Journal
CELL SYSTEMS
Volume 3, Issue 3, Pages 278-+Publisher
CELL PRESS
DOI: 10.1016/j.cels.2016.07.001
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Funding
- Genome Canada Large scale Applied Research Grant [174CDE]
- Child and Family Research Institute, Vancouver
- British Columbia Children's Hospital Foundation
- Norwegian Research Council
- Helse Sor-Ost
- University of Oslo through the Centre for Molecular Medicine Norway (NCMM), Nordic European Molecular Biology Laboratory partnership for Molecular Medicine
- NIH [R01GM106056, U01GM103804]
- Alfred P. Sloan Research Fellowship
- National Science Foundation [MCB-1413539]
- Direct For Biological Sciences
- Div Of Molecular and Cellular Bioscience [1413539] Funding Source: National Science Foundation
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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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