Identifying topologically associating domains using differential kernels

Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix A i j, where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in A i j within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states.Author summary: Chromatin, which encodes the genetic information for cells, must fold into the cell nucleus that is many times smaller in size. The folded 3D structure of chromatin in the nucleus enables gene expression and proper cell function. With the advent of advanced chromatin conformation capture techniques, we can identify topologically associating domains (TADs), which are regions of the genome that prefer to interact within themselves rather than with neighboring regions. Numerous methods have been developed to automatically detect TADs in Hi-C maps, however, they frequently disagree on the location and number of TADs. We develop a new algorithm, KerTAD, to identify TADs using techniques from image processing and computer vision. We find that our method is more accurate on both synthetic and manually-annotated experimental Hi-C maps than all tested methods. Our method also performs well in the presence of noise and sparsity, which are frequently encountered in experimental Hi-C maps. KerTAD will enable future studies to elucidate the role of TADs in gene regulation and disease formation.