For ChIP-seq experiments, oftentimes thousands of transcription factor binding sites or histone modification sites are identified. by assessment of Gene Ontology (GO) terms ( 1–3). Gene set enrichment testing is one way to determine how these lists of genomic regions or genes are related biologically, e.g. Genome-wide high-throughput experiments can assess transcription factor binding, epigenetic marks, differential gene expression or disease association, and often result in thousands of identified genomic regions or genes. ChIP-Enrich is available as a web interface ( ) and Bioconductor package. We also identify known and potential new biological functions of GRα. We identify DNA-binding proteins, including CTCF, JunD and glucocorticoid receptor α (GRα), that show different enrichment patterns for peaks closer to versus further from transcription start sites. We show that ChIP-Enrich has a well-calibrated type I error rate using permuted ENCODE ChIP-seq data sets in contrast, two commonly used gene set enrichment methods, Fisher's exact test and the binomial test implemented in Genomic Regions Enrichment of Annotations Tool (GREAT), can have highly inflated type I error rates and biases in ranking. Unlike alternative methods, ChIP-Enrich can account for the wide range of gene locus length-to-peak presence relationships (observed in ENCODE ChIP-seq data sets). Adjustment for gene locus length is necessary because it is often positively associated with the presence of one or more peaks and because many biologically defined gene sets have an excess of genes with longer or shorter gene locus lengths. Here, we develop a method, ChIP-Enrich, for this analysis which empirically adjusts for gene locus length (the length of the gene body and its surrounding non-coding sequence). Gene set enrichment testing can enhance the biological interpretation of ChIP-seq data.
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