Genome-wide ChIP-exo profiling of Fpt1 in glucose
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ABSTRACT:
PROVIDER: PRJNA950350 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24016020_1.fastq.gz | Fastqsanger.gz | |||
| SRR24016020_2.fastq.gz | Fastqsanger.gz | |||
| SRR24016021_1.fastq.gz | Fastqsanger.gz | |||
| SRR24016021_2.fastq.gz | Fastqsanger.gz | |||
| SRR24016022_1.fastq.gz | Fastqsanger.gz |
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Project description:Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) and its ultra-high resolution cousin ChIP-exo are methods that identify where proteins bind along any genome in vivo. ChIP-exo achieves near-base pair resolution by creating exonuclease stop sites just 5’ to where formaldehyde-induced protein-DNA cross-links occur. Whereas construction of ChIP genomic libraries is straightforward and widely adopted for ChIP-seq, ChIP-exo is technically more involved which has resulted in limited adoption. Here we describe multiple ChIP-exo protocols, each with use-specific advantages and limitations. The new versions are greatly simplified through removal of multiple enzymatic steps. This is achieved in part through the use of Tn5 tagmentation and/or single-stranded DNA ligation. The result is greater library yields, lower processing time, and lower cost. A similar streamlined approach was developed for ChIP-seq, called ChIP-seq 1-step, where library construction is achieved in one-step.
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Project description: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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