title: Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes.
abstract: We take a comprehensive approach to the study of regulatory control of gene expression in melanocytes that proceeds from large-scale enhancer discovery facilitated by ChIP-seq; to rigorous validation in silico, in vitro, and in vivo; and finally to the use of machine learning to elucidate a regulatory vocabulary with genome-wide predictive power. We identify 2489 putative melanocyte enhancer loci in the mouse genome by ChIP-seq for EP300 and H3K4me1. We demonstrate that these putative enhancers are evolutionarily constrained, enriched for sequence motifs predicted to bind key melanocyte transcription factors, located near genes relevant to melanocyte biology, and capable of driving reporter gene expression in melanocytes in culture (86%; 43/50) and in transgenic zebrafish (70%; 7/10). Next, using the sequences of these putative enhancers as a training set for a supervised machine learning algorithm, we develop a vocabulary of 6-mers predictive of melanocyte enhancer function. Lastly, we demonstrate that this vocabulary has genome-wide predictive power in both the mouse and human genomes. This study provides deep insight into the regulation of gene expression in melanocytes and demonstrates a powerful approach to the investigation of regulatory sequences that can be applied to other cell types.
authors.affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
authors.forename: David U
authors.initials: DU
authors.lastname: Gorkin
authors.forename: Dongwon
authors.initials: D
authors.lastname: Lee
authors.forename: Xylena
authors.initials: X
authors.lastname: Reed
authors.forename: Christopher
authors.initials: C
authors.lastname: Fletez-Brant
authors.forename: Seneca L
authors.initials: SL
authors.lastname: Bessling
authors.forename: Stacie K
authors.initials: SK
authors.lastname: Loftus
authors.forename: Michael A
authors.initials: MA
authors.lastname: Beer
authors.forename: William J
authors.initials: WJ
authors.lastname: Pavan
authors.forename: Andrew S
authors.initials: AS
authors.lastname: McCallion
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D050881:E1A-Associated p300 Protein / Q000235:genetics / Q000378:metabolism; D004742:Enhancer Elements, Genetic; D019143:Evolution, Molecular; D005786:Gene Expression Regulation; D017930:Genes, Reporter; D015894:Genome, Human; D006657:Histones / Q000378:metabolism; D006801:Humans; D008544:Melanocytes / Q000378:metabolism*; D051379:Mice; D017422:Sequence Analysis, DNA / Q000379:methods; D014157:Transcription Factors / Q000378:metabolism; D015027:Zebrafish
chemical_list: D006657:Histones; D014157:Transcription Factors; D050881:E1A-Associated p300 Protein; C494361:Ep300 protein, mouse