human cis-regulatory variation, we mapped quantitative trait loci for chromatin accessibility (caQTLs)—a key step in cis-regulation—in 1000 individuals from 10 diverse populations. Most caQTLs were shared across populations, allowing us to leverage the genetic diversity to fine-map candidate causal regulatory variants, several thousand of which have been previously implicated in GWAS. In addition, many caQTLs that affect the expression of distal genes also alter the landscape of long-range chromosomal interactions, suggesting a mechanism for long-range expression QTLs. In sum, our results show that molecular QTL mapping integrated across diverse populations provides a high-resolution view of how worldwide human genetic variation affects chromatin accessibility, gene expression, and phenotype.

scholarly journals Widespread allelic heterogeneity in complex traits

2016 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Anthony Zhu ◽  
Gleb Kichaev ◽  
Ayellet V. Segrè ◽  
Chelsea J.-T. Ju ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Computational Method ◽  
Allelic Heterogeneity ◽  
Genome Wide Association Studies ◽  
New Methods ◽  
Genome Wide ◽  
Causal Variants

AbstractRecent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

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