Aims to identify topological domains in the genome. DI is a statistic method to quantify the degree of upstream or downstream interaction bias for a genomic region. It intends to provide a reproducible approach that uses a Hidden Markov model (HMM) to identify biased “states” and therefore infer the locations of topological domains in the genome.
Permits users to forecast nested structures of chromatin domains from raw Hi-C interaction matrices. Matryoshka was developed to predict domains at different resolutions, calculated using intrinsic properties of the chromatin data, and clusters these to construct the hierarchy. It optimizes a function to obtain a set of domains that do not overlap at a collection of different resolutions such as size scales.
Allows analysis and 3D modelling of 3C-based data. TADbit is a computational framework including: (i) read quality control and design of the mapping strategy; (ii) mapping of reads to the reference genome; (iii) interaction map filtering and normalization; (iv) interaction matrix analysis, including matrix comparison, Topologically Associating Domain (TAD) detection and TAD alignment; (v) 3D modelling of genomes and genomic domains; and (vi) 3D model analysis.
Capturing persistent domains across various resolutions by adjusting a single scale parameter. Armatus produces domains that display much higher interaction frequencies within the domains than in-between domains and for which the boundaries between these domains exhibit substantial enrichment for several insulator and barrier-like elements. It uses a multiscale approach that finds domains at various size scales and generates multiple optimal and near-optimal solutions.
Identifies topological domains (TDs), along with a set of statistical methods for evaluating their quality. TopDom is more efficient than existing methods and depends on just one intuitive parameter: a window size. It reveals that the locations of housekeeping genes are closely associated with cross-tissue conserved TDs. The tool can help emerging efforts on investigating the higher order genome organization.
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.