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

Motivation Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein‐protein interactions with other regulators. Each 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′ → 3′ exonuclease digestion. Since different regulatory complexes will result in different protein‐DNA crosslinking signatures, analysis of ChIP‐exo tag enrichment 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 motifs to cluster binding events into subtypes. Results 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 memberships of binding events using both ChIP‐exo tag distribution patterns and DNA motifs. 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 identifies possible recruitment mechanisms of FoxA1 and ER&agr;, thus demonstrating that ChExMix can effectively stratify ChIP‐exo binding events into biologically meaningful subtypes. Availability and implementation ChExMix is available from https://github.com/seqcode/chexmix. Supplementary information Supplementary data are available at Bioinformatics online.