Hi-C is a common technique for assessing 3D chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline Hi-C-based TE analyzer (HiTea) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole-genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE-insertion landscape. We employ the pipeline to identify TE-insertions from human cell-line Hi-C samples. AVAILABILITY AND IMPLEMENTATION: HiTea is available at https://github.com/parklab/HiTea and as a Docker image. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
A large amount of genomic data for profiling three-dimensional genome architecture have accumulated from large-scale consortium projects as well as from individual laboratories. In this review, we summarize recent landmark datasets and collections in the field. We describe the challenges in collection, annotation, and analysis of these data, particularly for integration of sequencing and microscopy data. We introduce efforts from consortia and independent groups to harmonize diverse datasets. As the resolution and throughput of sequencing and imaging technologies continue to increase, more efficient utilization and integration of collected data will be critical for a better understanding of nuclear architecture.
Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer.
The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions.
Single-cell Hi-C (scHi-C) allows the study of cell-to-cell variability in chromatin structure and dynamics. However, the high level of noise inherent in current scHi-C protocols necessitates careful assessment of data quality before biological conclusions can be drawn. Here we present GiniQC, which quantifies unevenness in the distribution of inter-chromosomal reads in the scHi-C contact matrix to measure the level of noise. Our examples show the utility of GiniQC in assessing the quality of scHi-C data as a complement to existing quality control measures. We also demonstrate how GiniQC can help inform the impact of various data processing steps on data quality.
We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.
The 4D Nucleome Network aims to develop and apply approaches to map the structure and dynamics of the human and mouse genomes in space and time with the goal of gaining deeper mechanistic insights into how the nucleus is organized and functions. The project will develop and benchmark experimental and computational approaches for measuring genome conformation and nuclear organization, and investigate how these contribute to gene regulation and other genome functions. Validated experimental technologies will be combined with biophysical approaches to generate quantitative models of spatial genome organization in different biological states, both in cell populations and in single cells.
The chromatin state of pluripotency genes has been studied extensively in embryonic stem cells (ESCs) and differentiated cells, but their potential interactions with other parts of the genome remain largely unexplored. Here, we identified a genome-wide, pluripotency-specific interaction network around the Nanog promoter by adapting circular chromosome conformation capture sequencing. This network was rearranged during differentiation and restored in induced pluripotent stem cells. A large fraction of Nanog-interacting loci were bound by Mediator or cohesin in pluripotent cells. Depletion of these proteins from ESCs resulted in a disruption of contacts and the acquisition of a differentiation-specific interaction pattern prior to obvious transcriptional and phenotypic changes. Similarly, the establishment of Nanog interactions during reprogramming often preceded transcriptional upregulation of associated genes, suggesting a causative link. Our results document a complex, pluripotency-specific chromatin "interactome" for Nanog and suggest a functional role for long-range genomic interactions in the maintenance and induction of pluripotency.