EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

Abstract The genomewide signal profiles from functional genomics experiments are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinct patterns along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in many epigenetic assays such as ChIP-Seq and bisulfite sequencing. Valleys mark locations of cis-regulatory elements such as enhancers. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements using epigenetic data. Nevertheless, the valleys are generally not reported by analysis pipelines. Here, we describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification and DNA methylation valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes.