QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants

Vincentius Martin; Jingkang Zhao; Ariel Afek; Zachery Mielko; Raluca Gordân

doi:10.1093/nar/gkz363

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QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants

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QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants

Vincentius Martin, Jingkang Zhao, Ariel Afek, Zachery Mielko and Raluca Gordân

Nucleic acids research, Vol.47(W1), pp.W127-W135

02/Jul/2019

DOI: https://doi.org/10.1093/nar/gkz363

PMCID: PMC6602471

PMID: 31114870

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Published (Version of record)CC BY V4.0, Open Access

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.

Binding Sites - genetics

Computational Biology - methods

DNA - genetics

Genomics - methods

Humans

Protein Binding - genetics

Transcription Factors - genetics

Algorithms

Software

Details

Title: QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants
Creators: Vincentius Martin - Duke University
Jingkang Zhao - Duke University
Ariel Afek - Duke University
Zachery Mielko - Duke University
Raluca Gordân - Duke University School of Medicine
Resource Type: Journal article
Publication Details: Nucleic acids research, Vol.47(W1), pp.W127-W135; 02/Jul/2019
Language: English
DOI: https://doi.org/10.1093/nar/gkz363
PMID: 31114870
PMCID: PMC6602471
Grant note: The authors would like to thank Brendan Frey, Leo J. Lee and Alice Gao (University of Toronto) for help with training DeepBind PBM models on the same datasets used for our OLS models. High-performance computing was partially supported by the Duke Center for Genomic and Computational Biology. National Institutes of Health [R01-GM117106 to R.G.]; National Science Foundation [MCB-1715589 to R.G.]. Funding for open access charge: NIH Grant [R01-GM117106].
Record Identifier: 993328515303596

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