Showing 32 of total 32 results (show query)
bioc
HiCcompare:HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets
HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingnormalizationdifference-detectionhi-cvisualization
114.6 match 19 stars 8.61 score 51 scripts 5 dependentsbioc
multiHiCcompare:Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available
multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingnormalization
75.4 match 9 stars 7.30 score 37 scripts 2 dependentsbioc
plotgardener:Coordinate-Based Genomic Visualization Package for R
Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page.
Maintained by Nicole Kramer. Last updated 5 months ago.
visualizationgenomeannotationfunctionalgenomicsgenomeassemblyhiccpp
50.1 match 308 stars 10.16 score 167 scripts 3 dependents
pneuvial
adjclust:Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix
Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.
Maintained by Pierre Neuvial. Last updated 5 months ago.
clusteringfeatureextractiongwashi-chierarchical-clusteringlinkage-disequilibriumcppopenmp
47.7 match 16 stars 7.35 score 13 scripts 2 dependentsbioc
diffHic:Differential Analysis of Hi-C Data
Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.
Maintained by Aaron Lun. Last updated 3 months ago.
multiplecomparisonpreprocessingsequencingcoveragealignmentnormalizationclusteringhiccurlbzip2xz-utilszlibcpp
48.7 match 5.58 score 38 scriptsbioc
FitHiC:Confidence estimation for intra-chromosomal contact maps
Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C.
Maintained by Ruyu Tan. Last updated 5 months ago.
43.1 match 4.78 score 2 scriptsbioc
HiCBricks:Framework for Storing and Accessing Hi-C Data Through HDF Files
HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization.
Maintained by Koustav Pal. Last updated 5 months ago.
dataimportinfrastructuresoftwaretechnologysequencinghic
37.5 match 4.48 score 9 scripts 1 dependentsbioc
InteractionSet:Base Classes for Storing Genomic Interaction Data
Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments.
Maintained by Aaron Lun. Last updated 5 months ago.
infrastructuredatarepresentationsoftwarehiccpp
21.1 match 7.97 score 250 scripts 36 dependentsbioc
HiCDCPlus:Hi-C Direct Caller Plus
Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation.
Maintained by Merve Sahin. Last updated 5 months ago.
hicdna3dstructuresoftwarenormalizationzlibcpp
36.1 match 4.20 score 16 scriptsbioc
HiTC:High Throughput Chromosome Conformation Capture analysis
The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided.
Maintained by Nicolas Servant. Last updated 5 months ago.
sequencinghighthroughputsequencinghic
26.4 match 5.40 score 42 scriptsbioc
HiCDOC:A/B compartment detection and differential analysis
HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files.
Maintained by Maigné Élise. Last updated 3 months ago.
hicdna3dstructurenormalizationsequencingsoftwareclusteringcpp
23.5 match 4 stars 5.86 score 6 scripts 1 dependentsbioc
bnbc:Bandwise normalization and batch correction of Hi-C data
Tools to normalize (several) Hi-C data from replicates.
Maintained by Kipper Fletez-Brant. Last updated 5 months ago.
hicpreprocessingnormalizationsoftwarecpp
30.3 match 1 stars 3.88 score 15 scriptsbioc
HiCExperiment:Bioconductor class for interacting with Hi-C files in R
R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package.
Maintained by Jacques Serizay. Last updated 5 months ago.
12.4 match 9 stars 6.89 score 48 scripts 2 dependentsbioc
sevenC:Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs
Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes.
Maintained by Jonas Ibn-Salem. Last updated 5 months ago.
dna3dstructurechipchipcoveragedataimportepigeneticsfunctionalgenomicsclassificationregressionchipseqhicannotation3d-genomechip-seqchromatin-interactionhi-cpredictionsequence-motiftranscription-factors
15.0 match 12 stars 5.38 score 3 scriptsbioc
GenomicInteractions:Utilities for handling genomic interaction data
Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics.
Maintained by Liz Ing-Simmons. Last updated 5 months ago.
softwareinfrastructuredataimportdatarepresentationhic
7.7 match 7 stars 9.39 score 162 scripts 6 dependentsbioc
HicAggR:Set of 3D genomic interaction analysis tools
This package provides a set of functions useful in the analysis of 3D genomic interactions. It includes the import of standard HiC data formats into R and HiC normalisation procedures. The main objective of this package is to improve the visualization and quantification of the analysis of HiC contacts through aggregation. The package allows to import 1D genomics data, such as peaks from ATACSeq, ChIPSeq, to create potential couples between features of interest under user-defined parameters such as distance between pairs of features of interest. It allows then the extraction of contact values from the HiC data for these couples and to perform Aggregated Peak Analysis (APA) for visualization, but also to compare normalized contact values between conditions. Overall the package allows to integrate 1D genomics data with 3D genomics data, providing an easy access to HiC contact values.
Maintained by Olivier Cuvier. Last updated 5 months ago.
softwarehicdataimportdatarepresentationnormalizationvisualizationdna3dstructureatacseqchipseqdnaseseqrnaseq
14.7 match 4.90 score 3 scriptsbioc
mariner:Mariner: Explore the Hi-Cs
Tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization.
Maintained by Eric Davis. Last updated 5 months ago.
functionalgenomicsvisualizationhic
12.6 match 4.89 score 77 scriptsbioc
idr2d:Irreproducible Discovery Rate for Genomic Interactions Data
A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks.
Maintained by Konstantin Krismer. Last updated 5 months ago.
dna3dstructuregeneregulationpeakdetectionepigeneticsfunctionalgenomicsclassificationhic
14.2 match 4.30 score 6 scripts
corneliusfritz
bigergm:Fit, Simulate, and Diagnose Hierarchical Exponential-Family Models for Big Networks
A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.
Maintained by Cornelius Fritz. Last updated 20 days ago.
20.0 match 2.60 score 4 scriptsbioc
plyinteractions:Extending tidy verbs to genomic interactions
Operate on `GInteractions` objects as tabular data using `dplyr`-like verbs. The functions and methods in `plyinteractions` provide a grammatical approach to manipulate `GInteractions`, to facilitate their integration in genomic analysis workflows.
Maintained by Jacques Serizay. Last updated 5 months ago.
10.9 match 4.75 score 14 scriptsbioc
Chicago:CHiCAGO: Capture Hi-C Analysis of Genomic Organization
A pipeline for analysing Capture Hi-C data.
Maintained by Mikhail Spivakov. Last updated 5 months ago.
epigeneticshicsequencingsoftware
10.3 match 4.81 score 32 scripts
henrikbengtsson
TopDom:An Efficient and Deterministic Method for Identifying Topological Domains in Genomes
The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 <doi:10.1093/nar/gkv1505>). The authors published an implementation of their method as an R script (two different versions; also available in this package). This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation. It also provides a small number of bug fixes to the original code.
Maintained by Henrik Bengtsson. Last updated 4 years ago.
genomicshictopological-domains
7.9 match 21 stars 5.80 score 20 scripts 1 dependents
syedhaider5
chicane:Capture Hi-C Analysis Engine
Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.
Maintained by Syed Haider. Last updated 3 years ago.
12.7 match 2.75 score 28 scriptsbioc
GOTHiC:Binomial test for Hi-C data analysis
This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome.
Maintained by Borbala Mifsud. Last updated 5 months ago.
immunooncologysequencingpreprocessingepigeneticshic
7.4 match 4.30 score 6 scriptsbioc
HiCool:HiCool
HiCool provides an R interface to process and normalize Hi-C paired-end fastq reads into .(m)cool files. .(m)cool is a compact, indexed HDF5 file format specifically tailored for efficiently storing HiC-based data. On top of processing fastq reads, HiCool provides a convenient reporting function to generate shareable reports summarizing Hi-C experiments and including quality controls.
Maintained by Jacques Serizay. Last updated 5 months ago.
7.3 match 2 stars 4.34 score 11 scriptsbioc
GRaNIE:GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data
Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.
Maintained by Christian Arnold. Last updated 5 months ago.
softwaregeneexpressiongeneregulationnetworkinferencegenesetenrichmentbiomedicalinformaticsgeneticstranscriptomicsatacseqrnaseqgraphandnetworkregressiontranscriptionchipseq
5.7 match 5.40 score 24 scripts
cran
rankdist:Distance Based Ranking Models
Implements distance based probability models for ranking data. The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance, Weighted-tau distance and Weighted Kendall distance. Phi-component model and mixture models are also supported.
Maintained by Zhaozhi Qian. Last updated 6 years ago.
20.0 match 1.48 score 1 dependentsbioc
TADCompare:TADCompare: Identification and characterization of differential TADs
TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingfeatureextractionclustering
1.0 match 23 stars 7.04 score 10 scriptsbioc
SpectralTAD:SpectralTAD: Hierarchical TAD detection using spectral clustering
SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingfeatureextractionclustering
1.0 match 8 stars 6.53 score 17 scriptsbioc
HiContacts:Analysing cool files in R with HiContacts
HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment.
Maintained by Jacques Serizay. Last updated 5 months ago.
1.1 match 12 stars 5.95 score 49 scriptsbioc
preciseTAD:preciseTAD: A machine learning framework for precise TAD boundary prediction
preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingclusteringclassificationfunctionalgenomicsfeatureextraction
1.0 match 7 stars 5.29 score 14 scriptscran
treediff:Testing Differences Between Families of Trees
Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests.
Maintained by Nathalie Vialaneix. Last updated 1 years ago.
3.8 match 1.00 score