L0segmentation enables data-driven concise representations of diverse epigenomic data

Abstract

AbstractMotivationEpigenetic assays using next-generation sequencing (NGS) have furthered our understanding of the functional genomic regions and the mechanisms of gene regulation. However, a single assay produces billions of data represented by nucleotide resolution signal tracks. The signal strength at a given nucleotide is subject to numerous sources of technical and biological noise and thus conveys limited information about the underlying biological state. In order to draw biological conclusions, data is typically summarized into higher order patterns. Numerous specialized algorithms for summarizing epigenetic signal have been proposed and include methods for peak calling or finding differentially methylated regions. A key unifying principle underlying these approaches is that they all leverage the strong prior that signal must be locally consistent.ResultsWe proposeL0segmentation as a universal framework for extracting locally coherent signals for diverse epigenetic sources.L0serves to both compress and smooth the input signal by approximating it as piece-wise constant. We implement a highly scalableL0segmentation with additional loss functions designed for NGS epigenetic data types including Poisson loss for single tracks and binomial loss for methylation/coverage data. We show that theL0segmentation approach retains the salient features of the data over a wide range of compression values and can identify subtle features, such as transcription end sites, missed by other analytic approaches.AvailabilityOur approach is implemented as an R package “l01segmentation” with a C++ backend. Available athttps://github.com/boooooogey/l01segmentation.