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Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.

Publication ,  Conference
Zhao, J; Li, D; Seo, J; Allen, AS; Gordân, R
Published in: Res Comput Mol Biol
May 2017

Many recent studies have emphasized the importance of genetic variants and mutations in cancer and other complex human diseases. The overwhelming majority of these variants occur in non-coding portions of the genome, where they can have a functional impact by disrupting regulatory interactions between transcription factors (TFs) and DNA. Here, we present a method for assessing the impact of non-coding mutations on TF-DNA interactions, based on regression models of DNA-binding specificity trained on high-throughput in vitro data. We use ordinary least squares (OLS) to estimate the parameters of the binding model for each TF, and we show that our predictions of TF-binding changes due to DNA mutations correlate well with measured changes in gene expression. In addition, by leveraging distributional results associated with OLS estimation, for each predicted change in TF binding we also compute a normalized score (z-score) and a significance value (p-value) reflecting our confidence that the mutation affects TF binding. We use this approach to analyze a large set of pathogenic non-coding variants, and we show that these variants lead to significant differences in TF binding between alleles, compared to a control set of common variants. Thus, our results indicate that there is a strong regulatory component to the pathogenic non-coding variants identified thus far.

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Published In

Res Comput Mol Biol

DOI

Publication Date

May 2017

Volume

10229

Start / End Page

336 / 352

Location

Germany

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Zhao, J., Li, D., Seo, J., Allen, A. S., & Gordân, R. (2017). Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. In Res Comput Mol Biol (Vol. 10229, pp. 336–352). Germany. https://doi.org/10.1007/978-3-319-56970-3_21
Zhao, Jingkang, Dongshunyi Li, Jungkyun Seo, Andrew S. Allen, and Raluca Gordân. “Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.” In Res Comput Mol Biol, 10229:336–52, 2017. https://doi.org/10.1007/978-3-319-56970-3_21.
Zhao J, Li D, Seo J, Allen AS, Gordân R. Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. In: Res Comput Mol Biol. 2017. p. 336–52.
Zhao, Jingkang, et al. “Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.Res Comput Mol Biol, vol. 10229, 2017, pp. 336–52. Pubmed, doi:10.1007/978-3-319-56970-3_21.
Zhao J, Li D, Seo J, Allen AS, Gordân R. Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. Res Comput Mol Biol. 2017. p. 336–352.

Published In

Res Comput Mol Biol

DOI

Publication Date

May 2017

Volume

10229

Start / End Page

336 / 352

Location

Germany

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences