ArticleNucleic acids research2019
QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 42 citations in OpenAlex.
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- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- A statistical approach for identifying single nucleotide variants that affect transcription factor binding.iScience · 2024Article
- Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci.Nature communications · 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
- UV irradiation remodels the specificity landscape of transcription factors.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Lineage-selective super enhancers mediate core regulatory circuitry during adipogenic and osteogenic differentiation of human mesenchymal stem cells.Cell death & disease · 2022Article
- Transcription Factor-Centric Approach to Identify Non-Recurring Putative Regulatory Drivers in Cancer.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2022Article
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- Predicting genotype-specific gene regulatory networks.Genome research · 2022Article
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- Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism.Human genomics · 2021Article
- Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources.BMC bioinformatics · 2021Article
- Mutational processes in cancer preferentially affect binding of particular transcription factors.Scientific reports · 2021Article
- In Silico Identification of the Complex Interplay between Regulatory SNPs, Transcription Factors, and Their Related Genes inInternational journal of molecular sciences · 2021Article
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function.Bioinformatics (Oxford, England) · 2020Article
- Association of rare non-coding SNVs in the lung-specific FOXF1 enhancer with a mitigation of the lethal ACDMPV phenotype.Human genetics · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a P-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation datasets in several formats, and it allows post-processing of the results through a user-friendly web interface. QBiC-Pred is freely available at http://qbic.genome.duke.edu.
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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.