1 - 19 of 19 results

TADtool / Topologically Associating Domains tool

Finds robust TAD-calling parameters with immediate visual feedback. TADtool allows the direct export of TADs called with a chosen set of parameters for two of the most common TAD calling algorithms: directionality and insulation index. This tool speeds up and improves the process of finding meaningful parameters for two of the most popular TADcalling algorithms. As such, TADtool is of great practical value for the genome organization community.


A multi-task spectral clustering method, to identify common and context-specific aspects of genome architecture. Arboretum-Hi-C is based on a previous multi-task clustering approach, Arboretum, which uses a generative probabilistic model to cluster expression data from multiple species while accounting for the hierarchical relationships among the species as described by a phylogenetic tree. However, instead of expression matrices at each leaf node, we now have Hi-C interaction graphs. Arboretum-Hi-C compares systematically the 3D organization across multiple cell types and species. It combines two clustering strategies: spectral clustering and multi-task clustering. Multi-task clustering is a special case of multi-task learning, where the goal is to solve multiple learning tasks simultaneously.


Allows a comprehensive and reproducible analysis of Hi-C sequencing data. HiC-bench performs complete Hi-C analysis starting with the alignment of reads (fastq files) and ending with the annotation of specific interactions, their visualization and enrichment analysis. Hi-C pipeline integrates Anchoring Topological Domain (TAD) calling HiC-bench using published methods and your own algorithm and performs calculation of boundary scores using your own methods and existing ones. Every pipeline step is followed by summary statistics (when applicable) and visualization of the results. This allows quality control and facilitates troubleshooting. Furthermore, HiC-bench allows parameter exploration and comparison of different methods in a combinatorial fashion. This feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple parameter/tool choices in each step.


An algorithm for the identification of hierarchical topological domains in Hi-C data. Beginning with contact matrix A, we compute the fold-enrichment over background for each pair of positions. For each interval [i,j], we estimate parameters δ(i,j), β(i,j). Next, for each genomic position i we compute the boundary index, a 1D statistic that looks for local shifts in interaction frequency at TAD boundaries. Finally, a dynamic program finds TAD trees that maximize the boundary index and best fit the contact matrix A, then selects an optimal set of TAD trees to form a TAD forest.


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. HubPredictor accurately and robustly predicts these features across datasets and cell types. Cell-type specific histone mark information is required for prediction of chromatin interaction hubs but not for TAD boundaries. HubPredictor provides a useful guide for the exploration of chromatin organization.


Allows users to compare genomic structures and annotations across multiple databases and platforms such as HaploReg and RegulomeDB. epiTAD permits researchers to browse and plot various data related to major genome organization structures without the need of programming knowledge. The platform is divided into five main panels including a single nucleotide polymorphisms (SNPs) query interface; a variant annotation panel; a gene annotation board and an outputs selector. The application aims to facilitate in silico discovery.

ClusterTAD / clustering-based TAD detection method

Detects Topologically Associated Domains (TADs) from Hi-C data. ClusterTAD is a clustering based method that employs standard clustering algorithms to extract topological domains from Hi-C contact data. It can be iteratively applied to divide larger clusters into small ones, which can be used to identify both large TADs and smaller sub-TADs. This application only requires one parameter – the number of cluster, and the parameter can be estimated automatically from the data.


Aims to identify topologically associating domains (TADs) from intra-chromosomal contact maps. MrTADFinder is based on the concept of modularity. A key component is to derive a background model for any given contact map, by numerically solving a set of matrix equations. MrTADFinder provides a self-consistent approach to identify TADs at different length scales, or resolutions. At a low resolution, larger TADs are found whereas, at a high resolution, smaller TADs are identified. Overall, MrTADFinder provides a computational framework to explore the multi-scale structures stored in Hi-C contact maps.