Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

Complex patterns of cell-type-specific gene expression are thought to be achieved by combinatorial binding of transcription factors (TFs) to sequence elements in regulatory regions. Predicting cell-type-specific expression in mammals has been hindered by the oftentimes unknown location of distal regulatory regions. To alleviate this bottleneck, we used DNase-seq data from 19 diverse human cell types to identify proximal and distal regulatory elements at genome-wide scale. Matched expression data allowed us to separate genes into classes of cell-type-specific up-regulated, down-regulated, and constitutively expressed genes. CG dinucleotide content and DNA accessibility in the promoters of these three classes of genes displayed substantial differences, highlighting the importance of including these aspects in modeling gene expression. We associated DNase I hypersensitive sites (DHSs) with genes, and trained classifiers for different expression patterns. TF sequence motif matches in DHSs provided a strong performance improvement in predicting gene expression over the typical baseline approach of using proximal promoter sequences. In particular, we achieved competitive performance when discriminating up-regulated genes from different cell types or genes up-and down-regulated under the same conditions. We identified previously known and new candidate cell-type-specific regulators. The models generated testable predictions of activating or repressive functions of regulators. DNase I footprints for these regulators were indicative of their direct binding to DNA. In summary, we successfully used information of open chromatin obtained by a single assay, DNase-seq, to address the problem of predicting cell-type-specific gene expression in mammalian organisms directly from regulatory sequence.

Functional changes in spatial genome organization during human development are poorly understood. Here we report a comprehensive profile of nuclear dynamics during human cardiogenesis from pluripotent stem cells by integrating Hi-C, RNA-seq and ATAC-seq. While chromatin accessibility and gene expression show complex on/off dynamics, large-scale genome architecture changes are mostly unidirectional. Many large cardiac genes transition from a repressive to an active compartment during differentiation, coincident with upregulation. We identify a network of such gene loci that increase their association inter-chromosomally, and are targets of the muscle-specific splicing factor RBM20. Genome editing studies show that TTN pre-mRNA, the main RBM20-regulated transcript in the heart, nucleates RBM20 foci that drive spatial proximity between the TTN locus and other inter-chromosomal RBM20 targets such as CACNA1C and CAMK2D . This mechanism promotes RBM20-dependent alternative splicing of the resulting transcripts, indicating the existence of a cardiac-specific trans -interacting chromatin domain (TID) functioning as a splicing factory.