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Chromatin is a polymer complex of DNA and proteins that regulates geneexpression. The three-dimensional structure and organization of chromatincontrols DNA transcription and replication. High-throughput chromatinconformation capture techniques generate Hi-C maps that can provide insightinto the 3D structure of chromatin. Hi-C maps can be represented as a symmetricmatrix where each element represents the average contact probability or numberof contacts between two chromatin loci. Previous studies have detectedtopologically associating domains (TADs), or self-interacting regions in Hi-Cmaps within which the contact probability is greater than that outside theregion. Many algorithms have been developed to identify TADs within Hi-C maps.However, most TAD identification algorithms are unable to identify nested oroverlapping TADs and for a given Hi-C map there is significant variation in thelocation and number of TADs identified by different methods. We develop a novelmethod, KerTAD, using a kernel-based technique from computer vision and imageprocessing that is able to accurately identify nested and overlapping TADs. Webenchmark this method against state-of-the-art TAD identification methods onboth synthetic and experimental data sets. We find that KerTAD consistently hashigher true positive rates (TPR) and lower false discovery rates (FDR) than alltested methods for both synthetic and manually annotated experimental Hi-Cmaps. The TPR for KerTAD is also largely insensitive to increasing noise andsparsity, in contrast to the other methods. We also find that KerTAD isconsistent in the number and size of TADs identified across replicateexperimental Hi-C maps for several organisms. KerTAD will improve automated TADidentification and enable researchers to better correlate changes in TADs tobiological phenomena, such as enhancer-promoter interactions and diseasestates.

Identifying topologically associating domains using differential kernels