HiC-3DViewer： a new tool to visualize Hi-C data in 3D space

Background： Although significant progress has been made to map chromatin structure at unprecedented resolution and scales, we are short of tools that enable the intuitive visualization and navigation along the three-dimensional （3D） structure of chromatins. The available tools people have so far are generally script-based or present basic features that do not easily enable the integration of genomic data along with 3D chromatin structure, hence, many scientists find themselves in the obligation to hack tools designed for other purposes such as tools for protein structure study. Methods： We present HiC-3DViewer, a new browser-based interactive tool designed to provide an intuitive environment for investigators to facilitate the 3D exploratory analysis of Hi-C data along with many useful annotation functionalities. Among the key features of HiC-3DViewer relevant to chromatin conformation studies, the most important one is the 1D-to-2D-to-3D mapping, to highlight genomic regions of interest interactively. This feature enables investigators to explore their data at different levels/angels. Additionally, investigators can superpose different genomic signals （such as ChIP-Seq, SNP） on the top of the 3D structure. Results： As a proof of principle we applied HiC-3DViewer to investigate the quality of Hi-C data and to show the spatial binding of GATA1 and GATA2 along the genome. Conclusions： As a user-friendly tool, HiC-3DViewer enables the visualization of inter/intra-chromatin interactions and gives users the flexibility to customize the look-and-feel of the 3D structure with a simple click. HiC-3DViewer is implemented in Javascript and Python, and is freely available at： http：//bioinfo.au.tsinghua.edu.cn/member/nadhir/ HiC3DViewer/. Supplementary information （User Manual, demo data） is also available at this website.

Building a sequence map of the pig pan-genome from multiple de novo assemblies and Hi-C data

Pigs were domesticated independently in the Near East and China,indicating that a single reference genome from one individual is unable to represent the full spectrum of divergent sequences in pigs worldwide.Therefore,12 de novo pig assemblies from Eurasia were compared in this study to identify the missing sequences from the reference genome.As a result,72.5 Mb of nonredundant sequences(~3% of the genome)were found to be absent from the reference genome(Sscrofa11.1)and were defined as pan-sequences.Of the pan-sequences,9.0 Mb were dominant in Chinese pigs,in contrast with their low frequency in European pigs.One sequence dominant in Chinese pigs contained the complete genic region of the tazarotene-induced gene 3(TIG3)gene which is involved in fatty acid metabolism.Using flanking sequences and Hi-C based methods,27.7% of the sequences could be anchored to the reference genome.The supplementation of these sequences could contribute to the accurate interpretation of the 3D chromatin structure.A web-based pan-genome database was further provided to serve as a primary resource for exploration of genetic diversity and promote pig breeding and biomedical research.

Computational tools for Hi-C data analysis

Background： In eukaryotic genome, chromatin is not randomly distributed in cell nuclei, but instead is organized into higher-order structures. Emerging evidence indicates that these higher-order chromatin structures play important roles in regulating genome functions such as transcription and DNA replication. With the advancement in 3C （chromosome conformation capture） based technologies, Hi-C has been widely used to investigate genome-wide long- range chromatin interactions during cellular differentiation and oncogenesis. Since the first publication of Hi-C assay in 2009, lots of bioinformatic tools have been implemented for processing Hi-C data from mapping raw reads to normalizing contact matrix and high interpretation, either providing a whole workflow pipeline or focusing on a particular process. Results： This article reviews the general Hi-C data processing workflow and the currently popular Hi-C data processing tools. We highlight on how these tools are used for a full interpretation of Hi-C results. Conclusions： Hi-C assay is a powerful tool to investigate the higher-order chromatin structure. Continued development of novel methods for Hi-C data analysis will be necessary for better understanding the regulatory function of genome organization.

调控染色质高级结构的蛋白质机器的系统鉴定与机制研究

染色质高级结构及功能研究是当前生命科学领域的国际前沿热点。染色质高级结构决定了基因的正确表达,其异常通常伴随着发育畸形和癌症发生。迄今为止,染色质高级结构如何建立和维持、如何行使功能、其动态变化及其与基因、细胞功能的关系尚不清楚。本项目整合团队在染色质高级结构蛋白质机器的功能基因组学、染色质高级结构蛋白质机器的功能动态调控以及染色质高级结构蛋白质机器的结构与组装方面的优势,围绕"染色质高级结构的调控及功能"这一关键科学问题,从3个角度深入研究拓扑相关结构域的形成与功能相关的蛋白质机器,包括调控染色质高级结构蛋白质机器的系统鉴定、单细胞和全基因组水平上染色质高级结构动态调控的机理研究、染色质高级结构对基因和细胞功能调控的结构基础和分子机制。