scholarly journals Single Nucleotide Variants of the TGACTCA Motif Modulate Energetics and Orientation of Binding of the Jun-Fos Heterodimeric Transcription Factor†

Biochemistry ◽  
2009 ◽  
Vol 48 (9) ◽  
pp. 1975-1983 ◽  
Author(s):  
Kenneth L. Seldeen ◽  
Caleb B. McDonald ◽  
Brian J. Deegan ◽  
Amjad Farooq
Keyword(s):  
Transcription Factor ◽  
Single Nucleotide Variants ◽  
Single Nucleotide

scholarly journals Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics

Science ◽  
2013 ◽  
Vol 342 (6154) ◽  
pp. 1235587 ◽  
Author(s):  
Ekta Khurana ◽  
Yao Fu ◽  
Vincenza Colonna ◽  
Xinmeng Jasmine Mu ◽  
Hyun Min Kang ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Similar Pattern ◽  
Cancer Genomics ◽  
Network Centrality ◽  
Computational Tool ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Noncoding Regions ◽  
Cancer Genomes ◽  
Personal Genomes

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations (“ultrasensitive”) and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, “motif-breakers”). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

scholarly journals Identification of Deleterious Single Nucleotide Polymorphism (SNP)s in the Human TBX5 Gene & Prediction of Their Structural & Functional Consequences: An In Silico approach

2020 ◽  
Author(s):  
A M U B Mahfuz ◽  
Md. Arif Khan
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Transcription Factor ◽  
In Silico ◽  
Developmental Process ◽  
Upper Limbs ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Functional Consequences ◽  
Tbx5 Gene

ABSTRACTT-box transcription factor 5 (TBX5) gene encodes the transcription factor TBX5 which plays a crucial role in the development of the heart and upper limbs. Alternative splicing resulting in several isoforms regulate the functions of this gene during the developmental process. Damaging single nucleotide variants in this gene alter the structure and disturb the functions of TBX5 and ultimately cause Holt-Oram Syndrome (HOS), an autosomal dominant disease where various congenital malformations of the heart (with or without conduction defects), upper limbs and shoulder girdles are observed. Besides HOS, TBX5 single nucleotide variants can also be associated with Dilated Cardiomyopathy, Atrial Fibrillation, and Tetralogy of Fallot without skeletal deformity.By exploiting available Single Nucleotide Polymorphism information in dbSNP, this study was designed to identify in silico the deleterious TBX5 SNPs, and predict their structural and functional consequences, and alteration of biochemical properties on the candidate protein. For this purpose, various reliable in silico analysis tools such as PROVEAN, SIFT, PolyPhen-2, MutPred2, PredictSNP1, PredictSNP2, MetaLR, MetaSVM, REVEL, ConSurf, NetsurfP-2.0, iStable 2.0, Missense3D, UTRdb, MirSNP, and Human Splicing Finder (HSF) have been used. 58 missense substitutions were found damaging by both sequence homology-based tools SIFT (Sorting Intolerant from Tolerant) and PROVEAN (Protein Variation Effect Analyzer), and structure homology-based tool PolyPhen-2 (Polymorphism Phenotyping-2). Among these 58 substitutions, 13 are already annotated as Pathogenic/Likely Pathogenic in ClinVar database, and so they were excluded. Then, the rest 45 high confidence substitutions were further scrutinized by various disease association predicting meta servers. Next, conservation profile of the native amino acid residues, their surface & solvent accessibility, and stability and structural integrity of the protein upon mutation were assessed. Analysis of 1 stop loss SNP, and 2 nonsense SNPs were done by PredictSNP2. Analysis of SNPs in the UTR region were done using UTRdb and MirSNP, and splice site SNPs were evaluated by Human Splicing Finder (HSF). This study provides a comprehensive list of most deleterious SNPs onTBX5 gene. The results from this study can help in early diagnosis of HOS and in relevant genetic counseling.

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 Publisher Correction: Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sebastian Carrasco Pro ◽  
Katia Bulekova ◽  
Brian Gregor ◽  
Adam Labadorf ◽  
Juan Ignacio Fuxman Bass
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Factor Binding ◽  
Genome Wide

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

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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sergey Abramov ◽  
Alexandr Boytsov ◽  
Daria 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 Positional weight matrices have sufficient prediction power for analysis of noncoding variants

F1000Research ◽  
2022 ◽  
Vol 11 ◽  
pp. 33
Author(s):  
Alexandr Boytsov ◽  
Sergey Abramov ◽  
Vsevolod J. Makeev ◽  
Ivan V. Kulakovskiy
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Factor Binding ◽  
Regulatory Variants ◽  
Scoring Matrix ◽  
Regulatory Potential ◽  
The Rich ◽  
Positional Weight

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.

Single-Nucleotide Variants in microRNAs Sequences or in their Target Genes Might Influence the Risk of Epilepsy: A Review

2021 ◽  
Author(s):  
Renata Parissi Buainain ◽  
Matheus Negri Boschiero ◽  
Bruno Camporeze ◽  
Paulo Henrique Pires de Aguiar ◽  
Fernando Augusto Lima Marson ◽  
...  
Keyword(s):  
Target Genes ◽  
Single Nucleotide Variants ◽  
Single Nucleotide

scholarly journals Combination of Genome-Wide Polymorphisms and Copy Number Variations of Pharmacogenes in Koreans

2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Nayoung Han ◽  
Jung Mi Oh ◽  
In-Wha Kim
Keyword(s):  
Copy Number ◽  
Genome Wide Association Study ◽  
Copy Number Gain ◽  
Copy Number Variations ◽  
Gene Gain ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Haplotype Blocks ◽  
Genome Wide ◽  
Control And Prevention

For predicting phenotypes and executing precision medicine, combination analysis of single nucleotide variants (SNVs) genotyping with copy number variations (CNVs) is required. The aim of this study was to discover SNVs or common copy CNVs and examine the combined frequencies of SNVs and CNVs in pharmacogenes using the Korean genome and epidemiology study (KoGES), a consortium project. The genotypes (N = 72,299) and CNV data (N = 1000) were provided by the Korean National Institute of Health, Korea Centers for Disease Control and Prevention. The allele frequencies of SNVs, CNVs, and combined SNVs with CNVs were calculated and haplotype analysis was performed. CYP2D6 rs1065852 (c.100C>T, p.P34S) was the most common variant allele (48.23%). A total of 8454 haplotype blocks in 18 pharmacogenes were estimated. DMD ranked the highest in frequency for gene gain (64.52%), while TPMT ranked the highest in frequency for gene loss (51.80%). Copy number gain of CYP4F2 was observed in 22 subjects; 13 of those subjects were carriers with CYP4F2*3 gain. In the case of TPMT, approximately one-half of the participants (N = 308) had loss of the TPMT*1*1 diplotype. The frequencies of SNVs and CNVs in pharmacogenes were determined using the Korean cohort-based genome-wide association study.

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