Differential chromatin interaction identification software tools | Hi-C data analysis
Chromatin conformation capture with high-throughput sequencing (Hi-C) is a technique that measures the in vivo intensity of interactions between all pairs of loci in the genome. Most conventional analyses of Hi-C data focus on the detection of statistically significant interactions. However, an alternative strategy involves identifying significant changes in the interaction intensity (i.e., differential interactions) between two or more biological conditions. This is more statistically rigorous and may provide more biologically relevant results.
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.
A software package for rigorous detection of differential interactions from Hi-C data. diffHic provides methods for read pair alignment and processing, counting into bin pairs, filtering out low-abundance events and normalization of trended or CNV-driven biases. It uses the statistical framework of the edgeR package to model biological variability and to test for significant differences between conditions. Several options for the visualization of results are also included. On real data, diffHic is able to successfully detect interactions with significant differences in intensity between biological conditions. It also compares favourably to existing software tools on simulated data sets. diffHic is able to accommodate complex experimental designs, including paired or blocked designs and those with more than two groups. It does this by accessing the generalized linear model functionality of edgeR.
Provides a simple graphical user interface for data pre-processing and a collection of higher-level data analysis tools implemented in R. Data pre-processing also supports a wide range of additional data types required for in-depth analysis of the Hi-C data (e.g. RNA-Seq, ChIP-Seq, and BS-Seq). Importantly, HiCdat is focused on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools.
Determines differential chromatin interactions among two Hi-C experiments. FIND is an R package offering a computational approach which considers the dependency between adjacent loci to investigate chromatin structures. The application highlights interactions that presents a significant change in their frequency as well as the interaction frequency of their neighboring bins by exploiting a spatial Poisson process model.
Allows joint normalization and difference detection in multiple chromosome conformation capture sequencing technologies (Hi-C) datasets. HiCcompare is an extension of the popular RNA-seq differential expression method edgeR, operating on individual raw sequencing data. It contains a Sequential Component Normalization (SCN) method that attempts to smooth out biases due to GC content and circularization. The tool was tested on chromosome-specific chromatin interaction matrices.
Allows detection of collaborative transcription factor pairs. MMARGE consists of a suite of software tools to analyze ChIP-seq, ATAC-seq, DNase I Hypersensitivity or other next generation sequencing (NGS) assays where genotyping or DNA sequence data is available. For performing, this tool needs two types of data: (1) genetic variation, and (2) high-throughput sequencing data (ChIP-seq, ATAC-seq, DNaseI-seq).
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.