Chromosome conformation capture sequencing data analysis software tools
The 3C method uses formaldehyde cross-linking to covalently link interacting chromatin segments in intact cells. Cross-linked chromatin is then solubilized and digested with an appropriate restriction enzyme. This is followed by intramolecular ligation of cross-linked fragments. The resulting ‘3C template’ thus contains a large collection of ligation products. Each ligation product reflects an interaction between two genomic loci and can be detected by quantitative PCR using specific primers. The abundance of each ligation product is a measurement of the frequency with which the two loci interact. Thus, the 3C method can be used to capture and quantify physical interactions between genes and distant elements, both in cis and in trans.
A Bayesian framework to derive the 3D architecture of a chromosome from 3C-based data. InfMod3DGen can compute an accurate ensemble of 3D chromatin conformations that best interpret the distance constraints derived from 3C-based data and also agree with other sources of geometric constraints derived from experimental evidence in the previous studies.
An algorithmic framework, which is based on hierarchical block matrices (HBMs), for topological analysis and integration of chromosome conformation capture (3C) data. We first describe chromoHBM, an algorithm that compresses high-throughput 3C (HiT-3C) data into topological features that are efficiently summarized with an HBM representation. We suggest that instead of directly combining HiT-3C datasets across resolutions, which is a difficult task, we can integrate their HBM representations, and describe chromoHBM-3C, an algorithm which merges HBMs. Since three-dimensional (3D) reconstruction can also benefit from topological information, we further present chromoHBM-3D, an algorithm which exploits the HBM representation in order to gradually introduce topological constraints to the reconstruction process.
Reconstructs bacterial 3D chromosome structures from DNA interaction data measured by 3C/Hi-C experiments. EVR suits for closed-loop structural features of prokaryotic chromosomes. This software exploits contact frequency matrix derived from 3C/Hi-C experimental data. It can be also used to determine the backbone structure of prokaryotic chromosomes at domain levels.
Assists in making genome-wide predictions of CCCTC-binding factor (CTCF)-mediated loops. Lollipop is a machine-learning framework based on random forests classifier. It accounts for the complexity of loop structures by integrating genomic and epigenomic features. Moreover, this approach reveals novel determinants of CTCF-mediated chromatin wiring, such as gene expression within the loop.
Optimizes the processing and binning of metagenomic 3C datasets. metaTOR aligns paired-end reads on a preliminary assembly to create a network from detected contacts between DNA chunks. It can annotate the assembly to match with the bins. This tool then extracts bin genomes and subnetworks, constructs bin-local and global contact maps.
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