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.2012;8(8):e1002638.
doi: 10.1371/journal.pcbi.1002638. Epub 2012 Aug 9.

High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints

Affiliations

High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints

Yuchun Guo et al. PLoS Comput Biol.2012.

Abstract

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 in vivo spacing constraints between key transcription factors would reveal key aspects of this genomic regulatory language. To discover novel transcription factor spatial binding constraints in vivo, 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 in vivo 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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. GEM improves spatial accuracy in binding event prediction and the resolution of proximal binding events.
A) Fraction of predicted GABP binding events with a motif within the given distance following event discovery by GEM, GPS, SISSRs, MACS, cisGenome, QuEST and PeakRanger. Events shown were predicted by all seven methods and had a GABP motif within 100 bp.B) Fraction of predicted CTCF binding events with a motif within the given distance following event discovery by GEM, GPS, SISSRs, MACS, cisGenome, QuEST, FindPeaks, spp-wtd and spp-mtc. Events shown were predicted by all nine methods and had a CTCF motif within 100 bp.C) Example of a predicted binary GABP event that contains coordinately located GABP motifs.D) Numbers of GABP binding events discovered by GEM, GPS, SISSRs, MACS, cisGenome, QuEST and PeakRanger in 477 regions that contain clustered GABP motifs within 500 bp.
Figure 2
Figure 2. GEM motif discovery outperforms other methods when detecting motifs in ChIP-Seq data.
The motif detection performance of GEM is compared to the motif detection performance of various motif-finders on 214 ENCODE ChIP-Seq experiments.
Figure 3
Figure 3. GEM improves the spatial resolution of ChIP-exo data event prediction.
A) Fraction of predicted CTCF binding events with a motif within the given distance following event discovery by GEM, GPS, and the peak-pair midpoint method of Rhee, et al.B) GEM automatically adapts to the ChIP-exo read spatial distribution.
Figure 4
Figure 4. GEM reveals transcription factor spatial binding constraints.
A),B), andC) 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.
Figure 5
Figure 5. Enhancer grammar elements deduced from mouse ES cell transcription factor binding sites predicted by GEM.
A) The binding site distribution of Sox2, Klf4, Nanog, Oct4, Esrrb, Nr5a2 and Tcfcp21l in 123 regions that exhibit Sox2-Klf4 spatial binding constraints. The Sox2 sites are aligned at the 0 bp positions, and Klf4 sites are at the 25 bp positions. The rows are ordered by Esrrb offset positions.B) Color chart representation of 201 bp sequences in the same regions as inA. Each row represents a 201 bp bound sequence. Green, blue, yellow and red indicate A, C, G and T. The motif logos are generated by STAMP from the motifs discovered using all the binding sites in the respective datasets.
Figure 6
Figure 6. Spatial binding constraints detected from ENCODE ChIP-Seq datasets.
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 represent the significance levels (corrected p-value) of the strongest spacings. The numbers represent the distances between the factors in the strongest spacings.
Figure 7
Figure 7. Spatial binding constraints detected from ENCODE ChIP-Seq datasets.
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).
Figure 8
Figure 8. Examples of transcription factor spatial binding constraints detected from GEM analysis.
A) Genome wide spatial distribution of USF1 binding sites in a 201 bp window around c-Jun binding sites.B) Egr1 binding sites around CTCF binding sites.C) 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.
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