DNA Shape Features Improve Transcription Factor Binding Site Predictions In Vivo

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Abstract

Interactions of transcription factors (TFs) with DNA comprise a complex interplay between base-specific amino acid contacts and readout of DNA structure. Recent studies have highlighted the complementarity of DNA sequence and shape in modeling TF binding in vitro. Here, we have provided a comprehensive evaluation of in vivo datasets to assess the predictive power obtained by augmenting various DNA sequence-based models of TF binding sites (TFBSs) with DNA shape features (helix twist, minor groove width, propeller twist, and roll). Results from 400 human ChIP-seq datasets for 76 TFs show that combining DNA shape features with position-specific scoring matrix (PSSM) scores improves TFBS predictions. Improvement has also been observed using TF flexible models and a machine-learning approach using a binary encoding of nucleotides in lieu of PSSMs. Incorporating DNA shape information is most beneficial for E2F and MADS-domain TF families. Our findings indicate that incorporating DNA sequence and shape information benefits the modeling of TF binding under complex in vivo conditions.

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APA

Mathelier, A., Xin, B., Chiu, T. P., Yang, L., Rohs, R., & Wasserman, W. W. (2016). DNA Shape Features Improve Transcription Factor Binding Site Predictions In Vivo. Cell Systems, 3(3), 278-286.e4. https://doi.org/10.1016/j.cels.2016.07.001

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