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• W4380359899 abstract "Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. Although research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we show a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR–target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR–target listings, as well as transparent experiment-level gene summaries for community use." @default.
• W4380359899 created "2023-06-13" @default.
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• W4380359899 creator A5088091812 @default.
• W4380359899 date "2023-05-01" @default.
• W4380359899 modified "2023-09-23" @default.
• W4380359899 title "Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets" @default.
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• W4380359899 doi "https://doi.org/10.1101/gr.277273.122" @default.
• W4380359899 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37308292" @default.
• W4380359899 hasPublicationYear "2023" @default.
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