Dataset Information

Characterizing protein-DNA binding event subtypes in ChIP-exo data

ABSTRACT: Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each genomic recruitment mechanism may be associated with distinct motifs, and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5’ to 3’ exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo sequencing tag patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type, or rely on the presence of DNA motifs to cluster binding events into subtypes. To systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype membership of protein-DNA binding events using both ChIP-exo tag enrichment patterns and DNA sequence information, thus offering a principled and robust approach to characterizing binding subtypes in ChIP-exo data. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix detects cooperative binding interactions between FoxA1, ERalpha, and CTCF, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.

ORGANISM(S): Saccharomyces cerevisiae Homo sapiens

PROVIDER: GSE110502 | GEO | 2018/02/13

REPOSITORIES: GEO

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Project description:Each protein within a regulatory complex associates with the genome by either binding DNA directly or by forming protein-protein interactions with DNA-bound proteins. In the chromatin immunoprecipitation (ChIP) assay, each protein’s unique mode of genomic association may be reflected by their patterns of formaldehyde-induced crosslinks to the DNA sequences that are in very close proximity. The ChIP-exo protocol precisely delineates protein-DNA crosslinking patterns by combining ChIP with 5' to 3' exonuclease digestion. Within a regulatory complex, the physical distance of a regulatory protein to the DNA affects crosslinking efficiencies. Therefore, the spatial organization of a protein-DNA complex could potentially be inferred by analyzing how crosslinking signatures vary between the subunits of a regulatory complex, and how they remain consistent over a set of coordinately regulated regions. Here, we present a computational framework that aligns ChIP-exo crosslinking patterns from multiple proteins across a set of regulatory regions, and which detects and quantifies protein-DNA crosslinking events within the aligned profiles. Our gapped multiple profile alignment approach does not rely on sequence motif features, but rather operates directly on the multi-protein, strand separated ChIP-exo tag patterns. The output of the alignment approach is a set of composite profiles that represent the crosslinking signatures of the complex across analyzed regulatory regions. We then use a probabilistic mixture model to deconvolve individual crosslinking events within the aligned ChIP-exo profiles, enabling consistent measurements of protein-DNA crosslinking strengths across multiple proteins. Lastly, we apply dimensionality reduction to visualize the relative organization of proteins within the regulatory complex. We demonstrate our approach by applying it to characterize regulatory complex organization in three biological settings. Firstly, we demonstrate that our alignment approach can recover the known organization of regulatory proteins at yeast ribosomal protein genes, without relying on any DNA sequence features. Secondly, we apply our gapped alignment and crosslinking quantification approaches to a novel set of ChIP-exo data to characterize the spatial organization of Pol III transcriptional machinery assembly at yeast tRNA genes. Finally, we demonstrate that our approach can be used to quantify changes in protein-DNA complex organization when applied to ChIP-nexus data from Drosophila Pol II transcriptional components in two experimental conditions. Our results suggest that principled analyses of ChIP-exo crosslinking patterns enable inference of spatial organization within protein-DNA complexes.