Predicting the effects of SNPs on transcription factor binding affinity

Nishizaki, Sierra S.; Ng, Natalie; Dong, Shengcheng; Porter, Robert S.; Morterud, Cody; Williams, Colten; Asman, Courtney; Switzenberg, Jessica A.; Boyle, Alan P.

doi:10.1093/bioinformatics/btz612

Cited by 47 publications

(35 citation statements)

References 58 publications

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“…It is noteworthy that the identification of a TF with the help of listed methods requires the prior knowledge about the TFBSs harboring SNPs and this knowledge is usually acquired by bioinformatics analysis of the corresponding DNA sequence. Currently, different models of TFBSs, specialized databases, and the related tools are widely used for TFBS prediction, functional annotation of sequence variants, and prediction of the SNP impact on TF binding [ 60 , 61 , 62 , 63 , 64 , 65 , 66 ] and others. Moreover, the closer the result of bioinformatics analysis to the truth, the fewer labor and funds spent on the corresponding experiments.…”

Section: Modern Array Of Methods For Studying Individual Rsnpsmentioning

confidence: 99%

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

Degtyareva

Antontseva

Merkulova

2021

IJMS

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The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

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Section: Modern Array Of Methods For Studying Individual Rsnpsmentioning

confidence: 99%

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

Degtyareva

Antontseva

Merkulova

2021

IJMS

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“…Of note, this pattern is not seen for another transcription factor, CEBPB, where the SEMplMe output for methylated sites is highly correlated between all cell types examined (K562, IMR-90, HepG2, and GM12878), suggesting that not all transcription factors are subject to cell type specificity due to methylation differences ( Supplementary Figure 2). Interestingly, SEMpl data without methylation appears to be primarily cell type agnostic, providing evidence that methylation plays a meaningful role in cell type specificity for some transcription factors (Nishizaki et al , 2020) .…”

Section: Dna Methylation Drives Cell Type Specific Transcription Factmentioning

confidence: 96%

“…To address this need, we have adapted SEMpl, a computational genome-wide transcription factor binding affinity prediction method, to incorporate whole genome bisulfite-seq (WGBS) data. This allows our predictions to include the effects of DNA methylation on binding affinity (Nishizaki et al , 2020) . SEMpl uses open-source in vivo data to generate predictions using transcription factor binding data from ChIP-seq and open chromatin data from DNase-seq for a transcription factor of interest.…”

Section: Introductionmentioning

confidence: 99%

SEMplMe: A tool for integrating DNA methylation effects in transcription factor binding affinity predictions

Nishizaki

Boyle

2020

Preprint

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Motivation: Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factors motif. Results: SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease. Availability and Implementation: SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe.

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“…(1) TFBS polymorphisms comprise only 8% of the genome polymorphisms but 31% of the trait-associated polymorphisms identified by GWAS [8]. (2) Up to 21.6% of variants affecting gene expression overlap annotated TFBSs [9].…”

Section: Introductionmentioning

confidence: 99%

Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases

Tseng

Wong

Liao

et al. 2021

IJMS

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Variants of transcription factor binding sites (TFBSs) constitute an important part of the human genome. Current evidence demonstrates close links between nucleotides within TFBSs and gene expression. There are multiple pathways through which genomic sequences located in TFBSs regulate gene expression, and recent genome-wide association studies have shown the biological significance of TFBS variation in human phenotypes. However, numerous challenges remain in the study of TFBS polymorphisms. This article aims to cover the current state of understanding as regards the genomic features of TFBSs and TFBS variants; the mechanisms through which TFBS variants regulate gene expression; the approaches to studying the effects of nucleotide changes that create or disrupt TFBSs; the challenges faced in studies of TFBS sequence variations; the effects of natural selection on collections of TFBSs; in addition to the insights gained from the study of TFBS alleles related to gout, its associated comorbidities (increased body mass index, chronic kidney disease, diabetes, dyslipidemia, coronary artery disease, ischemic heart disease, hypertension, hyperuricemia, osteoporosis, and prostate cancer), and the treatment responses of patients.

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Predicting the effects of SNPs on transcription factor binding affinity

Cited by 47 publications

References 58 publications

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

SEMplMe: A tool for integrating DNA methylation effects in transcription factor binding affinity predictions

Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases

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