ArticleBioinformatics (Oxford, England)2016
GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.Human mutation · 2019Pooled it
- baal-nf identifies motif-disrupting variants that decrease transcription factor binding affinity.Genome biology · 2026Article
- OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 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
- Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.BMC genomics · 2023Article
- Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data.iScience · 2023Article
- Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.Genome research · 2022Article
- A general framework for predicting the transcriptomic consequences of non-coding variation and small molecules.PLoS computational biology · 2022Article
- Motif-Raptor: a cell type-specific and transcription factor centric approach for post-GWAS prioritization of causal regulators.Bioinformatics (Oxford, England) · 2021Article
- Deep neural networks identify sequence context features predictive of transcription factor binding.Nature machine intelligence · 2021Article
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- Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response.BMC biology · 2019Article
- Stable enhancers are active in development, and fragile enhancers are associated with evolutionary adaptation.Genome biology · 2019Article
- BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts.Frontiers in genetics · 2019Article
- Recurrent Neural Network for Predicting Transcription Factor Binding Sites.Scientific reports · 2018Article
- Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk.Nature genetics · 2018Article
- DNA Motif Recognition Modeling from Protein Sequences.iScience · 2018Article
- Integrating ChIP-seq with other functional genomics data.Briefings in functional genomics · 2018Review
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4 authors.
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Abstract
motivationThe majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies.
resultsWe present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The implementation of GERV and related data are available at http://gerv.csail.mit.edu/.
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