Visualizes Hi-C data generated from 21 human primary tissues and cell liens. HUGIn a web browser that enables assessment of chromatin contacts both constitutive across and specific to tissue(s) and/or cell line(s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the understanding of both GWAS and eQTLs results and functional genomics data. HUGIn also hosts gene expression and a rich collection of epigenomic data, including typical and super enhancers.
Explores Hi-C and other contact map data. Juicebox allows users to zoom in and out of Hi-C maps interactively. It integrates many technologies developed for the Integrative Genomics Viewer with a broad ensemble of methods specifically designed for handling 2D contact data. Individual maps can be normalized (corrected for experimental bias), compared to one-dimensional tracks (such as gene tracks or chromatin immunoprecipitation sequencing data), and compared to 2D feature lists (such as loop and domain annotations).
Allows users to visualize and explore chromatin interaction data, such as Hi-C, ChIA-PET, Capture Hi-C, PLAC-Seq, and more. 3D Genome Browser also allows users to browse other omics data such as ChIP-Seq or RNA-Seq for the same genomic region, and gain a complete view of both regulatory landscape and 3D genome structure for any given gene. Users can also check the expression of any queried gene across hundreds of tissue/cell types measured by the ENCODE consortium. Finally, the virtual 4C page provides multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET and cross-cell-type correlation of proximal and distal DHSs.
An R/Bioconductor package that allows flexible integration of genomic visualizations into highly customizable, publication-ready, multi-panel figures from common genomic data formats including Browser Extensible Data (BED), bedGraph and Browser Extensible Data Paired-End (BEDPE). Sushi fills a critical void among currently available visualization tools by providing a means to easily produce sophisticated, customizable, genomic visualizations.
Simplifies the Hi-C data pre-processing, contact matrix transformation, and topologically associating domain (TAD) calling into a few easy steps. HiCExplorer is a tool-suite that can be used with other pipelines and processing tools as we have built-in import/export functions covering commonly used Hi-C data formats. This method works with HiCBrowser, a browser and an underlying program to visualize Hi-C and other genomic tracks.
Provides an online 5C tool for the rapid design of 5C primers. my5C allows complete control over extremely complex 5C design schemes. This tool is composed of two modules. The “my5C.primers” module is used to design 5C primers for restriction fragments throughout user-defined genomic regions. In the second module, “my5C.heatmap”, datasets are visualized as two-dimensional heatmaps where each datapoint corresponds to an interaction frequency between two loci. Users can download any data displayed as a heatmap as tables or as lists of pairwise interactions. Furthermore, to ensure confidentiality all data are password protected and users can opt not to store data on the my5C server.
A software package for rigorous detection of differential interactions from Hi-C data. diffHic provides methods for read pair alignment and processing, counting into bin pairs, filtering out low-abundance events and normalization of trended or CNV-driven biases. It uses the statistical framework of the edgeR package to model biological variability and to test for significant differences between conditions. Several options for the visualization of results are also included. On real data, diffHic is able to successfully detect interactions with significant differences in intensity between biological conditions. It also compares favourably to existing software tools on simulated data sets. diffHic is able to accommodate complex experimental designs, including paired or blocked designs and those with more than two groups. It does this by accessing the generalized linear model functionality of edgeR.