Multiscale Representation of Genomic Signals

Genomic information is encoded on a wide range of distance scales, ranging from tens of base pairs to megabases. We developed a multiscale framework to analyze and visualize the information content of genomic signals. Different types of signals, such as GC content or DNA methylation, are characterized by distinct patterns of signal enrichment or depletion across scales spanning several orders of magnitude. These patterns are associated with a variety of genomic annotations, including genes, nuclear lamina associated domains, and repeat elements. By integrating the information across all scales, as compared to using any single scale, we demonstrate improved prediction of gene expression from Polymerase II chromatin immunoprecipitation sequencing (ChIP-seq) measurements and we observed that gene expression differences in colorectal cancer are not most strongly related to gene body methylation, but rather to methylation patterns that extend beyond the single-gene scale.