Dataset Information

Knock-down of BCL6 expression in human Diffuse Large B-Cell Lymphoma cell lines

ABSTRACT: This dataset was used to benchmark the Virtual Inference of Protein-activity by Regulon Readout algorithm (VIPER). Despite recent advances in molecular profiling, proteome-wide assessment of protein activity in individual samples remains a highly elusive target. In stark contrast, protein activity quantitation is increasingly critical to the dissection of key regulatory processes and to the elucidation of biologically relevant mechanisms. Importantly, its value extends to the study of drug activity, as most small molecules inhibit activity of their cognate protein substrates without affecting the protein’s or associated mRNA’s abundance. VIPER leverages the increasingly accurate and context specific knowledge of regulatory networks. Specifically, it uses the expression of the transcriptional targets most directly regulated by a given protein in an individual sample as a reporter for the computational inference of its activity.

ORGANISM(S): Homo sapiens

PROVIDER: GSE45838 | GEO | 2013/09/01

SECONDARY ACCESSION(S): PRJNA196519

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.

Dataset Information

Knock-down of BCL6 expression in human Diffuse Large B-Cell Lymphoma cell lines

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