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.
A computational model integrating Hi-C and histone mark ChIP-seq data to predict two important features of chromatin organization: chromatin interaction hubs and topologically associated domain (TAD) boundaries. HubPredictor accurately and robustly predicts these features across datasets and cell types. Cell-type specific histone mark information is required for prediction of chromatin interaction hubs but not for TAD boundaries. HubPredictor provides a useful guide for the exploration of chromatin organization.
A high-throughput identification pipeline for promoter interacting enhancer element to streamline the workflow from mapping raw Hi-C reads, identifying DNA-DNA interacting fragments with high confidence and quality control, detecting histone modifications and DNase hypersensitive enrichments in putative enhancer elements, to ultimately extracting possible intra- and inter-chromosomal enhancer-target gene relationships.
A hidden Markov random field based Bayesian peak caller to identify long range chromatin interactions from Hi-C data. Comparing to the existing anchor-fragment peak caller, HMRFBayesHiC is the first two-dimensional peak caller, which takes observed and expected Hi-C contact matrix as the input files. HMRFBayesHiC explicitly models the spatial dependency of chromatin interaction among adjacent neighborhood regions, resulting in superior reproducibility and enhanced statistical power.
A hidden Markov random field (HMRF)-based peak caller to detect long-range chromosomal interactions from Hi-C data. In real Hi-C data analysis, FastHiC runs around ten times faster than our previous Bayesian version peak caller HMRFBayes. The substantial speed-up comes from a novel implementation of simulated field approximation, which approximates the joint distribution of the hidden peak status by a set of independent random variables, leading to more tractable computation.
Offers functions for the analysis of chromatin interactions using MC_DIST model (one sample problem), Two-Step model and One-Step model. MDM provides: (i) a function MCDIST to detect ture chromatin interactions using a dataset obtained from a chromatin looping experiment, (ii) a function MDTS to peoform the second step of the Two-Step model for detecting chromatin interactions with different intensities in two samples, and (iii) a function MDOS to peoform the One-Step model for detecting chromatin interactions with different intensities in two samples.
A web server for analyzing spatial contact of chromosomes from the publicly available Hi-C data. ChromContact is designed to be simple and easy to use. By specifying a locus of interest, ChromContact calculates contact profiles and generates links to the UCSC Genome Browser, enabling users to visually examine the contact information with various annotations. ChromContact provides wide-range of molecular biologists with a user-friendly means to access high-resolution Hi-C data. One of the possible applications of ChromContact is investigating novel long-range promoter-enhancer interactions. This facilitates the functional interpretation of statistically significant markers identified by GWAS or ChIP-seq peaks that are located far from any annotated genes.
Detects the spatial proximity between pairs of genomic loci. GOTHiC is a binomial model that corrects the complex combination of known and unknown biases in Hi-C data by assuming that all biases are captured in the total number of reads mapping to the interacting loci. Significance levels and observed/expected ratios obtained from GOTHiC can be used as the basis for algorithms predicting the 3D structure of genome or those finding topologically associated domains.
Provides users with a statistical pipeline for analysing chromosomal interactions data (Hi-C data). chromoR combines wavelet methods and a Bayesian approach for correction (bias and noise) and comparison (detecting significant changes between Hi-C maps) of Hi-C contact maps. In addition, it also support detection of change points in 1D Hi-C contact profiles. The chromoR package provides researchers with a means to analyse chromosomal interaction data using statistical bioinformatics, offering a new and comprehensive solution to this task.
Uses for interacting with the PGL file standard for paired-genomic loci. Pgltools is a cross platform, pypy compatible python package available both as an easy-to-use UNIX package, and as a python module, for integration into pipelines of paired-genomic-loci analyses. This software performs genomic arithmetic on PGL files such as comparing, merging, and intersecting two sets of paired-genomic-loci, as well as integrates BED files with PGL files.
A bioinformatics pipeline for the automated analysis of data generated by high-throughput chromatin conformation capture (HiC). The analysis workflow comprises steps of data formatting, genome alignment, quality control and filtering, identification of genome-wide chromatin interactions, visualization and statistics. An interactive browser enables visual inspection of interaction data and results.