Histone acetylation and GAF occupancy are important covariates in predicting HSF binding intensity.

<p>Plotted are the relative values of the sums of the coefficients associated with all rules that reference each covariate in the rules ensemble <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002610#pgen.1002610-Friedman1" target="_blank">[20]</a>. Results are shown for (A) the histone variant and modification model and (B) the non-Histone factor model.</p

Pentamers within the HSEs are dependent upon their consensus match and also their position relative to the other pentamers.

<p>A) The mixture model defines each pentamer within the HSE as strict or relaxed depending upon how well it conforms to the canonical HSE. Note that the position of relaxed pentamers strongly influences their composition. B) A probabilistic sequence model reveals that the presence of two strict (red) and one relaxed (blue) pentamer provides the best explanation of the data.</p

In vitro and in vivo binding of HSF to genomic HSEs do not correlate.

<p>A) A scatter plot comparing the observed in vivo HSF binding intensity and in vitro binding intensity for each isolated HSE indicates that the vast majority of in vivo binding is suppressed (green) or abolished (blue), if we assume that the top seven most DNase I hypersensitive isolated HSE clusters provide the best estimates for sites that are minimally influenced by chromatin. After scaling, red points have similar in vivo and in vitro intensity, black points may be enhanced in vivo, while green and blue points are suppressed and abolished, respectively. B) The points from panel A were categorized, and the resulting bar chart shows the relative frequencies of each category.</p

In vitro binding reveals potential HSF binding sites.

<p>The blue box highlights strong differences in the usage of potential binding sites in vivo at the Cpr67B locus, while the green boxes highlight differences in the magnitude of binding to major heat shock genes promoters, despite comparable in vitro binding affinities.</p

Genomic chromatin and PB–seq data accurately predict in vivo HSF binding intensity.

<p>A) The intensity of in vivo ChIP-seq peaks is not recapitulated by in vitro PB–seq data; however, genomic DNase I hypersensitivity data and histone modification ChIP-chip data can be used to accurately predict HSF binding intensity. B) The experimentally determined ratio between in vivo ChIP-seq HSF intensity and in vitro PB–seq intensity is plotted against the predicted in vivo/actual PB–seq ratio. The Pearson correlation for each model is shown.</p

DNase I hypersensitivity can be inferred using histone marks and MNase data.

<p>A) The intensity of DNase I hypersensitivity landscape is inferred by models (colors) that use histone modification profiles, non-histone factor profiles, DNase I data and MNase-seq data. B) The experimentally determined DNase I hypersensitivity data is plotted against inferred intensity for the various models. The Pearson correlation for each model is shown.</p

Recombinant HSF binds HSEs with picomolar affinity in vitro.

<p>A and B) The mobility of the constant 200 attomole HSE probe shifts into a trimeric-HSF:HSE complex as increasing HSF is added. There is no HSF in the left-most lane, the right-most lane contains 3 nM HSF (1 nM trimeric HSF), and the intervening lanes contain two-fold serial dilutions of HSF. C) A hyperbolic curve based on the Kd equation (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002610#s4" target="_blank">Methods</a>) was modeled using the band shift data, indicating a Kd of 42.6 pM (95% confidence interval of 36.8–49.4 pM). D) A hyperbolic curve based on the Kd equation (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002610#s4" target="_blank">Methods</a>) was modeled using the band shift data, indicating a Kd of 224 pM (95% confidence interval of 181–276 pM). E) The intensity of each isolated HSE in the <i>Drosophila</i> genome is transformed to an absolute Kd using the absolute Kds calculated from band shift data in panels A and B. The Kd values range from 40–400 pM.</p

Identification of breast cancer associated variants that modulate transcription factor binding

<div><p>Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed <i>de novo</i> motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.</p></div

The most highly expressed TFs with paralogous DNA binding domains are most relevant to breast cancer.

<p>The relative expression of TFs that recognize the same sequence motif can identify the top candidate functional TFs. FOXA1 and ESR1 are the highest expressed TF in each of their TF families. We quantified gene expression using TCGA breast cancer patient solid tumor samples [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006761#pgen.1006761.ref057" target="_blank">57</a>].</p

Reference SNP rs4414128 affects CTCF binding as measured by allele-specific ChIP-seq among many diploid, heterozygous cell lines.

<p>We analyzed allele-specific binding of all ENCODE cell lines with reported normal karyotype that are heterozygous at rs4414128. CTCF binding is unbalanced in favor of the C allele, which conforms more strongly to the consensus sequence.</p