RESUMEN

Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73-98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.

Asunto(s)

Elementos de Facilitación Genéticos , Regulación del Desarrollo de la Expresión Génica , Aprendizaje Automático , Animales , Desoxirribonucleasa I , Drosophila melanogaster/embriología , Drosophila melanogaster/genética , Desarrollo Embrionario/genética , Genes Reporteros , Código de Histonas , Motivos de Nucleótidos , Regiones Promotoras Genéticas , Análisis de Secuencia de ADN , Factores de Transcripción/metabolismo