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HOMER / Hypergeometric Optimization of Motif EnRichment

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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.

ICE / Iterative Correction and Eigenvector decomposition

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A computational pipeline that 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. Iterative correction leverages the unique pairwise and genome-wide structure of Hi-C data to decompose contact maps into a set of biases and a map of relative contact probabilities between any two genomic loci, achieving equal visibility across all genomic regions.


Examines epigenomic and transcriptomic next generation sequencing (NGS) data. Octopus-toolkit can be used for antibody- or enzyme-mediated experiments and studies for the quantification of gene expression. It can accelerate the data mining of public epigenomic and transcriptomic NGS data for basic biomedical research. This tool provides a private and a public mode: one to process the user’s own data, and the other to analyze public NGS data by retrieving raw files from the GEO database.


Explores Hi-C and other contact map data. Juicebox allows users to zoom in and out of Hi-C maps interactively. It integrates many technologies developed for the Integrative Genomics Viewer with a broad ensemble of methods specifically designed for handling 2D contact data. Individual maps can be normalized (corrected for experimental bias), compared to one-dimensional tracks (such as gene tracks or chromatin immunoprecipitation sequencing data), and compared to 2D feature lists (such as loop and domain annotations).

HiCapp / HiCorrectorapp

Corrects the copy number bias in tumor HiCorrector (Hi-C) data using chromosome-adjusted iterative correction bias (caICB) correction algorithm. HiCapp is able to efficiently correct for the copy number bias as well as other potential biases in tumor Hi-C data without a priori knowledge of these biases. Our caICB method is suitable for extremely high-resolution Hi-C maps, because it can achieve robust results when using a small subset of genomic ranges instead of using the whole genome contact map. The method does not require copy number data for the samples for which Hi-C data is available, and has the potential to adjust for other possible biases in Hi-C data without their priori knowledge.


Allows a comprehensive and reproducible analysis of Hi-C sequencing data. HiC-bench performs complete Hi-C analysis starting with the alignment of reads (fastq files) and ending with the annotation of specific interactions, their visualization and enrichment analysis. Hi-C pipeline integrates Anchoring Topological Domain (TAD) calling HiC-bench using published methods and your own algorithm and performs calculation of boundary scores using your own methods and existing ones. Every pipeline step is followed by summary statistics (when applicable) and visualization of the results. This allows quality control and facilitates troubleshooting. Furthermore, HiC-bench allows parameter exploration and comparison of different methods in a combinatorial fashion. This feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple parameter/tool choices in each step.


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.


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.


A set of tools for handling HiC and 5C data. HiFive provides efficient data handling and a variety of normalization approaches for easy, fast analysis and method comparison. Integration of MPI-based parallelization allows scalability and rapid processing time. In addition to single-command analysis of an entire experiment from mapped reads to interaction values, HiFive has been integrated into the open-source, web-based platform Galaxy to connect users with computational resources and a graphical interface.


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.

MARGE / Mutation Analysis for Regulatory Genomic Elements

Investigates ChIP-seq, ATAC-seq, DNase I Hypersensitivity or other next generation sequencing (NGS) assays. MARGE recognizes DNA binding motifs that potentially affect transcription factor (TF) binding using traditional de-novo motif analysis on genomic sequence for each polymorphic allele. It serves to find combinations of collaborating transcription factors. This tool contains visualization software to interpret the results.