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| Status |
Public on Feb 13, 2018 |
| Title |
Characterizing protein-DNA binding event subtypes in ChIP-exo data |
| Organisms |
Saccharomyces cerevisiae; Homo sapiens |
| Experiment type |
Genome binding/occupancy profiling by high throughput sequencing
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| Summary |
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.
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| Overall design |
Genome-wide analysis of key regulatory factors in breast cancer progression using ChIP-exo
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| Contributor(s) |
Yamada N, Lai WK, Farrell N, Pugh BF, Mahony S |
| Citation(s) |
30165373 |
| |
| Submission date |
Feb 12, 2018 |
| Last update date |
Sep 06, 2020 |
| Contact name |
Shaun Mahony |
| E-mail(s) |
mahony@psu.edu
|
| Phone |
814-865-3008
|
| Organization name |
Penn State University
|
| Department |
Biochemistry & Molecular Biology
|
| Lab |
Shaun Mahony
|
| Street address |
404 South Frear Bldg
|
| City |
University Park |
| State/province |
PA |
| ZIP/Postal code |
16802 |
| Country |
USA |
| |
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| Platforms (2) |
| GPL18573 |
Illumina NextSeq 500 (Homo sapiens) |
| GPL19756 |
Illumina NextSeq 500 (Saccharomyces cerevisiae) |
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| Samples (9)
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| Relations |
| BioProject |
PRJNA433873 |
| SRA |
SRP132723 |
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