There are 1 repository under hic topic.
HiCExplorer is a powerful and easy to use set of tools to process, normalize and visualize Hi-C data.
Extract Sequence from Genome According to Annotation File
Software for comparing contact maps from HiC, CaptureC and other 3D genome data.
This Nextflow DSL2 pipeline takes aligned HiC reads, creates contact maps and a table of statistics.
R package: TopDom - An efficient and Deterministic Method for identifying Topological Domains in Genomes
Joint normalization of two Hi-C matrices, visualization and detection of differential chromatin interactions. See multiHiCcompare for the analysis of multiple Hi-C matrices
High-performance stochastic modeling of DNA loop extrusion interactions
Blazing fast toolkit to work with .hic and .cool files
3DGB is a workflow to build 3D models of genomes from HiC data
I'm sharing my automation of the 3D_DNA and SALSA Hi-C scaffolding pipelines.
Python bindings for hictk: read and write .cool and .hic files directly from Python
FIREcaller: Python library for detecting Frequently Interacting REgions (FIREs) from Hi-C data
A few tools for converting contact maps to .hic format compatible with the 4D Genome Browser.
Snakemake wrapper around the Arima Capture Hi-C (CHiC) workflow
Snakemake pipeline for analysis and normalization of Hi-C data starting from fastq.gz files. It includes the possibility to perform grouped analyses, TAD, loops and stripes detections, as well as differential compartment and chromatin interaction analyses.
Github Repo for Hi-C and MC-3C analysis scripts for the Topo II Inhibition Manuscript
Identification of enriched motif pairs from chromatin interaction data
[Research Intern work] HiC Data Analysis Suite
R bindings for hictk: read .cool and .hic files directly from R
A Nextflow DSL2 pipeline for pretext generation in curation
Using chromatin-chromatin interaction scores generated by the Capture Hi-C technology, I developed this R code during my six week internship in the Spicuglia lab, part of the INSERM institute (TAGC) related to the Aix-Marseille University, France, in order to identify the DNA interactions happening between different regions, specifically the interaction of the OAS3 Epromoter with its target promoters OAS1 and OAS2, before and after IFN alpha stress stimulation and then to figure out the changes of the 3D chromatin organization upon this stress stimulation.
TAD Calling using spectral clustering in R
Identification of enriched motif pairs from chromatin interaction data