ArticleBioinformatics (Oxford, England)2020
Predicting the effects of SNPs on transcription factor binding affinity.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gene-level analysis reveals the genetic aetiology and therapeutic targets of schizophrenia.Nature human behaviour · 2025Pooled it
- Non-coding regulatory variants in adolescent idiopathic scoliosis risk and pathogenesis.Communications biology · 2026Review
- Molecular regulatory mechanisms of schizophrenia-associated functional non-coding variants.Molecular psychiatry · 2026Article
- Tumor heterogeneity: development, mechanisms, and therapeutic implications.Signal transduction and targeted therapy · 2026Review
- SEMPLR: an R package for transcription factor binding prediction.Bioinformatics (Oxford, England) · 2026Article
- Quantitative modulation of a spatial enhancer through the biophysical properties of a transcription factor binding site.Science advances · 2026Article
- The IGVF catalog-from genetic variation to function.Nucleic acids research · 2026Article
- MMP-8 in Peri-Implantitis: A Cross-Sectional Study on Genetic Polymorphisms and Enzymatic Activation.Journal of periodontal research · 2026Article
- Patient-Specific Regulatory Network Rewiring in Inflammatory Bowel Disease: How Genetic Polymorphisms Divert Incoming Signals and Contribute to Disease Pathogenesis.Inflammatory bowel diseases · 2025Article
- Article
- SNPeBoT: a tool for predicting transcription factor allele specific binding.BMC bioinformatics · 2025Article
- An activity-regulated transcriptional program directly drives synaptogenesis.Nature neuroscience · 2024Article
- Computational exploration ofBiochemistry and biophysics reports · 2024Article
- Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.Frontiers in bioscience (Scholar edition) · 2024Review
- Genetic Diversity in Bronchial Asthma Susceptibility: Exploring the Role of Vitamin D Receptor Gene Polymorphisms in Varied Geographic Contexts.International journal of molecular sciences · 2024Article
- Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Briefings in bioinformatics · 2024Article
- Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks.BMC genomics · 2023Article
- Applications for Deep Learning in Epilepsy Genetic Research.International journal of molecular sciences · 2023Review
- PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.Journal of cheminformatics · 2023Article
- 3D chromatin structure in chondrocytes identifies putative osteoarthritis risk genes.Genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
motivationGenome-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).
resultsSEMpl 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.
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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.