The extremely large number of possible pairwise interactions in Hi-C samples imposes limitations on the realistically achievable sequencing depth at individual interactions, leading to reduced sensitivity. The recently developed Capture Hi-C (CHi-C) technology uses sequence capture to enrich Hi-C material for multiple genomic regions of interest (hereafter referred to as “baits”), making it possible to profile the global interaction profiles of many thousands of regions globally (“many vs all”) and at a high resolution.
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.
Designs to process Hi-C data, from raw fastq files (paired-end Illumina data) to the normalized contact maps. The pipeline is flexible, scalable and optimized. It can operate either on a single laptop or on a computational cluster using the PBS-Torque scheduler
A set of tools for calling significant interactions in Capture Hi-C data, such as Promoter Capture Hi-C. CHiCAGO uses a convolution noise model accounting for both ‘Brownian’ (distance-dependent) and ‘technical’ noise. It borrows information across interactions (with appropriate normalisation) to estimate these noise components separately on different subsets of data. CHiCAGO then performs a p-value weighting procedure based on the expected true positive rates at different distance ranges (estimated from data), with scores representing soft-thresholded -log weighted p-values. CHiCAGO consists of an open-source R package (Chicago), a data package with subsets of published Promoter Capture Hi-C datasets for training purposes (PCHiCdata) and a set of command line tools for pre-processing and post-processing (chicagoTools).
A web-based tool that allows interactive exploration of promoter capture Hi-C (PCHi-C) interaction maps and integration with both public and user-defined genomic datasets. CHiCP employs a hybrid approach: users see a circular overview for all interactions for a given SNV, gene or region, with the option to highlight a particular interaction of interest that results in a standard linear view.
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