Discovery of non-directional and directional pioneer transcription factors by modeling DNase profile magnitude and shape

By R. Sherwood, Tatsunori B. Hashimoto, Charles W. O'Donnell, Sophia Lewis, Amira A. Barkal, J. V. van Hoff, Vivek Karun, Tommi S. Jaakkola, David K. Gifford. Nature Biotechnology, 2014

431 citations recorded by the source corpus.

We describe protein interaction quantitation (PIQ), a computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through the use of machine-learning techniques, PIQ identified binding sites for >700 TFs from one DNase I hypersensitivity analysis followed by sequencing (DNase-seq) experiment with accuracy comparable to that of chromatin immunoprecipitation followed by sequencing (ChIP-seq). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into prepancreatic and intestinal endoderm. We identified 120 and experimentally validated eight 'pioneer' TF families that dynamically open chromatin. Four pioneer TF families only opened chromatin in one direction from their motifs. Furthermore, we identified 'settler' TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and nondirectional pioneer activity shapes the chromatin landscape for population by settler TFs.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9f511c948da76907619e4ae5cb15a69c2a5f5616

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In Molecular Biology

Citations in the corpus

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding (2017, methodology): “…clustering regions into “bound” and “unbound” based on their experimental properties (e.g., DNase-seq data or histone modiﬁcations (Pique-Regi et al. , 2011; Sherwood et al. , 2014; Gusmao et al. , 2014)), while others base their predictions on statistical tests (Piper et al. , 2013) or…”
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks (2015, background): “The typical DHS is a nucleosome-free region where protein(s) bind the DNA to create an accessible site (Voss and Hager 2014; Sherwood et al. 2014).”
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “Computational methods designed to predict TF binding include PIQ (Sherwood et al., 2014), Centipede (Pique-Regi et al., 2011), and msCentipede (Raj et al., 2015).”
SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps (2015, methodology): “Meanwhile, several methods use DNase-seq profiles to scan for instances of knownmotifs [10, 11], and one recently proposed approach exploits the read-level properties of high-depth digital genomic footprinting (DGF) to improve localization of known motifs [12].”