Here we compiled a list of tools available for data analysis and/or visualization of 4DN-related datasets.
If you have any suggestions to add/update this list, please send them to us via email: rcalandrelli@eng.ucsd.edu
If you have any suggestions to add/update this list, please send them to us via email: rcalandrelli@eng.ucsd.edu
| Software | Data Type | Description |
|---|---|---|
| 3D Genome Browser | Hi-CVirtual 4C | The 3D Genome Browser provides heatmap virtualizations of the Hi-C intra-chromosomal contract matrices. The browser allows the exploration of potential cis-regulatory elements and/or topologically associating domains (TADs) for the gene or genomic feature of interest. |
| 3DIV | Hi-C | 3DIV is a database which processed 315 Hi-C experiments from 80 human samples. 3DIV is an easy-to-use database providing epigenetic annotations and one-to-all chromatin interaction visualization with a variety of options. |
| 4C-ker | 4C | 4C-ker is a method developed to analyze 4C-Seq (circularized chromosome conformation capture) data. 4C-ker is a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. |
| AutoChrom3D | 3C, 4C, 5CHi-C | AutoChrom3D is an automatic pipeline to model chromatin regions with genomic size ranging from hundreds of kilobases to megabases at 8 kb resolution. |
| Centurion | Hi-C | A method that jointly calls all centromeres in a genome-wide Hi-C contact map. |
| ChIA-PET Browser | ChIA-PET | A software package for automatic processing of ChIA-PET sequence data, including linker filtering, mapping tags to reference genomes, identifying protein binding sites and chromatin interactions, and displaying the results on a graphical genome browser. |
| ChiaSig | ChIA-PET | ChiaSig finds significant interactions based on the NCHG model. |
| CHiCAGO | Hi-C | A set of tools for calling significant interactions in Capture Hi-C data, such as Promoter Capture Hi-C. |
| ChromContact | Hi-C | ChromContact is a web server for analyzing spatial contact of chromosomes from the publicly available Hi-C data. |
| chromoR | Hi-C | chromoR provides users with a statistical pipeline for analysing chromosomal interactions data (Hi-C data). |
| ChromSDE | Hi-C | A deterministic method which applies semi-definite programming techniques to find the best structure fitting the observed data and uses golden section search to find the correct parameter for converting the contact frequency to spatial distance. |
| CytoHiC | Hi-C | A Cytoscape plugin, which allow users to view and compare spatial maps of genomic landmarks, based on normalized Hi-C datasets. |
| diffHiC | Hi-C | A software package for the 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. |
| FisHiCal | Hi-C | FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis. |
| Fit-Hi-C | Hi-C | Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. |
| Galaxy HiCExplorer | Hi-C | A Galaxy based web server to give biomedical researchers an easy to use access to the HiCExplorer (for more information see the related item below in the table) and additional high-throughput analysis software like deeptools or mappers like BWA-MEM or Bowtie are provided. |
| GenomicInteractions | Hi-CChIA-PET | R package for handling Genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots/statistics. |
| GITAR | Hi-C | GITAR (Genome Interaction Tools and Resources) is a standardized way to analyze and visualize genomic interaction data. The tool is composed of two modules: HiCtool (a standardized Python library for processing and visualizing Hi-C data) and processed data (a collection of datasets processed using HiCtool). The pipeline performs also TADs analysis. |
| GIVE | Hi-CChIA-PET | GIVE is an open source programming library that allows anyone with HTML programming experience to build custom genome browser websites or apps. With a few lines of codes, one can add to a personal webpage an interactive genome browser that host custom data. It typically takes less than half a day to build a genome browser website with GIVE. |
| GOTHiC | Hi-C | A Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. |
| HiBrowse | Hi-C | A statistical web toolkit for analyzing, interpreting and visualizing genome-wide chromosome conformation capture data, such as Hi-C, TCC, GCC and similar. |
| HiCapp | Hi-C | HiCapp is a Hi-C analysis pipeline which can correct for the copy number bias in tumor Hi-C data using caICB correction algorithm. |
| HiCdat | Hi-C | HiCdat provides a simple graphical user interface for data pre-processing and a collection of higher-level data analysis tools implemented in R. |
| Hi-C Data Browser | Hi-C | The tool enables the generation of genome-wide 3D proximity maps. You will also be able to visually navigate the dataset and explore Hi-C proximity maps of entire human genomes. |
| HiC-inspector | Hi-C | A bioinformatics pipeline for the automated analysis of data generated by high-throughput chromatin conformation capture (HiC). |
| HiCExplorer | Hi-C | Hi-C analysis software which covers the whole pipeline of Hi-C data analysis. Starting with the creation of the contact matrix, the correction, computation of TADs and A / B compartments to a visualization of these. Additional data tracks from other high-throughput analysis like ChIP-Seq or gene tracks can be added to the visualization. |
| HiCNorm | Hi-C | A parametric model to remove systematic biases in the raw Hi-C contact maps, resulting in a simple, fast, yet accurate normalization procedure. |
| Hi-Corrector | Hi-C | A fast, scalable and memory-efficient package for normalizing large-scale Hi-C data. |
| Hi-C Pipeline | Hi-C | Hicpipe is a set of scripts and programs that correct Hi-C contact maps, given a list restriction enzyme sites and mapped paired reads. |
| HiCPlotter | Hi-C | An easy-to-use open-source visualization tool to facilitate juxtaposition of Hi-C matrices with diverse genomic assay outputs, as well as to compare interaction matrices between various conditions. |
| HiC-bench | Hi-C | HiC-bench is a tool for a comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking. |
| HiC-Pro | Hi-C | HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. |
| HiCseg | Hi-C | This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue. |
| hiCtools | Hi-C | HiC is high-throughput sequencing technology applied to chromatin conformation techniques. This collection of tools stream-lines the processing of HiC data from raw sequence to contact matrices and beyond. |
| HiCUP | Hi-C | A tool for mapping and performing quality control on Hi-C data. |
| HiFive | 5CHi-C | The HiFive tool suite provides efficient data handling and a variety of normalization approaches for easy, fast analysis and method comparison. |
| HiGlass | Interaction matrices | HiGlass is a tool for displaying and comparing large matrices within a web page. Matrices are typically visualized as heatmaps. In the case of large matrices, the heatmaps are too large to render all at once. Instead, we aggregate their values and display summaries at lower resolution while allowing one to zoom in and explore them in greater detail. |
| HiPiler | Hi-C | HiPiler is an interactive web application for feature-centric exploration of Hi-C matrices. HiPiler represents 2D features or annotations (like loops, TADs, CTCF sites, etc.) as individual thumbnail-like snippets. Snippets can be laid out automatically based on their data and meta attributes. They can be interactively aggregated into piles for stratification and are visually linked back to the matrix to provide context. |
| HIPPIE | Hi-C | HIPPIE is the workflow for analyzing batches of Hi-C paired-end reads in compressed FASTQ format (.fastq.gz) and predict enhancer-target gene interactions. |
| HiTC | 5CHi-C | The package explores high-throughput 'C' data such as 5C or Hi-C. |
| HMRFBayesHiC | Hi-C | A hidden Markov random field based Bayesian peak caller to identify long range chromatin interactions from Hi-C data. |
| HOMER | Hi-C | HOMER contains several programs and analysis routines to facilitate the analysis of Hi-C data. |
| HubPredictor | Hi-C | A computational model integrating Hi-C and histone mark ChIP-seq data to predict two important features of chromatin organization: chromatin interaction hubs and topologically associated domain (TAD) boundaries. |
| ICE | Hi-C | A computational pipeline that integrates a strategy to map sequencing reads with a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. |
| Juicebox | Hi-C | Software for visualizing data from Hi-C and other proximity mapping experiments. |
| Juicer | Hi-C | Juicer is a one-click system for analyzing loop-resolution Hi-C experiments. Specifically, the software is an end-to-end automated pipeline for converting raw reads into Hi-C maps and loop calls using only a single command. |
| LACHESIS | Hi-C | A software tool to measure the thread of life. LACHESIS exploits contact probability map data (e.g. from Hi-C) for chromosome-scale de novo genome assembly. |
| Mango | ChIA-PET | A complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity. |
| MDM | ChIA-PET | MDM provides functions for the analysis of chromatin interactions using MC_DIST model, Two-Step model and One-Step model, based on ChIA-PET data. |
| MICC | ChIA-PET | An easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate. |
| MOGEN | Hi-C | A 3D chromosome reconstruction method to make it capable of reconstructing 3D models of genomes from both intra- and inter-chromosomal Hi-C contact data. |
| NuChart | Hi-C | An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations. |
| PASTIS | Hi-C | Novel approach to infer a consensus three-dimensional structure of a genome from Hi-C data. The method incorporates a statistical model of the contact counts, assuming that the counts between two loci follow a Poisson distribution whose intensity decreases with the physical distances between the loci. |
| R3CPET | ChIA-PET | The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. |
| TADbit | Hi-C | An algorithm for the identification of hierarchical topological domains in Hi-C data. |
| TADtree | Hi-C | TADbit is a complete Python library to deal with all steps to analyze, model and explore 3C-based data. |
| WashU Epigenome Browser | 5CHi-CChIA-PET | The browser supports multiple types of long-range genome interaction data. This enables investigators to explore epigenomic data in the context of higher-order chromosomal domains and to generate multiple types of intuitive, publication-quality figures of interactions. |
| GMAP | Hi-C | GMAP (Gaussian Mixture model And Proportion test), is an algorithm to identify topologically associating domains and subdomains (Yu et al., Nature Communications, 2017). It provides a new and efficient tool to analyze simulated and experimental Hi-C data with increased statistical robustness and better consistency of the predictions compared to three state-of-the-art methods |