ArticleNucleic acids research2016
Evaluating the impact of single nucleotide variants on transcription factor binding.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 43 citations in OpenAlex.
- Statistical framework for calling allelic imbalance in high-throughput sequencing data.Nature communications · 2025Article
- A statistical approach for identifying single nucleotide variants that affect transcription factor binding.iScience · 2024Article
- Allele-specific binding (ASB) analyzer for annotation of allele-specific binding SNPs.BMC bioinformatics · 2023Article
- ANANASTRA: annotation and enrichment analysis of allele-specific transcription factor binding at SNPs.Nucleic acids research · 2022Article
- CASCADE: high-throughput characterization of regulatory complex binding altered by non-coding variants.Cell genomics · 2022Article
- Landscape of allele-specific transcription factor binding in the human genome.Nature communications · 2021Article
- Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles.PLoS genetics · 2020Article
- MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function.Bioinformatics (Oxford, England) · 2020Article
- Personalized and graph genomes reveal missing signal in epigenomic data.Genome biology · 2020Article
- Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.Nucleic acids research · 2019Article
- Gene expression models based on transcription factor binding events confer insight into functional cis-regulatory variants.Bioinformatics (Oxford, England) · 2019Article
- Transcriptional regulation of normal human mammary cell heterogeneity and its perturbation in breast cancer.The EMBO journal · 2019Review
- QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants.Nucleic acids research · 2019Article
- Four novel polymorphisms in long non-coding RNA HOTTIP are associated with the risk and prognosis of colorectal cancer.Bioscience reports · 2019Article
- RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding.Computational and structural biotechnology journal · 2019Article
- DNA Motif Recognition Modeling from Protein Sequences.iScience · 2018Article
- Analysis of Genetic Variation Indicates DNA Shape Involvement in Purifying Selection.Molecular biology and evolution · 2018Article
- MANTA2, update of the Mongo database for the analysis of transcription factor binding site alterations.Scientific data · 2018Article
- Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity.Nature genetics · 2018Article
- JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework.Nucleic acids research · 2018Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Diseases and phenotypes caused by disrupted transcription factor (TF) binding are being identified, but progress is hampered by our limited capacity to predict such functional alterations. Improving predictions may be dependent on expanding the set of bona fide TF binding alterations. Allele-specific binding (ASB) events, where TFs preferentially bind to one of the two alleles at heterozygous sites, reveal the impact of sequence variations in altered TF binding. Here, we present the largest ASB compilation to our knowledge, 10 765 ASB events retrieved from 45 ENCODE ChIP-Seq data sets. Our analysis showed that ASB events were frequently associated with motif alterations of the ChIP'ed TF and potential partner TFs, allelic difference of DNase I hypersensitivity and allelic difference of histone modifications. For TF dimers bound symmetrically to DNA, ASB data revealed that central positions of the TF binding motifs were disproportionately important for binding. Lastly, the impact of variation on TF binding was predicted by a classification model incorporating all the investigated features of ASB events. Classification models using only DNase I hypersensitivity and sequence data exhibited predictive accuracy approaching the models with substantially more features. Taken together, the combination of ASB data and the classification model represents an important step toward elucidating regulatory variants across the human genome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.