An expansive human regulatory lexicon encoded in transcription factor footprints

By Shane J. Neph, J. Vierstra, Andrew B. Stergachis, Alex P. Reynolds, E. Haugen, Benjamin Vernot, Robert E. Thurman, R. Sandstrom, Audra K. Johnson, Matthew T. Maurano and 26 more. Nature, 2012

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Regulatory factor binding to genomic DNA protects the underlying sequence from cleavage by DNase I, leaving nucleotide-resolution footprints. Using genomic DNase I footprinting across 41 diverse cell and tissue types, we detected 45 million transcription factor occupancy events within regulatory regions, representing differential binding to 8.4 million distinct short sequence elements. Here we show that this small genomic sequence compartment, roughly twice the size of the exome, encodes an expansive repertoire of conserved recognition sequences for DNA-binding proteins that nearly doubles the size of the human cis–regulatory lexicon. We find that genetic variants affecting allelic chromatin states are concentrated in footprints, and that these elements are preferentially sheltered from DNA methylation. High-resolution DNase I cleavage patterns mirror nucleotide-level evolutionary conservation and track the crystallographic topography of protein–DNA interfaces, indicating that transcription factor structure has been evolutionarily imprinted on the human genome sequence. We identify a stereotyped 50-base-pair footprint that precisely defines the site of transcript origination within thousands of human promoters. Finally, we describe a large collection of novel regulatory factor recognition motifs that are highly conserved in both sequence and function, and exhibit cell-selective occupancy patterns that closely parallel major regulators of development, differentiation and pluripotency.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/551c35dd2e902cd076b5792610c0288de6a282d6

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Citations in the corpus

An Integrated Encyclopedia of DNA Elements in the Human Genome (2012, background): “25 .”
Super-enhancers in the control of cell identity and disease. (2013, neutral): “The set of enhancers that control any one cell’s gene expression program is probably best defined in murine embryonic stem cells (ESCs).”
Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain (2015, background): “In agreement with previous footprinting studies (Neph et al., 2012), we observe a range of footprint shapes for different TFs (Figure 5A).”
Chromatin-state discovery and genome annotation with ChromHMM (2017, background): “ChromHMM works on fixed bin sizes (default 200 bp), but for some applications, one can obtain more relevant higher-resolution boundaries by directly considering nucleosome depletion from the histone modification signal 1 , DNase I peaks 68 , or foot-prints within them 69,70 .”
GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations (2013, methodology): “As such, we have integrated three primary chromatin annotations from the ENCODE project: observed transcription factor binding sites [29], DNase1 hypersensitivity sites among 125 cell types [8], and Segway/ChromHMM consensus chromatin segmentation predictions among for 6 Tier 1 ENCODE cell types [9]”
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].”
Transposable Elements Are Major Contributors to the Origin, Diversification, and Regulation of Vertebrate Long Noncoding RNAs (2013, methodology): “We made use of the DNase I clusters track from the integrated ENCODE data sets [81,82,113], which was retrieved from the UCSC Genome Browser [107].”
PlantRegMap: charting functional regulatory maps in plants (2019, background): “As prediction from direct scanning yields a rather high false positive rate, additional data such as DNase-seq footprints (9,10) and conserved elements (11–16) have been incorporated to screen for functional TFBSs.”
Data integration in the era of omics: current and future challenges (2014, methodology): “Network analysis have also been addressed at ENCODE by the investigation of DNase-seq data, which allows the identification of active regulatory DNA elements, and its integration with Position Weight Matrixes to generate regulatory networks for each ENCODE cell-type [63,64].”