ArticleiScience2024
A statistical approach for identifying single nucleotide variants that affect transcription factor binding.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci.Nature communications · 2026Article
- Enhancing KLF15 activity in cardiomyocytes: a novel approach to prevent pathological reprogramming and fibrosis via nuclease-deficient dCas9VPR.Signal transduction and targeted therapy · 2026Article
- The transaminase-ω-amidase pathway senses oxidative stress to control glutamine metabolism and α-ketoglutarate levels in endothelial cells.The EMBO journal · 2026Article
- GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on
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Registered trials
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