Differential chromatin profiles partially determine transcription factor binding

<div><p>We characterize how genomic variants that alter chromatin accessibility influence regulatory factor binding with a new method called DeltaBind that predicts condition specific factor binding more accurately than other methods based on DNase-seq data. Using DeltaBind and DNase-seq experiments we predicted the differential binding of 18 factors in K562 and GM12878 cells with an average precision of 28% at 10% recall, with the prediction of individual factors ranging from 5% to 65% precision. We further found that genome variants that alter chromatin accessibility are not necessarily predictive of altering proximal factor binding. Taken together these findings suggest that DNase-seq or ATAC-seq Quantitative Trait Loci (dsQTLs), while important, must be considered in a broader context to establish causality for phenotypic changes.</p></div

AUPR for 18 tested factors using 3 different methods.

<p>AUPR for 18 tested factors using 3 different methods.</p

Comparison of prediction performance for pioneer class factors and non-pioneer (settler and migrant) class factors.

<p>Comparison of prediction performance for pioneer class factors and non-pioneer (settler and migrant) class factors.</p

DeltaBind decision boundaries (orange) of different confidence levels.

<p>Axes are K562 PIQ rank vs. GM12878 PIQ rank. Red represents true differential sites indicated by ChIP-seq signals.</p

Summary statistics of differential binding prediction for different factors.

<p>Summary statistics of differential binding prediction for different factors.</p

Spatial binding constraints detected from ENCODE ChIP-Seq datasets.

<p>Matrix representation of pairwise spatial binding constraints between factor B (column) and factor A (row) detected from 37 ChIP-Seq dataset in human K562 cells. The colors and numbers represent the number of positions exhibiting significant spatial binding constraints within the 201 bp window around the binding sites of factor B (column).</p

Examples of transcription factor spatial binding constraints detected from GEM analysis.

<p><b>A</b>) Genome wide spatial distribution of USF1 binding sites in a 201 bp window around c-Jun binding sites. <b>B</b>) Egr1 binding sites around CTCF binding sites. <b>C</b>) FOXA1 binding sites around HNF4α binding sites. Vertical dashed lines represent the centered factor binding sites at position 0; horizontal dashed lines represent the number of occurrences at a position corresponding to corrected p-value of 1e−8.</p

High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints

<div><p>An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of <em>in vivo</em> spacing constraints between key transcription factors would reveal key aspects of this genomic regulatory language. To discover novel transcription factor spatial binding constraints <em>in vivo</em>, we developed a new integrative computational method, genome wide event finding and motif discovery (GEM). GEM resolves ChIP data into explanatory motifs and binding events at high spatial resolution by linking binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. GEM analysis of 63 transcription factors in 214 ENCODE human ChIP-Seq experiments recovers more known factor motifs than other contemporary methods, and discovers six new motifs for factors with unknown binding specificity. GEM's adaptive learning of binding-event read distributions allows it to further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of closely spaced binding events of the same factor. In a systematic analysis of <em>in vivo</em> sequence-specific transcription factor binding using GEM, we have found hundreds of spatial binding constraints between factors. GEM found 37 examples of factor binding constraints in mouse ES cells, including strong distance-specific constraints between Klf4 and other key regulatory factors. In human ENCODE data, GEM found 390 examples of spatially constrained pair-wise binding, including such novel pairs as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4A/FOXA1. The discovery of new factor-factor spatial constraints in ChIP data is significant because it proposes testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control.</p> </div

GEM motif discovery outperforms other methods when detecting motifs in ChIP-Seq data.

<p>The motif detection performance of GEM is compared to the motif detection performance of various motif-finders on 214 ENCODE ChIP-Seq experiments.</p

GEM reveals transcription factor spatial binding constraints.

<p><b>A</b>), <b>B</b>), and <b>C</b>) Genome wide spatial distribution of Oct4 binding sites in a 201 bp window around Sox2 binding sites, obtained by using GEM binding calls, GPS binding calls, or GPS binding calls snapping to the nearest motifs within 50 bp, respectively. Dashed lines represent the Sox2 binding sites at position 0.</p