Performs peak finding and downstream data analysis for next-generation sequencing analysis. HOMER affords several tools and methods to make use of ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and other types of functional genomics sequencing data sets. This software offers support to UCSC visualization, peaks annotation, quantification of transcripts and repeats or differential features, enrichment and expression.
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.
Maps, filters and analyzes Hi-C data. hiclib 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. This library consists of three parts: mapping pipeline (mapping.py), fragment-level filtering pipeline (FragmentHiC.py), and a binned data analysis toolset (BinnedData.py).
An approach to infer a consensus 3D 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. The method can automatically adjust the transfer function relating the spatial distance to the Poisson intensity and infer a genome structure that best explains the observed data.
Simplifies the Hi-C data pre-processing, contact matrix transformation, and topologically associating domain (TAD) calling into a few easy steps. HiCExplorer is a tool-suite that can be used with other pipelines and processing tools as we have built-in import/export functions covering commonly used Hi-C data formats. This method works with HiCBrowser, a browser and an underlying program to visualize Hi-C and other genomic tracks.
Analyzes terabase-scale Hi-C datasets. Juicer allows users without a computational background to transform raw sequence data into normalized contact maps with one click. Juicer produces a hic file containing compressed contact matrices at many resolutions, facilitating visualization and analysis at multiple scales.