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.
Highlights
Genetic variants and mutations play important roles in human disease [1]
In previous work we showed that our ordinary least squares (OLS) model-based predictions of transcription factors (TFs) binding changes due to DNA mutations correlate well with measured changes in gene expression [2]
We analyzed a large set of pathogenic non-coding variants, showing that these variants lead to more significant differences in TF binding between alleles, compared to common variants, which indicates that there is a strong regulatory component to pathogenic non-coding variants [2]
Summary
Most variants occur in non-coding genomic regions, where they can impact gene expression by disrupting interactions between transcription factors (TFs) and DNA. In previous work we have introduced an ordinary least squares (OLS)-based method for assessing the impact of non-coding mutations on TF-DNA interactions [2]. We used the OLS models to predict changes in TF binding due to DNA mutations, and we showed that our binding change predictions correlate well with measured changes in gene expression. Our approach is novel compared to previous models because, by using OLS, we obtain estimates of the model coefficients, and the variance of these estimates, which allows us to compute normalized binding change scores (z-scores) and significance levels (P-values) reflecting our confidence that a mutation affects TF binding. The computed P-values implicitly take into account the quality of the model and of the training data, so in the case of poor predictive models a large change in binding is required for a mutation to be called significant [2]
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