title: ChIP-Enrich: gene set enrichment testing for ChIP-seq data.
abstract: Gene set enrichment testing can enhance the biological interpretation of ChIP-seq data. 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). 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. 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). 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. 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 also identify known and potential new biological functions of GRα. ChIP-Enrich is available as a web interface (http://chip-enrich.med.umich.edu) and Bioconductor package.
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Ryan P
authors.initials: RP
authors.lastname: Welch
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Chee
authors.initials: C
authors.lastname: Lee
authors.affiliation: Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Imbriano
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Snehal
authors.initials: S
authors.lastname: Patil
authors.affiliation: Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Terry E
authors.initials: TE
authors.lastname: Weymouth
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: R Alex
authors.initials: RA
authors.lastname: Smith
authors.affiliation: Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA ljst@umich.edu.
authors.forename: Laura J
authors.initials: LJ
authors.lastname: Scott
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA sartorma@umich.edu.
authors.forename: Maureen A
authors.initials: MA
authors.lastname: Sartor
mesh_terms: D047369:Chromatin Immunoprecipitation / Q000379:methods*; D004268:DNA-Binding Proteins / Q000032:analysis; D005796:Genes; D056426:Genetic Loci; D016015:Logistic Models; D011965:Receptors, Glucocorticoid / Q000032:analysis; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004268:DNA-Binding Proteins; D011965:Receptors, Glucocorticoid; C411470:glucocorticoid receptor alpha