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 probabilistic model for Hi-C data and explore chromosomal architectures in human lymphoblasts using it. Hicpipe is a set of scripts and programs that correct Hi-C contact maps, given a list restriction enzyme sites and mapped paired reads.
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 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.
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).
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.
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 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.
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.
Corrects local chromosomal abnormalities in Hi-C experiments. Dryhic is a set of tools that was developed ground up to address the need to normalize data from biological samples with aberrant karyotypes, but it applies seamlessly to the case of normal karyotypes. The other principle of this method is to explicitly model Hi-C biases on a single variable: the total amount of contacts for each bin of the matrix.
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).
Allows analysis and 3D modelling of 3C-based data. TADbit is a computational framework including: (i) read quality control and design of the mapping strategy; (ii) mapping of reads to the reference genome; (iii) interaction map filtering and normalization; (iv) interaction matrix analysis, including matrix comparison, Topologically Associating Domain (TAD) detection and TAD alignment; (v) 3D modelling of genomes and genomic domains; and (vi) 3D model analysis.
An easy-to-use, open source implementation of the Hi-C data normalization algorithm. Its salient features are (i) scalability - the software is capable of normalizing Hi-C data of any size in reasonable times; (ii) memory efficiency - the sequential version can run on any single computer with very limited memory, no matter how little; (iii) fast speed - the parallel version can run very fast on multiple computing nodes with limited local memory.
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.
Assists users in removing biases in single-cell Hi-C data. scHiCNorm is a software package that uses zero-inflated and hurdle models. This model eliminates systematic biases for single-cell Hi-C data, which better reveal variations between cells in chromosomal structures. This method also includes cutting sites, GC content, and mappability.
Explores the impact of copy number variants (CNVs) on Hi-C data. cancer-hic-norm provides a solution to deal with its effect on cancer data normalization. This tool proposes a model simulating large copy number rearrangements on a diploid Hi-C contact map. It extends the ICE algorithm and corrects the data from systematic biases. It also considers the CNVs as a bias to remove or as an interesting signal to conserve in the data structure.
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.
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.