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.
Measures the Hi-C data reproducibility. HiCRep evaluates the reproducibility of Hi-C intra-chromosome Hi-C data. It can infer confidence intervals for stratum-adjusted correlation coefficient (SCC), and further it can estimate the statistical significance of the difference in reproducibility measurements for different data sets. The software is able to distinguish biological replicates from non-replicates, whereas Pearson and Spearman correlations failed to do so consistently.
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 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 collection of matrix-related functions for analysing Hi-C contact maps. A contact map is a matrix whose elements store the population-averaged co-location frequencies between pairs of loci. Ultimately, HiC-spector uses a reproducibility metric for quantifying the similarity between contact maps based on spectral decomposition. HiC-spector allows a better understanding of chromosomal contact maps.
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 users to evaluate Hi-C data quality inside a single sample and between multiple ones. QuASAR intends to help in making choices in Hi-C data production or performing data comparisons. The software also assesses for return on additional sequencing, calculating a maximum reliable resolution that can be used for a Hi-C sample and absolute quality limits. QuASAR is a part of the HiFive suite of tools.
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.
Assesses the similarity of a pair of contact maps obtained from chromosome capture experiments. GenomeDISCO is designed for evaluating concordance and reproducibility of chromatin contact maps obtained from chromosome conformation capture experiments. It distinguishes biological replicates from different cell types with the desired sensitivity to sequencing depth, node and edge dropout noise, changes in domain boundaries and subtle differences in distance dependence.
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.
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