Allows users to identify thousands of simultaneous chromatin contacts in a single cell. The Single cell Hi-C Pipeline consists in a set of scripts that process single cell Hi-C libraries. It is able to determine the contacts in an individual nucleus.
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.
Simplifies the Hi-C data pre-processing, contact matrix transformation, and topologically associating domain (TAD) calling into a few easy steps. HiCExplorer is a tool-suite that can be used with other pipelines and processing tools as we have built-in import/export functions covering commonly used Hi-C data formats. This method works with HiCBrowser, a browser and an underlying program to visualize Hi-C and other genomic tracks.
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
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.
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.
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.
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.
Integrates existing pipelines focusing on individual steps of Hi-C data processing into an all-in-one package with adjustable parameters to infer the consensus 3D structure of genome from raw Hi-C sequencing data. HBP could assign statistical confidence estimation for chromatin interactions, and clustering interaction loci according to enrichment tracks or topological structure automatically. HBP provides the tools for the sequence mapping of raw reads, extraction and normalization of chromosome interactions, heatmap visualization of interaction frequency, network analysis of specific interactions, statistical evaluation with adjustable parameters.
Assists users in analyzing next-generation sequencing (NGS) data. snakePipes provides DNA-mapping, ChIP-seq, ATAC-seq, RNA-seq, whole-genome bisulfite-seq (WGBS), HiC and single-cell RNA-seq workflows. It employs extensive quality-checks and produces reports that inform users about processing and analysis results. It provides workflows that allow processing and downstream analysis of data in an allele-specific manner.
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.
Analyzes Genome Architecture Mapping (GAM) datasets. GAMtools covers the automated mapping of raw next-generation sequencing (NGS) data generated by GAM, detection of genomic regions present in each nuclear slice, calculation of quality control metrics, generation of inferred proximity matrices, plotting of heatmaps and detection of genomic features for which chromatin interactions are enriched/depleted.
A tool for mapping and performing quality control on Hi-C data. HiCUP is designed to take the raw sequence output from a HiC experiment and produce a filtered set of mapped interaction pairs, suitable for subsequent analysis. It will also produce a set of metrics which can be used to assess the quality of the data and help improve the construction of future libraries.
Optimizes the processing and binning of metagenomic 3C datasets. metaTOR aligns paired-end reads on a preliminary assembly to create a network from detected contacts between DNA chunks. It can annotate the assembly to match with the bins. This tool then extracts bin genomes and subnetworks, constructs bin-local and global contact maps.
Provides a Hi-C pipeline written in C. Hi.C is an application that generates three binaries: (i) an in-silico digestion of genomes using defined restriction enzymes, (ii) reads mapped files and finds valid Hi-C contact pairs, and (iii) a content that simplifies the reads mapped files.
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