RT Journal
A1 Morin, Alexander
A1 Chu, Eric Ching-Pan
A1 Sharma, Aman
A1 Adrian-Hamazaki, 
Alex
A1 Pavlidis, Paul
T1 Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets
JF Genome Research 
JO Genome Research 
YR 2023 
FD May 01 
VO 33 
IS 5 
SP 763 
OP 778 
DO 10.1101/gr.277273.122 
UL http://genome.cshlp.org/content/33/5/763.abstract 
AB 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.