scholarly journals Evaluating the impact of single nucleotide variants on transcription factor binding

2016 ◽  
pp. gkw691 ◽  
Author(s):  
Wenqiang Shi ◽  
Oriol Fornes ◽  
Anthony Mathelier ◽  
Wyeth W. Wasserman
Author(s):  
Sergey Abramov ◽  
Alexandr Boytsov ◽  
Dariia Bykova ◽  
Dmitry D. Penzar ◽  
Ivan Yevshin ◽  
...  

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.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sebastian Carrasco Pro ◽  
Katia Bulekova ◽  
Brian Gregor ◽  
Adam Labadorf ◽  
Juan Ignacio Fuxman Bass

An amendment to this paper has been published and can be accessed via a link at the top of the paper.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sergey Abramov ◽  
Alexandr Boytsov ◽  
Daria Bykova ◽  
Dmitry D. Penzar ◽  
Ivan Yevshin ◽  
...  

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.


F1000Research ◽  
2022 ◽  
Vol 11 ◽  
pp. 33
Author(s):  
Alexandr Boytsov ◽  
Sergey Abramov ◽  
Vsevolod J. Makeev ◽  
Ivan V. Kulakovskiy

The commonly accepted model to quantify the specificity of transcription factor binding to DNA is the position weight matrix, also called the position-specific scoring matrix. Position weight matrices are used in thousands of projects and computational tools in regulatory genomics, including prediction of the regulatory potential of single-nucleotide variants. Yet, recently Yan et al. presented new experimental method for analysis of regulatory variants and, based on its results, reported that "the position weight matrices of most transcription factors lack sufficient predictive power". Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can successfully quantify transcription factor binding to alternative alleles.


Epigenomics ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 613-630
Author(s):  
Vidya Chidambaran ◽  
Xue Zhang ◽  
Valentina Pilipenko ◽  
Xiaoting Chen ◽  
Benjamin Wronowski ◽  
...  

Background: Overlap of pathways enriched by single nucleotide polymorphisms and DNA-methylation underlying chronic postsurgical pain (CPSP), prompted pilot study of CPSP-associated methylation quantitative trait loci (meQTL). Materials & methods: Children undergoing spine-fusion were recruited prospectively. Logistic-regression for genome- and epigenome-wide CPSP association and DNA-methylation-single nucleotide polymorphism association/mediation analyses to identify meQTLs were followed by functional genomics analyses. Results: CPSP (n = 20/58) and non-CPSP groups differed in pain-measures. Of 2753 meQTLs, DNA-methylation at 127 cytosine–guanine dinucleotides mediated association of 470 meQTLs with CPSP (p < 0.05). At PARK16 locus, CPSP risk meQTLs were associated with decreased DNA-methylation at RAB7L1 and increased DNA-methylation at PM20D1. Corresponding RAB7L1/PM20D1 blood eQTLs (GTEx) and cytosine–guanine dinucleotide-loci enrichment for histone marks, transcription factor binding sites and ATAC-seq peaks suggest altered transcription factor-binding. Conclusion: CPSP-associated meQTLs indicate epigenetic mechanisms mediate genetic risk. Clinical trial registration: NCT01839461 , NCT01731873  (ClinicalTrials.gov).


2019 ◽  
Vol 17 ◽  
pp. 1415-1428 ◽  
Author(s):  
Walter Santana-Garcia ◽  
Maria Rocha-Acevedo ◽  
Lucia Ramirez-Navarro ◽  
Yvon Mbouamboua ◽  
Denis Thieffry ◽  
...  

2020 ◽  
Author(s):  
Jan Grau ◽  
Florian Schmidt ◽  
Marcel H. Schulz

AbstractSeveral studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that are affected by DNA methylation. Overall, we find that CpG methylation decreases the likelihood of binding for the majority of TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding.


2019 ◽  
Author(s):  
Sierra S Nishizaki ◽  
Natalie Ng ◽  
Shengcheng Dong ◽  
Robert S Porter ◽  
Cody Morterud ◽  
...  

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.


2021 ◽  
Author(s):  
Carlos A. Villarroel ◽  
Paulo Canessa ◽  
Macarena Bastias ◽  
Francisco A Cubillos

Saccharomyces cerevisiae rewires its transcriptional output to survive stressful environments, such as nitrogen scarcity under fermentative conditions. Although divergence in nitrogen metabolism has been described among natural yeast populations, the impact of regulatory genetic variants modulating gene expression and nitrogen consumption remains to be investigated. Here, we employed an F1 hybrid from two contrasting S. cerevisiae strains, providing a controlled genetic environment to map cis factors involved in the divergence of gene expression regulation in response to nitrogen scarcity. We used a dual approach to obtain genome-wide allele-specific profiles of chromatin accessibility, transcription factor binding, and gene expression through ATAC-seq and RNA-seq. We observed large variability in allele-specific expression and accessibility between the two genetic backgrounds, with a third of these differences specific to a deficient nitrogen environment. Furthermore, we discovered events of allelic bias in gene expression correlating with allelic bias in transcription factor binding solely under nitrogen scarcity, where the majority of these transcription factors orchestrates the Nitrogen Catabolite Repression regulatory pathway and demonstrates a cis x environment-specific response. Our approach allowed us to find cis variants modulating gene expression, chromatin accessibility and allelic differences in transcription factor binding in response to low nitrogen culture conditions.


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