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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.
HIPPIE / High-throughput Identification Pipeline for Promoter Interacting Enhancer elements
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.
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.
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.
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