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A collection of tools for Hi-C data analysis

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Hi-C data analysis tools and papers

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Tools are sorted by publication date, newest on top. Unpublished tools are listed at the end of each section. See Hi-C data notes and single-cell Hi-C notes for more. Please, contribute and get in touch! See MDmisc notes for other data science and genomics-related notes.

Table of content


  • Juicer - Java full pipeline to convert raw reads into Hi-C maps, visualized in Juicebox. Call domains, loops, CTCF binding sites.

    file format for storing multi-resolution Hi-C data.
    • Durand, Neva C., Muhammad S. Shamim, Ido Machol, Suhas S. P. Rao, Miriam H. Huntley, Eric S. Lander, and Erez Lieberman Aiden. “Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments.” Cell Systems 3, no. 1 (July 2016)
    • Rao, Suhas S. P., Miriam H. Huntley, Neva C. Durand, Elena K. Stamenova, Ivan D. Bochkov, James T. Robinson, Adrian L. Sanborn, et al. “A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping.” Cell 159, no. 7 (December 18, 2014) - Juicer analysis example. TADs defined by frequent interactions. Enriched in CTCF and cohesin members. Five domain types. A1 and A2 enriched in genes. Chr 19 contains 6th pattern B6. Enrichment in different histone modification marks. TADs are preserved across cell types. Yet, differences between Gm12878 and IMR90 were detected. Boundaries detection by scanning image. Refs to the original paper.
  • HiC-Pro - Python and command line-based optimized and flexible pipeline for Hi-C data processing,

    • Servant, Nicolas, Nelle Varoquaux, Bryan R. Lajoie, Eric Viara, Chong-Jian Chen, Jean-Philippe Vert, Edith Heard, Job Dekker, and Emmanuel Barillot. “HiC-Pro: An Optimized and Flexible Pipeline for Hi-C Data Processing.” Genome Biology 16 (December 1, 2015) - HiC pipeline, references to other pipelines, comparison. From raw reads to normalized matrices. Normalization methods, fast and memory-efficient implementation of iterative correction normalization (ICE). Data format. Using genotyping information to phase contact maps.
  • HiCExplorer - set of programs to process, normalize, analyze and visualize Hi-C data, Python, .cool format, conversion utilities.,

  • Galaxy HiCExplorer - a web server for Hi-C data preprocessing, QC, visualization. Web interface,, Docker container,

  • v3 of the Galaxy HiCExplorer. Includes full analysis of Hi-C, Capture-C, scHi-C. Workflow-like description of tools/tasks for each data type. scHiCExplorer set of tools specifically designed for scHi-C data. scHiCExplorer's documentation

  • HiCUP - Perl-based pipeline, alignment only, output - BAM files.

    • Wingett, Steven, Philip Ewels, Mayra Furlan-Magaril, Takashi Nagano, Stefan Schoenfelder, Peter Fraser, and Simon Andrews. “HiCUP: Pipeline for Mapping and Processing Hi-C Data.” F1000Research 4 (2015) - HiCUP pipeline, alignment only, removes artifacts (religations, duplicate reads) creating BAM files. Details about Hi-C sequencing artifacts. Used in conjunction with other pipelines.
  • FAN-C - Python pipeline for Hi-C processing. Input - raw FASTQ (aligned using BWA or Bowtie2, artifact filtering) or pre-aligned BAMs. KR or ICE normalization. Analysis and Visualization (contact distance decay, A/B compartment detection, TAD/loop detection, Average TAD/loop profiles, saddle plots, triangular heatmaps, comparison of two heatmaps).  Automatic or modular. Compatible with .cool and .hic formats. Tweet1, Tweet2. Table 1 - detailed comparison of 13 Hi-C processing tools

  • GITAR - full Hi-C pre-processing, normalization, TAD detection, and visualization. Python scripts wrapping other tools. Table 1 summarizes the functionality of existing tools.

    • Calandrelli, Riccardo, Qiuyang Wu, Jihong Guan, and Sheng Zhong. “GITAR: An Open Source Tool for Analysis and Visualization of Hi-C Data.” Genomics, Proteomics & Bioinformatics 16, no. 5 (2018): 365–72.
  • HiCdat - Hi-C processing pipeline and downstream analysis/visualization. Analyses: normalization, correlation, visualization, comparison, distance decay, PCA, interaction enrichment test, epigenomic enrichment/depletion.

  • HiC-bench - complete pipeline for Hi-C data analysis.

  • TADbit - TADbit is a complete Python library to deal with all steps to analyze, model and explore 3C-based data. With TADbit, the user can map FASTsQ files to obtain raw interaction binned matrices (Hi-C like matrices), normalize and correct interaction matrices, identify and compare the Topologically Associating Domains (TADs), build 3D models from the interaction matrices, and finally, extract structural properties from the models. TADbit is complemented by TADkit for visualizing 3D models.

  • HiC_Pipeline - Python-based pipeline performing mapping, filtering, binning, and ICE-correcting Hi-C data, from raw reads (.sra, .fastq) to contact matrices. Additionally, converting to sparse format, performing QC.

  • HiCpipe - an efficient Hi-C data processing pipeline. It is based on Juicer and HiC-pro, which combines the advantages of these two processing pipelines. HiCpipe is much faster than Juicer and HiC-pro and can output multiple features of Hi-C maps.

  • ENCODE project Data Production and Processing Standard of the Hi-C Mapping Center, PDF

  • cword - perl cworld module and collection of utility/analysis scripts for C data (3C, 4C, 5C, Hi-C).

  • my5C - web-based tools, well-documented analysis and visualization of 5S data,

  • nf-core-hic - Analysis of Chromosome Conformation Capture data (Hi-C and more), Nextflow pipeline. Also, nf-core/hic

  • distiller-nf - Java modular Hi-C mapping pipeline for reproducible data analysis, nextflow pipeline. Alignment, filtering, aggregating Hi-C matrices.

  • 4D Nucleome Hi-C Processing Pipeline, set of scripts wrapped in a Docker image. Works with

    files. Overview,
  • Abdennur, Nezar, and Leonid Mirny. “Cooler: Scalable Storage for Hi-C Data and Other Genomically-Labeled Arrays.” Bioinformatics, January 1, 2020.

    • cooler file format for storing Hi-C matrices, sparse, hierarchical, multi-resolution.
      Python package for data loading, aggregation, merging, normalization (balancing), viewing, exporting data. Together with "pairs" text-based format, and hic, cooler is accepted by the 4D Nucleome Consortium DAC.,
    • cooltools - tools to work with .cool files, Documentation,
    • hiclib - Python tools to QC, map, normalize, filter and analyze Hi-C data,
    • hic2cool - Lightweight converter between hic and cool contact matrices.
    • pairtools - tools for low-level processing of mapped Hi-C paired reads. Documentation


  • CHiCANE - an R-based data processing and interaction calling toolkit for the analysis and interpretation of Capture Hi-C data (Arima, Dovetail). Data preprocessing with HiCUP (recommended), but BAMs from other pipelines are supported. Flexible regression modeling of the number of reads linking bait and target fragments, include distance. (Truncated) Negative binomial, Poisson distributions, zeros may be included, other covariates. Functionality to assess model fit. Similar tools - GOTHiC. CHiCAGO, ChiCMaxima. Protocol explaining each step.

  • CaptureCompendium - all-in-one toolkit for the design, analysis and presentation of 3C experiments, combines oligonucleotide design Capsequm2, sequence mapping and extraction CCseqBasic, statistical data presentation and distribution CaptureCompare with Peaky integration, CaptureSee. Allows for multi-way interactions (Tri-C). Overview of previous tools doing parts.

  • GOPHER - probe design for Capture Hi-C. All, or selected, promoters, or around GWAS hits.

  • capC-MAP - Capture-C analysis pipeline. Python and C++, run through a configuration file. Outputs bedGraph. Compared with HiC-Pro, better detects PCR duplicates, identifies more interactions. Normalization tuned for Capture-C data.,


  • CID - Chromatin Interaction Discovery, call chromatin interactions from ChIA-PET. Outperforms ChIA-PET2, MANGO pipelines, call more peaks than HICCUPS, hichipper. Java implementation,

  • HiChIP-Peak - HiChIP peak caller, focus on peaks at re-ligation sites. Peak filtering, then negative binomial model. Differential peak analysis similar to DiffBind.

  • MAPS - Model-based Analysis of PLAC-seq and HiChIP. A zero-truncated Poisson regression framework to explicitly remove systematic biases, normalize, call long-distance interactions. Compared with hichipper, identifies more long-range, biologically relevant interactions. Works with Arima's HiChIP data


Resolution improvement

  • VEHiCLE - a Variationally Encoded Hi-C Loss Enhancement algorithm for resolution enhancement of Hi-C contact data. Includes a generative model for generating synthetic Hi-C data from a latent space representation (15 PCA components). Trained as an adversarial network conditioned on low resolution input. A composite loss function consisting of adversarial, variational, MSE, and insulation loss. Uses GM12878, IMR90, K562, HMEC data. Compared using five metrics against HiCPlus, DeepHic, HiCSR, outperforms all. Improves TAD identification, 3D structure modeling. Python implementation, early version.

  • HiCRes - resolution estimation, based on the linear dependence of 20th percentile of coverage and the window size used to access coverage. Includes preseq for estimating and predicting library complexity, bowtie2 and HiCUP for estimating Hi-C-specific QC metrics. Relatively insensitive to enzyme of choice. Implemented as Docker/Singularity images. Requires significant computational resources, like 5 hours on 40 CPU cluster.

  • HiCSR - enhancement of Hi-C contact maps using a Generative Adversarial Network trained to optimize a custom loss function (weighted adversarial loss, pixel-wise L1 loss, and a feature reconstruction loss). An increase in resolution refers to recovering additional Hi-C contacts, "saturating" downsampled and noisy Hi-C matrices, not increasing the number of pixels. Representation learning with autoencoder with several convolutional layers and skip connections, then using it for the generator to create new matrices with discriminator telling them fake or real. Compared with HiCPlus, HiCNN, hicGAN, DeepHiC. Reproducibility is better using four metrics. Python3 PyTorch implementation

  • DeepHiC - a generative adversarial network (GAN) for enhancing Hi-C data. Does not change the bin size, enhances the content of Hi-C data. Reconstructs the content from ~1% of the original data. Outperforms BoostHiC, HiCPlus, HiCNN. Online tool:, code:

  • hicGAN - improving resolution (saturation) of Hi-C data using Generative Adversarial Networks. Generator - five inner residual blocks to fight vanishing gradient (each block has two convolutional layers and batch normalization) and an outer skip connection. The discriminator has three convolutional blocks. Evaluation metrics: MSE, signal-to-noise ratio, structure similarity index, chromatin loop score. Compared against HiCPlus. Python, Tensorflow implementation

  • HiCNN - a computational method for resolution enhancement. A modification of the HiCPlus approach, using very deep (54 layers, five types of layers) convolutional neural network. A Hi-C matrix of regular resolution is transformed into the high-resolution but very sparse matrix, HiCNN predicts the missing values. Pearson and MSE evaluation metrics, overlap of Fit-Hi-C-detected significant interactions - perform similar or slightly better than HiCPlus. PyTorch implementation.

  • Boost-HiC - infer fine-resolution contact frequencies in Hi-C data, performs well even on 0.1% of the raw data. TAD boundaries remain. Better than HiCPlus. It can be used for differential analysis (comparison) of two Hi-C maps.

  • mHi-C - recovering alignment of multi-mapped reads in Hi-C data. Generative model to estimate probabilities for each bin-pair originating from a given origin. Reproducibility of contact matrices (stratum-adjusted correlation), reproducibility and number of significant interactions are improved. Novel interactions. Enrichment of TAD boundaries in LINE and SINE repetitive elements. Multi-mapping is not sensitive to trimming. Read filtering strategy (Figure 1, supplementary figures are very visual).

  • HIFI - Hi-C Interaction Frequency Inference for restriction fragment-resolution analysis of Hi-C data. Sparsity is resolved by using dependencies between neighboring restriction fragments, with Markov Random Fields performing the best. Better resolves TADs and sub-TADs, significant interactions. CTCF, RAD21, SMC3, ZNF143 are enriched around TAD boundaries. Matrices normalized for fragment-specific biases.

  • HiCPlus - increasing resolution of Hi-C data using convolutional neural network, mean squared error as a loss function. Basically, smoothing parts of Hi-C image, then binning into smaller parts. Performs better than bilinear/biqubic smoothing.


  • FreeHi-C v.2.0 - simulation of realistic Hi-C matrices with user- or data-driven spike-ins. Spike-ins are introduced on read-level and converted to interaction frequency level. Benchmark of

    , and
    . Assessment of FDR, power, significance order, PRC and AUROC, genomic properties. GM12878 and A549 replicates of experimental Hi-C data. Three simulation settings with varying background distribution of interaction frequencies, spike-in proportions, sequencing depth. Figure 5 - summary of performances for all methods and comparison types. Subjective top performers:
  • FreeHi-C - Hi-C data simulation based on properties of experimental Hi-C data. Preserves A/B compartments, TADs, the correlation between replicated (HiCRep), significant interactions, improves power to detect differential interactions. Robust to sequencing depth changes. Tested on replicates of GM12878, A549 human cancer cells, malaria P.falciparum. Compared with poorly performing Sim3C. All simulated data are at Python3 implementation


  • HiCorr - a method for correcting known (mappability, CG content) and unknown (visibility) biases in Hi-C maps (multiplicative effects, Methods). Easy Hi-C protocol allowing for low-input (~100K cells) Hi-C (in vivo HindIII digestion, in situ proximity ligation, DpnII digestion after lysis and reverse crosslink, Methods). HiCorr outputs ratio matrixes representing enrichment of Hi-C signal, hence loops can be easily extracted. Recovers 65% of HICCUPS loops and more. Chromatin loops are better marks of cell identity than compartments and outperform eQTLs in defining neurological GWAS target genes. Human iPSCs, neural progenitors (NPCs), neurons, fetal cerebellum, adult temporal cortex, data from other studies.

  • multiHiCcompare - joint normalization of multiple Hi-C datasets using cyclic loess regression through pairs of MD plots (minus-distance). Data-driven normalization accounting for the between-dataset biases. Per-distance edgeR-based testing of significant interactions.

  • Binless - a resolution-agnostic normalization method that adapts to the quality and quantity of available data, to detect significant interactions and differences. Negative binomial count regression framework, adapted for ICE normalization. Fused lasso to smooth neighboring signals. TADbit for data processing, details of read filtering.

  • HiCcompare - joint normalization of two Hi-C datasets using loess regression through an MD plot (minus-distance). Data-driven normalization accounting for the between-dataset biases. Per-distance permutation testing of significant interactions.

  • HiFive - handling and normalization or pre-aligned Hi-C and 5C data,

    • Sauria, Michael EG, Jennifer E. Phillips-Cremins, Victor G. Corces, and James Taylor. “HiFive: A Tool Suite for Easy and Efficient HiC and 5C Data Analysis.” Genome Biology 16, no. 1 (December 2015). - HiFive - post-processing of aligned Hi-C and 5C data, three normalization approaches: "Binning" - model-based Yaffe & Tanay's method, "Express" - matrix-balancing approach, "Probability" - multiplicative probability model. Judging normalization quality by the correlation between matrices.
  • HiCNorm - removing known biases in Hi-C data (GC content, mappability, fragment length) via Poisson regression,

CNV-aware normalization

  • Hi-C data normalization considering CNVs. Extension of matrix-balancing algorithm to either retain the copy-number variation effect (LOIC) or remove them (CAIC). ICE itself can lead to misrepresentation of the contact probabilities between CNV regions. Estimating CNV directly from Hi-C data correcting for GC content, mappability, fragment length using Poisson regression. LOIC - the sum of contacts for a given genomic bin is proportional to CNV. CAIC - raw interaction counts are the product of a CNV bias matrix and the expected contact counts at a given genomic distance. Data, and cancer-hic-norm - Normalization of cancer Hi-C data, scripts for the manuscript. LOIC and CAIC methods are implemented in the iced Python package,

  • HiCapp - Iterative correction-based caICB method. Method to adjust for the copy number variants in Hi-C data. Loess-like idea - we converted the problem of removing the biases across chromosomes to the problem of minimizing the differences across the count-distance curves of different chromosomes. Our method assumes equal representation of genomic locus pairs with similar genomic distances located on different chromosomes if there were no bias in the Hi-C maps.

  • OneD - CNV bias-correction method, addresses the problem of partial aneuploidy. Bin-centric counts are modeled using the negative binomial distribution, and its parameters are estimated using splines. A hidden Markov model is fit to infer the copy number for each bin. Each Hi-C matrix entry is corrected by dividing its value by the square root of the product of CNVs for the corresponding bins. Reproducibility score (eigenvector decomposition and comparison) to measure improvement in the similarity between replicated Hi-C data.


  • - a fast Python implementation of stratum-adjusted correlation coefficient metric for measuring similarity between Hi-C datasets (HiCrep method, originally in R). Can be used for MDS. Evaluated on 90 datasets from 4D Nucleome. More than 20 times faster on a single CPU. Results are the same as R implementation.

  • HPRep - reproducibility measure for HiChIP and PLAC-seq data. HiCrep-inspired. Reorganize data into anchor-centric interaction bins, normalize (fragment length, GC content, mappability, log2 obs/exp) smooth, stratify by distance (concatenate bins with the same distance from anchors). Considering "AND" (bin-pairs), "XOR" (one anchor bin), "NOT" (no interactions, ignored) bin pairs. Distance metric - weighted Pearson correlation (pairs of columns) stratified by distance. Compared with HiCRep, HiC-Spector, and naive Pearson on mouse H3K4me3 PLAC-seq data (brain and mESCs), human H3K37ac HiChIP data from GM12878 and K562, human H3K4me3 PLAC-seq brain data. HPRep shows higher similarity for replicates and more differentiation between cell lines, robust to downsampling. Nearly same results can be achieved analysing one chromosome (for speed).

  • IDR2D - Irreproducible Discovery Rate that identifies replicable interactions in ChIP-PET, HiChIP, and Hi-C data. Includes the original 1D IDR version ( Resolves multiple pairwise interactions. 

  • 3DChromatin_ReplicateQC - Comparison of four Hi-C reproducibility assessment tools,

    . Tested the effects of noise, sparsity, resolution. Spearman doesn't work well. All tools performed similarly, worsening expectedly. QuASAR has a QC tool measuring the level of noise.
  • HiCRep - Similarity assessment using generalized Cochran-Mantel-Haenzel statistics M2. Spearman/Pearson doesn't work. 2-step procedure: Smooth the matrix, then CMH statistics. Basically, splitting data by distance chunks, Pearson on each chunk, summarize. Simple and well-thought stats. Methods: Hi-C datasets with replicates, including 11 ENCODE datasets. R package, and Python implementation

  • [QuASAR] - Hi-C quality and reproducibility measure using spatial consistency between local and regional signals. Finds the maximum useful resolution by comparing quality and replicate scores of replicates. Part of HiFive pipeline

  • HiC-Spector - reproducibility metric to quantify the similarity between contact maps using spectral decomposition. Decomposing Laplacian matrices and sum the Euclidean distance between eigenvectors.

  • localtadsim - Analysis of TAD similarity using a variation of information (VI) metric as a local distance measure. 23 human Hi-C datasets, Hi-C Pro processed into 100kb matrices, Armatus to call TADs. Defining structurally similar and variable regions. Comparison with previous studies of genomic similarity. Cancer-normal comparison - regions containing pan-cancer genes are structurally conserved in normal-normal pairs, not in cancer-cancer.

AB compartments

  • dcHiC - differential A/B compartment analysis of Hi-C data. Uses Multiple Factor Analysis (MFA), and extension of PCA which combines Hi-C maps before performing generalized PCA. Analogous to weighted PCA in which every dataset is normalized for its biases (Methods). Multivariate distance measure to estimate statistical significance of compartment differences. Applied to mouse neuronal differentiation, mouse hematopoietic system, human  cell Hi-C data. Gene enrichment analysis shows biologically relevant signal. Input - sparse matrix, hic, cool files.

  • Eigenvector - Juicer's native tool. The eigenvector can be used to delineate compartments in Hi-C data at coarse resolution; the sign of the eigenvector typically indicates the compartment. The eigenvector is the first principal component of the Pearson's matrix.

Loop callers

  • LASCA - loop/significant contact caller that uses Weibull distribution-based modeling to each diagonal. DBSCAN to cluster adjacent significant pixels. Works with Hi-C data from any species, tested on human, C. Elegans, S. Cerevisiae. Filters according Aggregate Peak Analysis patterns may be used to refine calls. Compared with HiCCUPS, MUSTACHE, demonstrates good overlap. Also identifies non-CTCF-driven loops. Input - .cool files. Python code

  • HiC-ACT - improved chromatin loop detection considering spatial dependency (especially at high 5-10kb resolution). Aggregated Cauchy Test (ACT) based approach accounting for possible correlations between adjacent loci pairs from high-resolution Hi-C data. Combine a set of p-values, T statistics following Cauchy distribution under arbitrary dependence structure. Need the local smoothing bandwidth size. Post-processing of results from loop callers that assume independence among loci. Input - bin-pair identifiers and the corresponding p-values. Tested on GM12878 and mESC data. The improvement in power is most pronounced in low-depth (downsampled) data. Fast, implemented in R. GitHub

  • FIREcaller - an R package to call frequently interacting regions from Hi-C data, as well as clustered super-FIREs. Normalization using HiCNormCis to regress out systematic biases. Converts normalized cis-interactions into Z-scores, calculates one-sided p-values and classifies bins as FIRE/nonFIRE. Also outputs continuous FIREscore (-ln(p-value)). FIREs are tissue-specific, can distinguish samples. Associated with H3K27ac and H3K4me3 signal.

  • HiCExplorer's hicDetectLoops for loop detection. Review and critique of HiCCUPS, HOMER, GOTHIC, cLoops, FastHiC. Distance-dependent of chromatin interactions with a continuous negative binomial distribution, detection of the interaction counts with p-values smaller than a threshold, then filtering.

  • Chromosight - loop and pattern detection, computer vision-based (borders, FIREs, hairpins, and centromeres) in Hi-C maps. Takes in a single, whole-genome contact map, text-based bedGraph2d, and binary cool formats, ICE-normalizes. Sliding window, pattern detection using Pearson correlation with the template, then series of filters. Output - text-based. Outperforms HiCexplorer, HICCUPS, HOMER, cooltools, in the order of decreasing F1. Tested on synthetic Hi-C data mimicking S. cerevisiae genome, benchmark data at Zenodo, Python3 code

  • SIP - loop caller using image analysis. Regional maxima-based, peaks called in a sliding window. Distance-normalized Hi-C matrices, image adjusted using Gaussian blur, contrast enhancement, White Top-Hat correction, identified peaks then filtered by peak enrichment, empirical FDR, loop decay. Comparison with HiCCUPS and cLoops callers. Robust to noise, sequencing depth, much faster, good agreement, improved detection rate. SIPMeta - average metaplots of loops on bias-corrected images for better representation. Java implementation, works with .hic and .cool files

  • Mustache - loop detection from Hi-C and Micro-C maps. Scale-space theory, detection of blob-shaped objects in a multi-scale representation of contact maps, Gaussian kernels with increasing scales. Differences of adjacent Gaussians guide the search for local maxima. Series of filtering steps to minimize false positives. Corrected for multiple testing p-values of blobs. Applied to Gm12878 and K562 Hi-C data, and HFFc6 cell line Micro-C data, 5kb resolution. Compared with HiCCUPS, detects similar and more loops flanked by convergent CTCF, RAD21, SMC3, loops confirmed by ChIA-PET and HiChIP data. Python3 tool, Conda/Docker wrapped, handles .hic/.cool files., Tweet

  • FitHiC2 - protocol to install/run FitHiC Python3 tool/scripts. Fit of non-increasing cubic splines to distance-interaction frequency decay to identify significant interactions in individual matrices. Accounts for biases derived from KR (ICE, or other) normalization (HiCKRy). Works with fixed-bin- or restriction cut site resolution data. Overview of FitHiC algorithm, accounting for biases. Flexible input options, from HiC-Pro, Juicer, and other tools, validPairs file format. Post-processing to prioritize highly significant interactions supported by the nearby loci, and filter noisy detections. HTML report, flexible BED-derived output format, conversion to formats for WashU epigenome and UCSC browsers. Installable using conda, pip, GitHub. Comparable methods - HiCCUPS, HOMER, GOTHiC, HiC-DC, a brief description of each. Tested on three datasets. GitHub:, Executable on Code Ocean:, Data:

  • FIREcaller - an R package to detect frequently interacting regions (FIREs, <200Kb interactions). Within-sample (HiCNormCis) and cross-sample (quantile) normalization, converting FIRE counts to Z-scores, taking significant ones. Schmitt data

  • - Pile-up (aggregation, averaging) analysis of Hi-C data (.cool format) for visualizing and identifying chromatin loops from several sparse datasets, e.g., single-cell. Visualization using script. Scripts for the paper:, tool:

  • cLoops - DBSCAN-based algorithm for the detection of chromatin loops in ChIA-PET, Hi-C, HiChIP, Trac-looping data. Local permutation-based estimation of statistical significance, several tests for enrichment over the background. Outperforms diffHiC, Fit-Hi-C, GOTHiC, HiCCUPS, HOMER.

  • FitHiChIP - significant peak caller in HiChIP and PLAC-seq data. Accounts for assay-specific biases, as well as for the distance effect. 3D differential loops detection. Methods.

  • StripeCaller - A toolkit for analyzing architectural stripes. Architectural stripes, created by extensive loading of cohesin near CTCF anchors, with Nipbl and Rad21 help. Little overlap between B cells and ESCs. Architectural stripes are sites for tumor-inducing TOP2beta DNA breaks. ATP is required for loop extrusion, cohesin translocation, but not required for maintenance, Replication of transcription is not important for loop extrusion. Zebra algorithm for detecting architectural stripes, image analysis, math in Methods. Human lymphoblastoid cells, mouse ESCs, mouse B-cells activated with LPS, CH12 B lymphoma cells, wild-type, treated with hydroxyurea (blocks DNA replication), flavopiridol (blocks transcription, PolII elongation), oligomycin (blocks ATP). Hi-C, ChIA-pet, ChIP-seq, ATAC-seq, and more Data1Data2.

  • HiC-DC - significant interaction detection using the zero-inflated negative binomial model and accounting for biases like GC content, mappability. Compared with Fit-Hi-C, more conservative. Robust to sequencing depth. Detects significant, biologically relevant interactions at all length scales, including sub-TADs. BWA-MEM alignment (Python script), then processing in R.

  • GoTHIC - R package for peak calling in individual HiC datasets, while accounting for noise.

    • Mifsud, Borbala, Inigo Martincorena, Elodie Darbo, Robert Sugar, Stefan Schoenfelder, Peter Fraser, and Nicholas M. Luscombe. “GOTHiC, a Probabilistic Model to Resolve Complex Biases and to Identify Real Interactions in Hi-C Data.” Edited by Mark Isalan. PLOS ONE 12, no. 4 (April 5, 2017) - The GOTHiC (genome organization through HiC) algorithm uses a simple binomial distribution model to simultaneously remove coverage-associated biases in Hi-C data and detect significant interactions by assuming that the global background interaction frequency of two loci. Use of the Benjamini–Hochberg multiple-testing correction to control for the false discovery rate.
  • HMRFBayesHiC - a hidden Markov random field-based Bayesian peak caller to identify long-range chromatin interactions from Hi-C data. Borrowing information from neighboring loci. Previous peak calling methods, Fit-Hi-C. Interactions between enhancers and promoters as a benchmark.

  • FastHiC - hidden Markov random field (HMRF)-based peak caller, fast and well-performing.

  • FitHiC - Python tool for detection of significant chromatin interactions,

    • Ay, Ferhat, Timothy L. Bailey, and William Stafford Noble. “Statistical Confidence Estimation for Hi-C Data Reveals Regulatory Chromatin Contacts.” Genome Research 24, no. 6 (June 2014) - Fit-Hi-C method, Splines to model distance dependence. Model mid-range interaction frequencies, decay with distance. Biases, normalization methods. Two-step splines - use all dots for the first fit, identify and remove outliers, second fit without outliers. Markers of boundaries - insulators, heterochromatin, pluripotent factors. CNVs are enriched in chromatin boundaries. Replication timing data how-to Validation Hi-C data.
  • HiCPeaks - Python CPU-based implementation for BH-FDR and HICCUPS, two peak calling algorithms for Hi-C data, proposed by Rao et al. 2014. Text-to-cooler Hi-C data converter, two scripts to call peaks, and one for visualization (creation of a .png file).

  • HOMER - Perl scripts for normalization, visualization, significant interaction detection, motif discovery. Does not correct for bias.

Capture-C peaks

  • Peaky - Bayesian sparse variable selection approach. The model proposes that for any given bait, the expected CHi-C signal at each prey fragment is expressed as a sum of contributions from a set of fragments directly contacting that bait.

  • ChiCMaxima - a pipeline for detection and visualization of chromatin loops in Capture Hi-C data. Loess smoothing combined with a background model to detect significant interactions Comparison with GOTHiC and CHiCAGO.

  • HiCapTools - A software package that can design sequence capture probes for targeted chromosome capture applications and analyze sequencing output to detect proximities involving targeted fragments. Two probes are designed for each feature while avoiding repeat elements and non-unique regions. The data analysis suite processes alignment files to report genomic proximities for each feature at restriction fragment level and is isoform-aware for gene features. Statistical significance of contact frequencies is evaluated using an empirically derived background distribution.

  • CHiCAGO is a Capture Hi-C data processing method that filters out contacts that are expected by chance given the linear proximity of the interacting fragments on the genome and takes into account the asymmetric biases introduced by the capture step used in the Capture Hi-C approach. Two-component background model (Delaporte distribution) - Brownian motion (Neg. Binom.) and technical noise (Poisson). Account for distance., Tweet by Mikhail Spivakov: Running Chicago with data generated w/ a 4-cutter such as DpnII? Default settings were tuned on 6-cutter data (HindIII) & not optimal for this. Our suggested settings for DpnII are: MaxLBrowndist = 75000, binsize = 1500, minFragLen=75, maxFragLen=1200.

Differential interactions

  • CHESS (Comparison of Hi-C Experimentss using Structural Similarity) - comparative analysis of Hi-C matrices and automatic feature extraction (TADs, loops, stripes). Image analysis-based structural similarity index (SSIM, combines brightness, contrast, and structure differences, S = 1 - identical matrices, <1 - differences) to assign similarity score and an associated p-value (from empirical distribution of SSIMs, two types of bacground model, Methods) to pairs of genomic regions. Obs/exp transformation, differential matrix, denoise, smooth, binarize, feature extraction using close morphology filter, k-means clustering, classification. Works with low sequencing depth, high noise data. Outperforms diffHiC, HOMER, and ACCOST. Applied to interspecies comparison of syntenic regions (Synteny portal), WT and Zelda-depleted Drosophila, C-cell lymphoma, Capture-C analysis. Input - Juicer, Cooler, of FAN-C format, plus .bedpe for regions to compare (chess pairs to generate). Python, scikit-image module. Documentation

  • BART3D - transcriptional regulators associated with differential chromatin interactions from Hi-C data. Input: HiC-Pro matrices, .hic Juicer files, .cool files. Output - ranked lists of transcriptional regulators. Distance-based normalization to average of individual matrices. Difference detection by a paired t-test of normalized interactions within 200kb (Figure 1A). Differential interactions are mapped to the union of DNAseI hypersensitive sites, then standard BART algorithm. Python implementation.

  • HICDCPlus - an R package for Hi-C/Hi-ChIP interaction calling (directly from raw data, negative binomial regression accounting for genomic distance,GC content, mappability, restriction enzyme-based bin size) and differential analysis (DESeq2). Includes TAD (TopDom) and A/B compartment callers. Input - HiC-Pro or Juicer. Output compatible with visualization in Juicebox and HiCExplorer. Compared with diffHiC, multiHiCcompare, Selfish, provides better results.

  • DiffGR - differentially interacting genomic regions. Stratum-adjusted correlation coefficient (SCC) (HiCrep-inspired) to measure similarity of local TAD regions. Focus on within-TAD interactions. Simulated data at various levels of sparsity, noise, HiCseg for TAD calling. 2D mean filter for smoothing, KR normalization. Permutation test to estimate the significance of SCC changes. FDR depends on the proportion of altered TADs. R implementation

  • Serpentine - differential analysis of two Hi-C maps using the 2D serpentine-binning method. Serpentine is a subset of connected pixels defined by thresholds in control and experimental contact maps. Serpentines are then compared using the Mean-Deviation plot. Help to alleviate the effect of sparsity. Uses HiCcompare functionality. Normalization does not help. Python package, currently processes full 1500x1500 matrices.

  • multiHiCcompare - joint normalization of multiple Hi-C datasets using cyclic loess regression through pairs of MD plots (minus-distance). Data-driven normalization accounting for the between-dataset biases. Per-distance edgeR-based testing of significant interactions.

  • Chicdiff - differential interaction detection in Capture Hi-C data. Signal normalization based on the CHiCAGO framework, differential testing using DESeq2. Accounting for distance effect by the Independent Hypothesis Testing (IHW) method to learn p-value weights based on the distance to maximize the number of rejected null hypotheses.

  • Selfish - comparative analysis of replicate Hi-C experiments via a self-similarity measure - local similarity borrowed from image comparison. Check reproducibility, detect differential interactions. Boolean representation of contact matrices for reproducibility quantification. Deconvoluting local interactions with a Gaussian filter (putting a Gaussian bell around a pixel), then comparing derivatives between contact maps for each radius. Simulated (Zhou method) and real comparison with FIND - better performance, especially on low fold-changes. Stronger enrichment of relevant epigenomic features. Matlab implementation

  • HiCcompare - joint normalization of two Hi-C datasets using loess regression through an MD plot (minus-distance). Data-driven normalization accounting for the between-dataset biases. Per-distance permutation testing of significant interactions.

  • diffloop - Differential analysis of chromatin loops (ChIA-PET). edgeR framework.

  • FIND - differential chromatin interaction detection comparing the local spatial dependency between interacting loci. Previous strategies - simple fold-change comparisons, binomial model (HOMER), count-based (edgeR). FIND exploits a spatial Poisson process model to detect differential chromatin interactions that show a significant change in their interaction frequency and the interaction frequency of their adjacent bins. "Variogram" concept. For each point, compare densities between conditions using Fisher's test. Explored various multiple correction testing methods, used r^th ordered p-values (rOP) method. Benchmarking against edgeR in simulated settings - FIND outperforms at shorter distances, edgeR has more false positives at longer distances. Real Hi-C data normalized using KR and MA normalizations. R package

    • Mohamed Nadhir, Djekidel, Yang Chen, and Michael Q. Zhang. “FIND: DifFerential Chromatin INteractions Detection Using a Spatial Poisson Process.” Genome Research, February 12, 2018.
  • AP - aggregation preference - parameter, to quantify TAD heterogeneity. Call significant interactions within a TAD, cluster with DBSCAN, calculate weighted interaction density within each cluster, average. AP measures are reproducible. Comparison of TADs in Gm12878 and IMR90 - stable TADs change their aggregation preference, these changes correlate with LINEs, Lamin B1 signal. Can detect structural changes (block split) in TADs.

  • diffHiC - Differential contacts using the full pipeline for Hi-C data. Explanation of the technology, binning. MA normalization, edgeR-based. Comparison with HOMER.

Differential abundance

  • Milo - an R package for differential abundance testing on scRNA-seq data between two groups or multiple conditions. Building a graph on the first 40 components of PCA, defining neighborhoods using a graph sampling algorithm. Each neighborhood (partially overlapping, in contrast to discrete clustering) contains cells from different conditions - differential abundance is tested using a negative binomial GLM. Tested on simulated datasets (dyntoy), a time course of mouse thymic epithelial cells development, liver cirrhosis analysis. Replicated datasets needed, batch corrected. Competitors: DA-seq, Cydar. Code to reproduce results for the paper

TAD callers

  • GRiNCH - TAD detection algorithm using Graph Regularized Nonnegative matrix factorization. Graph captures distance dependence. Also smoothes/imputes Hi-C matrices. Compared with Directionality Score, Insulation Index, rGMAP, Armatus, HiCseg, TopDom using several metrics - Davies-Bouldin index, Delta Contact Counts, Rand Index, Mutual Information. GRiNCH detects consistent similarly-sized TADs, robust to different resolutions, boundaries show robust enrichment in known markers of TAD boundaries (TFBSs and histone), detects consistent Fit-Hi-C significant interactions (area under precision-recall curve). Applied to mouse neural development and pluripotency reprogramming to confirm known and discover new boundary regulators. Applied to SPRITE and HiChIP data. Project, GitHub, and Visualization tutorial

  • BHi-Cect - identification of the full hierarchy of chromosomal interactions (TADs). Spectral clustering starting from the whole chromosome, detecting nested BHi-Cect Partition Trees (BPTs), partitioned in non-contiguous and interwoven enclaves, inspired by fractal globule idea. Variation of information to test the agreement between two clustering results, overlap-based metrics to test correspondence with TADs. Correspondence analysis of enclaves association with TF content. Gene enrichment. Different enclaves show different epigenomic and gene expression signatures, bottom enclaves are most crisply defined. Resolution affects what enclave size can be detected.

  • TADBD TAD caller using a multi-scale Haar diagonal template (sum of on-diagonal squares minus the sum of off-diagonal squares). Compared with HiCDB, IC-Finder, EAST (also using Haar features), TopDom, HiCseg using simulated (Forcato) and experimental (K562 and IMR90). ICE-normalized data. MCC, Jaccard. FAst. R package

  • TADpole - hierarchical TAD boundary caller. Preprocessing by filtering sparse rows, transforming the matrix into its Pearson correlation coefficient matrix, running PCA on it and retaining 200 PCs, transforming into a Euclidean distance matrix, clustering using the Constrained Incremental Sums of Squares clustering (rioja::chclust(, coniss)), estimating significance, Calinski-Harabasz index to estimate the optimal number of clusters (chromatin subdivisions). Benchmarking using Zufferey 2018 datasets, mouse limb bud development with genomic inversions from Kraft 2019. Resolution, normalization, sequencing depth. Metrics: the Overlap Score, the Measure of Concordance, all from Zufferey 2018. Enrichment in epigenomic marks. DiffT metric for differential analysis (on binarized TAD/non-TAD matrices). Compared with 22 TAD callers, including hierarchical (CaTCH, GMAP, Matryoshka, PSYCHIC).

  • HiCDB - TAD boundary detection using local relative insulation (LRI) metric, improved stability, less parameter tuning, cross-resolution, differential boundary detection, lower computations, visualization. Review of previous methods, directionality index, insulation score. Math of LRI. GSEA-like enrichment in genome annotations (CTCF). Differential boundary detection using the intersection of extended boundaries. Compared with Armatus, DI, HiCseg, IC-finder, Insulation, TopDom on 40kb datasets. Accurately detects smaller-scale boundaries. Differential TADs are enriched in cell-type-specific genes.

  • OnTAD - hierarchical TAD caller, Optimal Nested TAD caller. Sliding window, adaptive local minimum search algorithm, similar to TOPDOM. C++ implementation. OnTAD for coolers - a Python wrapper to work with

    • An, Lin, Tao Yang, Jiahao Yang, Johannes Nuebler, Qunhua Li, and Yu Zhang. “Hierarchical Domain Structure Reveals the Divergence of Activity among TADs and Boundaries,” July 3, 2018. - Intro about TADs, Dixon's directionality index, Insulation score. Other hierarchical callers - TADtree, rGMAP, Arrowhead, 3D-Net, IC-Finder. Limitations of current callers - ad hoc thresholds, sensitivity to sequencing depth and mapping resolution, long running time and large memory usage, insufficient performance evaluation. Boundaries are asymmetric - some have more contacts with other boundaries, support for asymmetric loop extrusion model. Performance comparison with DomainCaller, rGMAP, Arrowhead, TADtree. Stronger enrichment of CTCF and two cohesin proteins RAD21 and SMC3. TAD-adjR^2 metric quantifying the proportion of variance in the contact frequencies explained by TAD boundaries. Reproducibility of TAD boundaries - Jaccard index, tested at different sequencing depths and resolutions. Boundaries of hierarchical TADs are more active - more CTCF, epigenomic features, TFBSs expressed genes. Super-boundaries - shared by 5 or more TADs, highly active. Rao-Huntley 2014 Gm12878 data. Distance correction - subtracting the mean counts at each distance.
  • 3D-NetMod - hierarchical, nested, partially overlapping TAD detection using graph theory. Community detection method based on the maximization of network modularity, Louvain-like locally greedy algorithm, repeated several (20) times to avoid local maxima, then getting consensus. Tuning parameters are estimated over a sequence search. Benchmarked against TADtree, directionality index, Arrowhead. ICE-normalized data brain data from Geschwind (human data) and Jiang (mouse data) studies. Computationally intensive. Python implementation

    • Norton, Heidi K., Daniel J. Emerson, Harvey Huang, Jesi Kim, Katelyn R. Titus, Shi Gu, Danielle S. Bassett, and Jennifer E. Phillips-Cremins. “Detecting Hierarchical Genome Folding with Network Modularity.” Nature Methods 15, no. 2 (February 2018): 119–22.
  • deDoc - TAD detection minimizing structural entropy of the Hi-C graph (structural information theory). Detects optimal resolution (= minimal entropy). Pooled 10 single-cell Hi-C analysis. Intro about TADs, a brief description of TAD callers, including hierarchical. Works best on raw, non-normalized data, highly robust to sparsity (0.1% of the original data sufficient). Compared with five TAD callers (Armatus, TADtree, Arrowhead, MrTADFinder, Domaincall (DI)), and a classical graph modularity detection algorithm. Enrichment in CTCF, housekeeping genes, H3K4me3, H4K20me1, H3K36me3. Other benchmarks - weighted similarity, number, length of TADs. Detects hierarchy over different passes. Java implementation (won't run on Mac)

    • Li, Angsheng, Xianchen Yin, Bingxiang Xu, Danyang Wang, Jimin Han, Yi Wei, Yun Deng, Ying Xiong, and Zhihua Zhang. “Decoding Topologically Associating Domains with Ultra-Low Resolution Hi-C Data by Graph Structural Entropy.” Nature Communications 9, no. 1 (15 2018): 3265.
  • CaTCH - identification of hierarchical TAD structure. Reciprocal insulation (RI) index. Benchmarked against Dixon's TADs (diTADs). CTCF enrichment as a benchmark, enrichment of TADs in differentially expressed genes.

  • HiTAD - hierarchical TAD identification, different resolutions, correlation with chromosomal compartments, replication timing, gene expression. Adaptive directionality index approach. Data sources, methods for comparing TAD boundaries, reproducibility. H3K4me3 enriched and H3K4me1 depleted at boundaries. TAD boundaries (but not sub-TADs) separate replication timing, A/B compartments, gene expression.

  • IC-Finder - Segmentations of HiC maps into hierarchical interaction compartments,

  • ClusterTAD - A clustering method for identifying topologically associated domains (TADs) from Hi-C data,

  • EAST - Efficient and Accurate Detection of Topologically Associating Domains from Contact Maps, Haar-like features (rectangles on images) and a function that quantifies TAD properties: frequency within is high, outside - low, boundaries must be strong. Objective - finding a set of contiguous non-overlapping domains maximizing the function. Restricted by the maximum length of TADs. Boundaries are enriched in CTCF, RNP PolII, H3K4me3, H3K27ac.

  • TADtree - Hierarchical (nested) TAD identification. Two ways of TAD definition: 1D and 2D. Normalization by distance. Enrichment over the background.

  • TopDom - An efficient and Deterministic Method for identifying Topological Domains in Genomes, Method is based on the general observation that within-TAD interactions are stronger than between-TAD. binSignal value as the average of nearby contact frequency, fitting a curve, finding local minima, test them for significance. Fast, takes linear time. Detects similar domains to HiCseq and Dixon's directionality index. Found expected enrichment in CTCF, histone marks. Housekeeping genes and overall gene density are close to TAD boundaries, differentially expressed genes are not.

  • TADtool - wrapper for directionality index and insulation score TAD callers.

  • Arboretum-Hi-C - a multitask spectral clustering method to identify differences in genomic architecture. Intro about the 3D genome organization, TAD differences, and conservation. Assessment of different clustering approaches using different distance measures, as well as raw contacts. Judging clustering quality by enrichment in genomic regulatory signals (Histone marks, LADs, early vs. late replication timing, TFs like POLII, TAF, TBP, CTCF, P300, CMYC, cohesin components, LADs, replication timing, SINE, LINE, LTR) and by numerical methods (Davies-Bouldin index, silhouette score, others). Although spectral clustering on contact counts performed best, spectral + Spearman correlation was chosen. Comparing cell types identifies biologically relevant differences as quantified by enrichment. Peak counts or average signal within regions were used for enrichment. Data, and Arboretum-HiC

  • Armatus - TAD detection at different resolutions, Dynamic programming method.

  • HiCseg - TAD detection by maximization of likelihood based block-wise segmentation model. 2D segmentation rephrased as 1D segmentation - not contours, but borders. Statistical framework, solved with dynamic programming. Dixon data as gold standard. Hausdorff distance to compare segmentation quality. Parameters (from TopDom paper): nbchangemax = 500, distrib = 'G' and model = 'Dplus'.

  • domaincaller - A Python implementation of the original DI domain caller,

Architectural stripes

  • Stripenn - a computer vision-based method for architectural stripes detection using Canny edge detection.Scores stripes by median p-value and stripiness based on the continuity of interaction signal. Input - .cool files, optionally normalized. Output - coordinates and scores of the predicted stripes. Applicable to Hi-C, HiChIP, Micro-C data. Introduction to the biology of architectural stripes, review of previous methods (Zebra from Vian et al. 2018, domainClassifyR, CHESS for comparing 3D domains and stripes). Analysis of stripes from B and T lymphocytes identifies stripe anchors enriched in the transcriptionally active compartments, architectural proteins mediating loop extrusion. Strips are strongly conserved, correspond to TAD boundaries, subtle changes are associated with transcriptional output. Python, three functions (compute, score seeimage).

Differential/timecourse TAD analysis

  • TADcompare - R package for differential and time-course TAD boundary analysis. Uses SpectralTAD score - spectral decomposition of Hi-C matrices - to statistically detect five types of differential TAD boundaries: merge, split, complex, shifted, strength change. In the time-course analysis, detects six types of boundary score changes: highly common, early appearing, late appearing, early disappearing, late disappearing, and dynamic TAD boundaries. Returns genomic coordinated and types of TAD boundary changes in BED format.

  • InterTADs - TAD-centric integration of multi-omics (SNPs, gene expression, methylation) data by genomic coordinateы, proper data scaling, and differential analysis. Differential TAD analysis by interaction strength changes, the number of other differential events. R scripts, Wiki

  • BPscore metric to compare two TAD segmentations. Formula, methods. More stable to Variation of Information (VI) and Jaccard Index (JI). Python implementation for calculating all three metrics.

  • Sauerwald, Natalie, Akshat Singhal, and Carl Kingsford. “Analysis of the Structural Variability of Topologically Associated Domains as Revealed by Hi-C.” NAR Genomics and Bioinformatics, 30 September 2019 - TAD variability among 137 Hi-C samples (including replicates, 69 if not) from 9 studies. HiCrep, Jaccard, TADsim to measure similarity. Variability does not come from genetics. Introduction to TADs. 10-70% of TAD boundaries differ between replicates. 20-80% differ between biological conditions. Much less variation across individuals than across tissue types. Lab -specific source of variation - in situ vs. dilution ligation protocols, restriction enzymes not much. HiCpro to 100kb data, ICE-normalization, Armatus for TAD calling. Table 1 - all studies and accession numbers.

  • Sauerwald, Natalie, and Carl Kingsford. “Quantifying the Similarity of Topological Domains across Normal and Cancer Human Cell Types.” Bioinformatics (Oxford, England), (July 1, 2018) - Analysis of TAD similarity using variation of information (VI) metric as a local distance measure. Defining structurally similar and variable regions. Comparison with previous studies of genomic similarity. Cancer-normal comparison - regions containing pan-cancer genes are structurally conserved in normal-normal pairs, not in cancer-cancer. Kingsford-Group/localtadsim. 23 human Hi-C datasets, Hi-C Pro processed into 100kb matrices, Armatus to call TADs.

  • DiffTAD - differential contact frequency in TADs between two conditions. Two - permutation-based comparing observed vs. expected median interactions, and parametric test considering the sign of the differences within TADs. Both tests account for distance stratum.

  • dcHiC - Differential Compartment Analysis of Hi-C Datasets,

Prediction of 3D features

  • 3DPredictor - Prediction of enhnancer-promoter interactions from gene expression, CTCF binding and orientation, distance between interacting loci. Also predict changes in 3D genome organization - genomic rearrangements. Benchmarking of TargetFinder, its performance is overestimated. Class balance slightly improves performance. Two random chromosomes for validation, the rest - for training (10-90% split). Gradient boosting for prediction, significantly better than Random Forest. Multiple performance metrics. Model trained in one cell type can predict in another. Predict quantitative level of interactions. Other tools - EP2Vec, CTCF-MP, HiC-Reg. GitHub, Web tool

    • Belokopytova, Polina S., Miroslav A. Nuriddinov, Evgeniy A. Mozheiko, Daniil Fishman, and Veniamin Fishman. "Quantitative prediction of enhancer–promoter interactions." Genome research 30, no. 1 (2020): 72-84.
  • 3D-GNOME 2.0 - modeling 3D structure changes due to structural variants (SVs). Reference data - GM12878 ChIA-PET data (CTCF, RNAPII). Changes are modeled by removing or adding contacts between chromatin interaction anchors depending on SV (deletion, duplication, insertion, inversion). Web tool, Bitbucket

  • HiC-Reg - Predicting Hi-C contact counts from one-dimensional regulatory signals (Histone marks, CTCF, RAD21, Tbp, DNAse). Random Forest regression. Feature encoding - distance between two regions, pair-concat, window, multi-cell. Works across chromosomes (some chromosomes are worse than others) and cell lines (Gm12878, K562, Huvec, Hmec, Nhek, can be used to predict interactions on new cell lines). Selection of the most important features using multi-task group LASSO (distance, CTCF, Tbp, H4K20me1, DNAse, others). Predicted contacts correspond well to the original contacts (distance-stratified Pearson correlation), define TADs similar to the originals (Jaccard), define significant contacts (Fit-Hi-C) more enriched in CTCF binding. Validated on HBA1 and PAPPA gene promoters. Hi-C normalization doesn't have much effect.

  • TADBoundaryDectector - TAD boundary prediction from sequence only using deep learning models. 12 architectures tested, with three convolutional and an LSTM layer performed best. Methods, Implementation in Keras-TensorFlow. Model evaluation using different criteria, 96% accuracy reported. Deep learning outperforms feature-based models, among which Boosted Trees, Random Forest, elastic net logistic regression are the best performers. Data augmentation (aka feature engineering) by randomly shifting TAD boundary regions by som base pairs of length (0-100). Tested on Drosophila data.

  • 3DEpiLoop - prediction of 3D interactions from 1D epigenomic profiles using Random Forest trained on CTCF peaks (histone modifications are the most important predictors and TFBSs).

    - a polymer-based method (strings and binders switch SBS polymer model) to model 3D chromatin folding, to predict enhancer-promoter contacts, and to model the effect of structural variations (deletions, duplications, inversions) on the 3D genome organization. Input - Hi-C data. Infers minimal factors that shape chromatin folding and its equilibrium under the laws of physics, without prior assumptions or tunable parameters. Simulated annealing Monte Carlo optimization procedure that minimizes the distance between the predicted polymer model and the input contact matrix under a Bayesian weighting factor to avoid overfitting. Tested on a EPHA4 locus associated with limb malformations, and more. Newly generated capture Hi-C of mouse limb buds at embryonic day 11.5, and human skin fibroblasts, GEO. Public mutine CH12-LX cells and human IMR90 cells. Implementation - the LAMMPS package, code not available.
  • SNIPER - 3D subcompartment (A1, A2, B1, B2, B3) identification from low-coverage Hi-C datasets. A neural network based on a denoising autoencoder (9 layers) and a multi-layer perceptron. Sigmoidal activation of inputs, ReLU, softmax on outputs. Dropout, binary cross-entropy. exp(-1/C) transformation of Hi-C matrices. Applied to Gm12878 and 8 additional cell types to compare subcompartment changes. Compared with Rao2014 annotations, outperforms Gaussian HMM and MEGABASE.

  • Predicting TAD boundaries using training data and making new predictions. Bayesian network (BNFinder method), random forest vs. basic k-means clustering, ChromHMM, cdBEST. Using sequence k-mers and ChIP-seq data from modENCODE for prediction - CTCF ChIP-seq performs best. Used Boruta package for feature selection. The Bayesian network performs best. To read on their BNFinder method


  • iRegNet3D - Integrated Regulatory Network 3D (iRegNet3D) is a high-resolution regulatory network comprised of interfaces of all known transcription factor (TF)-TF, TF-DNA interaction interfaces, as well as chromatin-chromatin interactions and topologically associating domain (TAD) information from different cell lines. Goal: SNP interpretation. Input: One or several SNPs, rsIDs, or genomic coordinates. Output: For one or two SNPs, on-screen information of their disease-related info, connection over TF-TF and chromatin interaction networks, and whether they interact in 3D and located within TADs. For multiple SNPs, the same info downloadable as text files.

  • 3DSNP - 3DSNP database integrating SNP epigenomic annotations with chromatin loops. Linear closest gene, 3D interacting gene, eQTL, 3D interacting SNP, chromatin states, TFBSs, conservation. For individual SNPs.

  • HUGIn - tissue-specific Hi-C linear display of anchor position and around. Overlay gene expression and epigenomic data. Association of SNPs with genes based on Hi-C interactions. Tissue-specific.

    • Martin, Joshua S, Zheng Xu, Alex P Reiner, Karen L Mohlke, Patrick Sullivan, Bing Ren, Ming Hu, and Yun Li. “HUGIn: Hi-C Unifying Genomic Interrogator.” Edited by Inanc Birol. Bioinformatics 33, no. 23 (December 1, 2017)

CNV and Structural variant detection

  • hicpipe and, alternatively, HiCnorm normalization preserves CNVs in Hi-C data. From Zhang et al., “Local and Global Chromatin Interactions Are Altered by Large Genomic Deletions Associated with Human Brain Development.”

  • hic_breakfinder - Detection of structural variants (SV) by integrating optical mapping, Hi-C, and WGS. Custom pipeline using LUMPY, Delly, Control-FREEC software. New Hi-C data on 14 cancer cell lines and 21 previously published datasets. Integration of the detected SVs with genomic annotations, including replication timing. Supplementary data with SVs resolved by individual methods and integrative approaches.

  • HiCnv, HiTrans - CNV, translocation calling from Hi-C data. CNV calling using HMM on per-restriction site quantified data and 1D-normalized accounting for low GC-content (<0.2), mappability (<0.5). Translocation calling on inter-chromosomal matrices, binned.,

  • HiNT - CNV and translocation detection from ~10-20% ambiguous chimeric reads in Hi-C data. Three tools: HiNT-Pre - preprocessing of Hi-C data; HiNT-CNV and HiNT-TL - CNV and translocation detection, respectively (accept HiC-Pro output). Tested on K562 (cancer) and Gm12878 (normal) data. Removal of known biases using a GAM with Poisson function. Outperforms Delly, Meerkat, hic_breakfinder, HiCtrans. Relatively little overlap with CNVs from WGS (BIC-seq2). Gold-standard - FISH data from Dixon et al., “Integrative Detection and Analysis of Structural Variation in Cancer Genomes.”

  • TAD fusion score - quantifying the effect of deletions on Hi-C interactions. Intro about TAD fusion effect on genome structure. TAD fusion score - the expected total number of changes in pairwise genomic interactions as a result of the deletion. TAD fusion events are negatively selected.


  • CoolBox - a pyGenomeTracks-based Hi-C data visualization in Jupyter and command line, matplotlib plotting system. Works with .hic and .m/cool input, plus .bed, .pairs etc. Documentation and examples

  • Hi-C data visualization review. Good introduction into the 3D genome organization, 115 key references. Table 2. Hi-C visualization tools

  • circHiC - circular representation of Hi-C data, projection into polar coordinates. Linear relationship between the radius of a circle and the corresponding genomic distance. Predominantly for bacterial chromosomes. Python implementation. Documentation

  • GENOVA - an R package for the most common Hi-C analyses and visualization. Quality control - cis/trans ratio, checking for translocations; A/B compartments - single and differential compartment signal (H3K4me1 for compartment assignment); TADs - insulation score and directionality index; Genomic annotations - ChIP-seq signal, gene information; Distance decay, and differential analysis; Saddle plot; Aggregate region/peak/TAD/loop analysis, and differential analysis; Aggregated signal (tornado) plots; Intra-inter-TAD interaction differences; PE-SCAn (paired-end Spatial Chromatin Analysis) and C-SCAn enhancer-promoter aggregation. Data for statistical tests can be extracted from the discovery objects. Applied to Hi-C data from Hap1 cells, WT and deltaWAPL (published) and knockdown cohesin-SA1/SA2 conditions (HiC-pro, hg19, HiCCUPS). Input - HiC-pro, Juicer, .cool files. Storage - compressed sparse triangle format and the user-added metadata. All tools return the "discovery object". GitHub, Vignette

  • HiCeekR - Shiny app and GUI for Hi-C data analysis and interpretation. Input - aligned BAM file, with marked duplicates, restriction enzyme cutting sites (HRF5), genome in FASTQ, optionally ChIP-seq BAM, or RNA-seq gene expression (TSV). The workflow includes filtering (PCR artifacts, self-circle, dangling end fragments, using diffHiC) with diagnostic plots, binning interaction matrices in BED (coordinates) and TSV (counts) formats, normalization (ICE, WavSiS, using chromoR), calling A/B compartments (PCA, using HiTC), TADs (directionality index, TopDom, HiCseg), gene expression/epigenomic integration, network analysis and enrichment in GO, KEGG, other databases (using gProfileR). Visualization of zoomable heatmaps, networks (ggplot2, plotly, heatmaply, networkD3). Starts with creating configuration file. Compared with GITAR, HiCPro, HiC-bench, HiCdat, HiCexplorer, not Juicer or HiGlass. Illustrated using Rao2014 Gm12878 data. 32Gb RAM (minimal 16Gb) is sufficient, preprocessing of BAM files (Hi-C or ChIP-seq) is the longest.

  • HiCBricks - data format and visualization package. hdf5-based data storage format to handle large Hi-C matrices. Visualization of one or two Hi-C matrices, adding annotations.

  • 3D Genome Browser - visualizing existing Hi-C and other chromatin conformation capture data. Alongside with genomic and epigenomic data. Own data can be submitted in BUTLR format.

  • DNARchitect - a Shiny App for visualizing genomic data (HiC, mRNA, ChIP, ATAC, etc.) in bed, bedgraph, and bedpe formats. Wraps Sushi R package. Web-version,, GitHub,

  • HiGlass - visualization server for Google maps-style navigation of Hi-C maps. Overlay genes, epigenomic tracks. "Composable linked views" synchronizing exploration of multiple Hi-C datasets linked by location/zoom. GitHub, documentation, links and resources

  • HiPiler - exploration and comparison of loops and domains as snippets-heatmaps of data. GitHub, documentation

  • HiCPlotter - Hi-C visualization tool, allows for integrating various data tracks. Python implementation

  • NAT - the 4D Nucleome Analysis Toolbox, for Hi-C data (text, cool format) normalization (ICE, Toeplitz, CNV-Toeplitz), TAD calling (Directionality index, Armatus, custom), karyotype abnormalities visualization on inter-chromosomal matrices, time-course visualization. Matlab.

  • HiC-3DViewer - HiC-3DViewer is an interactive web-based tool designed to provide an intuitive environment for investigators to facilitate the 3D exploratory analysis of Hi-C data. It based on Flask and can be run directly or as a docker container.

  • NuChart - gene-centric network of genes interacting in 3D. Integration of epigenomic features. Statistical network analysis.

    • Merelli, Ivan, Pietro Liò, and Luciano Milanesi. “NuChart: An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations.” PloS One 8, no. 9 (2013): e75146.
  • HiTC - R package for High Throughput Chromosome Conformation Capture analysis, Processed data import from TXT/BED into GRanges. Quality control, visualization. Normalization, 45-degree rotation and visualization of triangle TADs. Adding annotation at the bottom. PCA to detect A/B compartments.

  • BentoBox - R-based multi-omics data visualization (Hi-C, genes, signal). Documentation

  • CoolBox - Jupyter notebook based genomic data visulization toolkit utilizing pyGenomeTracks.

  • pyGenomeTracks - python module to plot beautiful and highly customizable genome browser tracks,

  • TADKit - 3D Genome Browser. Main web site,, and GitHub,

De novo genome scaffolding

  • instaGRAAL - reimplementation of GRAAL genome assembler (chromosome level) for large genomes. Similar MCMC approach, implemented on NVIDIA GPU. Tested, among others, on segments of the human genome.

  • HiCAssembler - Hi-C scaffolding tool combining assembly using Hi-C data with scaffolds from regular sequencing (short or long sequencing). Uses strategies from LACHESIS and 3D-DNA. Visual adjustment of scaffolding errors. Automatic and manual misassembly correction.

  • bin3C - resolving metagenome-assembled genomes from Hi-C data. Metagenomic assembly using SPAdes. Tested using simulated (Sim3C and MetaART) and real-life data. Performance metrics: adjusted mutual information, weighted Bcubed. Contact matrix where bins are contigs. Infomap method for clustering the whole-contig graph. Compared with ProxiMeta (Phase Genomics).

  • 3D-DNA Hi-C genome assembler and its application/validation. Methods are in the supplemental.

  • GRAAL - Genome (Re)Assembly Assessing Likelihood - genome assembly from Hi-C data. Gaps in genome assembly that can be filled by scaffolding. Superior to Lachesis and dnaTri, which are sensitive to duplications, clustering they use to initially arrange the scaffolds, parameters, unknown reliability. A Bayesian approach, prior assumptions are that cis-contact probabilities follow a power-law decay and that counts in the interaction matrix are Poisson. Multiple genomic structures tested using MCMC (Multiple-Try Metropolis algorithm) to maximize the likelihood of data given a genomic structure.

  • dnaTri - genome scaffolding via probabilistic modeling using two constraints of Hi-C data - distance-dependent decay and cis-trans ratio. Using known chromosome scaffolds and de novo assembly. Naive Bayes classifier to distinguish chromosome-specific vs. on different chromosomes contigs. Average linkage clustering to assemble contigs into 23 groups of chromosomes. Completed 65 previously unplaced contigs. Data,

  • Lachesis - a three-step genome scaffolding tool: 1) graph clustering of scaffolds to chromosome groups, 2) ordering clustered scaffolds (minimum spanning tree, reassembling longest-to-shortest branches), 3) assigning orientation (exact position and the decay of interactions). Duplications and repeat regions may be incorrectly ordered/oriented. Tested on a normal human, mouse, drosophila genomes, and on the HeLa cancer genome.

  • Genome-Assembly-Cookbook - Genome assembly from Hi-C data, pipeline and instructions from the Aiden Lab. Download PDF, another PDF

3D modeling

  • Pastis-NB - an extrension of Pastis 3D modeling tool with negative binomial distribution-based modeling. Compared with MDS-based methods (ShRec3D, ChromSDE, Pastis-MDS) and Pastis- PM (Poisson model). More accurate and stable results. Supplementary material

  • 4DMax - 3D modeling over time, predicts dynamic chromosome conformation using time course Hi-C data. In contrast to TADdyn, models entire chromosomes, uses gradient descent optimization of a spatial restraint-based maximum-likelihood function. In contrast to TADdyn that focuses on approx. 2Mb retions, 4DMax models whole chromosomes. Tested on simulate Hi-C progression over 6 time points, and 10-day time course of induced stem cell pluripotency in mice. Preserves and predicts A/B compartments, TADs. Output - video of chromosome dynamics

  • TADdyn - studying time-dependent dynamics of chromatin domains during natural and induced cell processes by simulating smooth 3D transitions of chromosome structure. A part of TADBit, developed by the Marti-Renom group. Tested on in situ Hi-C time course experiment, reprogramming of murine B cells to pluripotent cells, changes of 21 genomic loci. Data and video

  • StoH-C - 3D genome reconstruction using tSNE. Python scripts for 3D embedding and visualization (plot-ly, matplotlib, Chart Studio). Visually tested on fission yeast genome as compared with MDS-reconstructed genome (wild type, G1-arrested, rad21 mutation, clr4 deletion).

  • Hierarchical3DGenome - high-resolution (5kb) reconstruction of the 3D structure of the genome. Using LorDG (, first, assemble the 3D model at the level of TADs, then inside individual TADs. Gm12878 cell line, Arrowhead for TAD calling, KR and ICE normalization, benchmarking against miniMDS, five tests including comparison with FISH.

  • CSynth - 3D genome interactive modeling on GPU, and visualization.

  • GenomeFlow - a complete set of tools for Hi-C data alignment, normalization, 2D visualization, 3D genome modeling and visualization. ClusterTAD for TAD identification. LorDG and 3DMax for 3D genome reconstruction.

  • ShRec3D - shortest-path reconstruction in 3D. Genome reconstruction by translating a Hi-C matrix into a distance matrix, then multidimensional scaling. Uses binary contact maps.


  • THUNDER - cell-type deconvolution of Hi-C data. NMF, uses informative interactions within and between chromosomes (top 1000 features by Fano factor), reformatted into matrix form by concatenation. Needs number of cell types k. Tested on in silico mixture of cell types. Outperforms TOAST, CIBERSORT. R implementation.


Methodological Reviews

General Reviews

  • Bouwman, Britta A. M., and Wouter de Laat. “Getting the Genome in Shape: The Formation of Loops, Domains and Compartments.” Genome Biology 16 (August 10, 2015) - TAD/loop formation review. Convergent CTCF, cohesin, mediator, different scenarios of loop formation. Stability and dynamics of TADs. Rich source of references.

  • Chakraborty, Abhijit, and Ferhat Ay. “The Role of 3D Genome Organization in Disease: From Compartments to Single Nucleotides.” Seminars in Cell & Developmental Biology 90 (June 2019): 104–13. - 3D genome structure and disease. Evolution of technologies from FISH to variants of chromatin conformation capture. Hierarchical 3D organization, Table 1 summarizes each layer and its involvement in disease. Rearrangement of TADs/loops in cancer and other diseases. Specific examples of the biological importance of TADs, loops as means of distal communication.

  • Zheng, H., and Xie, W. (2019). "The role of 3D genome organization in development and cell differentiation." Nat. Rev. Mol. Cell Biol. - 3D structure of the genome and its changes during gametogenesis, embryonic development, lineage commitment, differentiation. Changes in developmental disorders and diseases. Chromatin compartments and TADs. Chromatin changes during X chromosome inactivation. Promoter-enhancer interactions established during development are accompanied by gene expression changes. Polycomb-mediated interactions may repress developmental genes. References to many studies.

  • Yu, Miao, and Bing Ren. “The Three-Dimensional Organization of Mammalian Genomes.” Annual Review of Cell and Developmental Biology 33 (06 2017) - 3D genome structure review. The role of gene promoters, enhancers, and insulators in regulating gene expression. Imaging-based tools, all flavors of chromatin conformation capture technologies. 3D features - chromosome territories, topologically associated domains (TADs), the association of TAD boundaries with replication domains, CTCF binding, transcriptional activity, housekeeping genes, genome reorganization during mitosis. Use of 3D data to annotate noncoding GWAS SNPs. 3D genome structure change in disease.

  • Fraser, J., C. Ferrai, A. M. Chiariello, M. Schueler, T. Rito, G. Laudanno, M. Barbieri, et al. “Hierarchical Folding and Reorganization of Chromosomes Are Linked to Transcriptional Changes in Cellular Differentiation.” Molecular Systems Biology 11, no. 12 (December 23, 2015) - 3D genome organization parts. Well-written and detailed. References. Technologies: FISH, 3C. 4C, 5C, Hi-C, GCC, TCC, ChIA-PET. Typical resolution - 40bp to 1Mb. LADs - conserved, but some are cell type-specific. Chromosome territories. Cell type-specific. Inter-chromosomal interactions may be important to define cell-specific interactions. A/B compartments identified by PCA. Chromatin loops, marked by CTCF and Cohesin binding, sometimes, with Mediator. Transcription factories

  • Dekker, Job, Marc A. Marti-Renom, and Leonid A. Mirny. “Exploring the Three-Dimensional Organization of Genomes: Interpreting Chromatin Interaction Data.” Nature Reviews. Genetics 14, no. 6 (June 2013) - 3D genome review. Chromosomal territories, transcription factories. Details of each 3C technology. Exponential decay of interaction frequencies. Box 2: A/B compartments (several Mb), TAD definition, size (hundreds of kb). TADs are largely stable, A/B compartments are tissue-specific. Adjacent TADs are not necessarily of opposing signs, may jointly form A/B compartments. Genes co-expression, enhancer-promoters interactions are confined to TADs. 3D modeling.

  • Witten, Daniela M., and William Stafford Noble. “On the Assessment of Statistical Significance of Three-Dimensional Colocalization of Sets of Genomic Elements.” Nucleic Acids Research 40, no. 9 (May 2012)


  • Chromatin conformation capture technologies, from 3C to imaging. 3D structures (nucleolus, nuclear speckles, polycomb bodies, chromosome territories, A/B compartments, TADs, loops), their roles in gene expression (sometimes, conflicting), replication timing, DNA repair. Agreement (and disagreement) examples between 3C methods and FISH. Description of libation-based (3C - Hi-C) and ligation-free (GAM, SPRITE, DamC) technologies. Multiway interactions, primarily occur within TADs. TAD formation, loop extrusion mechanisms. Association between replication timing and A/B compartments. Effect of mechanical forces on chromosome folding.

  • Review of Hi-C, Capture-C, and Capture-C technologies, their computational preprocessing. Experimental protocols, similarities and differences, types of reads (figures), details of alignment, read orientation, elimination of artifacts, quality metrics. A brief overview of preprocessing tools. Example preprocessing of three types of data. Java tool for preprocessing all types of data, Diachromatic (Differential Analysis of Chromatin Interactions by Capture), GOPHER (Generator Of Probes for capture Hi-C Experiments at high Resolution) for genome cutting, probe design

  • Chromosome conformation capture technologies, 4C, 5C, Hi-C, ChIP-loop, ChIA-PET. From microscopy observations (constrained movement of genomic loci, LADs, preferential stability of chromosome conformation and its independence from transcription), to technology details (Figure 1). Examples of alpha- and beta-globin locus studies by different technologies, X chromosome inactivation, HOXA-d gene clusters. The future vision of single-cell, single-allele investigation of chromatin interactions.

  • Review of technologies for studying the 3D structure of the genome. From microscopy to 3C techniques revealing CTCF and cohesin as the key proteins for establishing chromatin loops.TADs are unlikely over large distances >>1Mb. Details of 3C, 4C, 5C, Hi-C, ChIP-PET, and other derivatives. A/B compartments and their subdivision. TADs, their conservation, ~35-50% still seem to change. CTCF (directionality of binding important) and cohesin. Diseases and the 3D genome, examples. Key steps in data analysis and interpretation, software, visualization. Hi-C data specifics - chimeric reads, mapping, data representation as fixed or enzyme-sized bins, normalization, detection of A/B compartments and TAD boundaries, significant interactions. Hi-C analysis tools: HiC-Pro, HiCUP, HOMER, Juicer. Tools for 3D modeling.

  • RADICL-seq - RNA And DNA Interacting Complexes Ligated and sequenced - RNA-chromatin interaction profiling in intact nuclei. Crosslinking, digesting withDNAse I, RNAse H treatment (reduces ribosomal contamination), a bridge adapter specifically ligating RNA and DNA, reverse crosslinking, conversion to double-stranded DNA, cutting to 25-27bp length with EcoP15I enzyme. RNA- and DNA tags mapped, quantified by distance. Improvement over other technologies (MARGI, GRID-seq, SPRITE) - less cells, less spurious interactions, more trans interactions (lncRNAs in particular), more long-distance interactions. Four technology advantages in Discussion. Analysis - accounts for coverage, one-sided binomial test to call significant interactions. Data - mESC, oli-neu (from mOPCs). Visualization as 25kb contact matrix. Several noncoding transcripts interact in trans. Integration with CAGE data, comparison with Hi-C, association with TAD boundaries, A/B compartments, repeat elements. Differential analysis, Cell-type-specific interactions. Code, data

  • scsHi-C - sister-chromatid-sensitive Hi-C to explore interactions between the sister chromatids. Distinguishing cis from trans sister contacts based on 4-thio-thymidine (4sT) labeling. Paired organization of sister chromatins in interphase and complete separation in mitosis. TADs that exhibit tight pairing are heterochromatin marked by H3K27me3. Chromatids are predominantly linked at TAD boundaries, within TADs - more flexible. Investigation of looping mechanism - NIPBL-depletion, Sororin degradation. Jupyter notebooks for each analysis

  • 4C technology, wet-lab protocol, and data analysis and visualization. R-based processing pipeline pipe4C, configuration parameters

  • SisterC Hi-C technology to test interactions between sister chromatids. Uses BrdU incorporation in S-phase and single-strand degradation by UV/Hoechst treatment to obtain inter-sister or intra-sister interactions. Findings about the alignment of chromatids, strong at centromeres, looser (~35kb spaced) interactions along arms, loops up to 50kb. Tested on the S. cerevisiae genome. distiller-nf, pairtools, cooltools for processing.

  • Pore-C chromatin conformation capture using Oxford Nanopore Technologies, direct sequencing of multi-way chromatin contacts without amplification (concatemers, HOLR - high-order and long-range contacts). >18 times higher efficiency as compared with SPRITE, enrichment in concatemers. Tested on the Gm12878 cell line. Hi-C matrix from Pore-C well resembles published data. Concatemers show significantly lower distance decay. Concatemers better resolve complex cancer rearrangements, well-suited for de novo genome scaffolding. Pore-C tools and a Snakemake pipeline, detection of multi-way interactions

  • Tiled-C low-input 3C technology, requiring >20,000 cells. Applied for in vivo mouse erythroid differentiation, alpha-globin genes. TADs are pre-existing, regulatory interactions gradually form during differentiation. Integration with scRNA-seq data (CITE-seq technology) and ATAC-seq data. Analyzed using CCseqBasic pipeline and TiledC. Data (Tiled-C, CITE-seq, ATAC-seq)

  • Red-C (RNA Ends on DNA Capture) technology - proximity ligation-based technology to understand RNA-DNA interactome (3' and 5' ends of the RNA molecule associated with a given DNA site). Technology description(Methods, Results, Figure 1). Assesses all coding and noncoding RNA classes (piRNAs, tRNAs, rRNAs, snRNAs, scRNAs, srpRNAs, vlinc, antisense RNAs). Applied to K562 cells. The RNA-DNA matrices show a wide distribution of RNA contacts along extended genomic regions. The highest number of contacts was observed for mRNA, linc and vlinc RNAs. Enhancer RNAs (eRNAs) produced from strong enhancers interact with other strong enhancers on the same chromosome.  MIR3648 and MIR3687 interact with repressed chromatin genome-wide. Investigation of mRNA interaction preference across gene body. Discovery of novel chromatin-interacting RNAs (X-RNAs). Processed Red-C and RNA-seq data, GitHub, Nextflow processing pipeline

  • ChIA-Drop technology - multiplex chromatin interaction analysis via droplet-based and barcode-linked sequencing. 10X genomics technology, instead of cells, each gel-bead-in-emulsion contains crosslinked (but not ligated) chromatin and unique barcode. Singleton reads removed, distance constraints further refine multi-way interactions. FISH agrees with ChIA-Drop. Only approx. 20% of promoters are regulated by multiple enhancers, in Drosophila S2 cells. Data. Supplementary - notes about data processing, overview of ChIA-Dropbox software for visualization, Python implementation. Drosophila S2 cell line ChIA-Drop data, Hi-C

  • Dip-C technology for single-cell Hi-C, multiplex end-tagging amplification (META). Detects approx. 5 times more contacts. Possible to make haplotype-separated Hi-C maps, detect CNVs, resolve X-chromosome inactivation. Approx. 10kb-resolution Hi-C matrices, 3D genome reconstruction at 20kb resolution. PCA on chromatin compartments separates cell types. Comparison with Nagano data, bulk Gm12878 Hi-C. dip-c - Tools to analyze Dip-C (or other 3C/Hi-C) data, hickit. Processed data: Gm12878, 17 cells, PBMC, 18 cells

  • Methyl-HiC technology, in situ Hi-C and WGBS. Comparable Hi-C matrices, TADs. 20% fewer CpGs overall, more CpGs in open chromatin. Proximal CpGs correlate irrespectively of loop anchors, weaker for inter-chromosomal interactions. Application to single-cell, mouse ESCs under different conditions. Relevant clustering, cluster-specific genes. Methods for wet-lab and computational processing. Bulk (replicates) and single-cell Methyl-HiC data. Scripts in, Bhmem pipeline to map bisulfite-converted reads, Juicer pipeline for processing, VC normalization, HiCRep at 1Mb matrix similarity.

  • Review of chromatin conformation capture technologies, from image-based methods (FISH), through proximity ligation (3/4/5C, Hi-C, TCC, ChIA-PET, scHi-C), to ligation-free methods (GAM, SPRITE, ChIA-Drop). Details of each technology (Table 1, Figures), comparison of them (Table 2).

  • Optimization steps for Hi-C wet-lab protocol. Pitfalls and their effect on the downstream quality. Recommendations for each step.

  • DLO Hi-C technology (digestion-ligation-only Hi-C). Uses two rounds of digestion and ligation, without biotin and pull-down. Allows for early evaluation of Hi-C quality. Cost-effective, high signal-to-noise-ratio. Tested on THP-1 (human monocytes) and K562 cells. Data processed with ChIA-PET Tool, normalized with ICE

  • HIPMap - high-throughput imaging and analysis pipeline to map the location of gene loci within the 3D space. FISH in a 384-well plate format, automated imaging.

  • HiC method description, 1Mb, Gm06990. Small chromosomes, but 18, interact. Compartment A associated with open chromatin. 1Mb, 100kb resolution

  • 3C technology, the matrix of interaction frequencies, application to reveal spatial information, applied to yeast (S cerevisiae) genome. Interphase and metaphase chromosomes show different patterns of interactions. Distance-dependent decay of interaction frequencies. Basic observations on chromosome size, inter-chromosomal interactions.

    • Dekker, Job, Karsten Rippe, Martijn Dekker, and Nancy Kleckner. “Capturing Chromosome Conformation.” Science (New York, N.Y.) 295, no. 5558 (February 15, 2002)


  • Ultra-deep Micro-C maps of human embryonic stem cells and fibroblasts. Compared with in situ Hi-C (DpnII 4bp cutter), Micro-C allows for the detection of ~20,000 additional loops, improved signal-to-noise ratio. Similar distance-dependent decay, recovery of A/B compartments, better recovery of close-range interactions. High-resolution interaction boundaries are not created equal - most are CTCF+ and YY1+, some are CTCF- and YY1+, CTCF- and YY1-, and completely negative boundaries. Multiple, weak pause sites of SMC complexes. Distiller-nf processing, ICE normalization, other Mirnylab tools. Data at

  • Micro-C (MNase digestion Hi-C) data analysis. Nucleosome-level (~100-200bp) resolution Hi-C, captures all structures in regular Hi-C data. Stripes/flames structures correspond to enhancer-promoter interactions, colocalize with PolII, CTCF.  TADs are further split into "micro TADs", insulation score. Active boundaries colocalize with CpG islands, promoters, tRNA genes. Inactive boundaries are in repeats. Micro TADs subdivided into five subgroups. Two-start zig-zag model tetra-nucleosome stacks. Mouse stem cell, 38 biological replicates. Twitter

  • Micro-C (MNase digestion Hi-C) technology and basic analysis. Human embryonic stem cells H1-ESC and differentiated human foreskin fibroblasts (HFFc6). Captures standard Hi-C features, with many additional interaction peaks ("dots"). Enrichment of classical marks of TAD boundaries (Fig 3C) - RAD21, TAF1, PHF8, CTCF, TBP, POL2RA, YY1, and more.

  • Micro-C technology - mononucleosome resolution mapping in yeast. Micrococcal nuclease to fragment chromatin. Yeast does not have TADs, but Micro-C revealed self-associating domains (chromatin interaction domains, CIDs) driven by the number of genes. Enrichment of histone modifications. Data

Multi-way interactions

  • Multi-contact 3C (MC-3C, based on conventional 3C, C-walk, and multi-contact 4C approaches) technology reveals distinct chromosome territories with very little mixing, never entanglement, same with chromosomal compartment domains (A-A, B-B interactions predominant, A-B - minimal to none). Analysis of C-walks - connected paths of pairwise interactions. Compared with C-walks generated from Hi-C data. Permutation analysis of the significance of insulated, mixed, and intermediate domains. PacBio sequencing, processing with SMRT Analysis package and a custom pipeline

  • MC-4C multi-way contacts technology and computational protocols. ~2 weeks, ~$600/sample, best for <120kb regions. Computational protocol, test data included

  • MC-4C - Multi-way interactions technology, uses Nanopore MinION (or, PacBio) sequencing. Cross-linking, cutting with four-cutter and six-cutter enzymes, circularization, cutting with Cas9 gRNA designed to the viewpoint region, selective amplification of concatemers with primers specific to the viewpoint. Rigorous filtering strategy, interactions are allowing to distinguish reads coming from individual alleles. Compared with genome-wide multi-contact technologies C-walks, SPRITE, GAM, Tri-C. Applied to mouse beta-globin (fetal liver where hemoglobin genes are expressed, and brain, where they are silent) and protocadherin-alpha (same tissues, vice versa ) loci. Super enhancers can form hubs, target multiple genes. WAPL deletion in HAP1 (leukemia) cells stimulates the collision of CTCF-anchored domain loops to form rosette-like structures. MC-4C processing pipeline, Visualization of the analyzed data, raw data, processed data matrices

  • Multiplex-GAM - Genome Architecture Mapping technology. Multiple nuclear profiles (2-3 NPs, slices of nuclei) are sequenced together. Updates of the SLICE method for GAM data analysis. Details on the dependencies between the number of GAM samples, probability to detect interactions, the number of NPs per sample, technical parameters. TAD boundaries match those from Hi-C. Method-specific contacts exist, but GAM contacts are enriched in CTCF-enhancer contacts and other active transcription annotations, in contrast to heterochromatin-prone Hi-C contacts. No data yet

  • GAM (genome architecture mapping) - restriction- and ligation free chromatin conformation capture technology. Isolates and sequences DNA content of many ultra-thin (~0.22um) cryo-fixes nuclear slices, whole-genome amplification.~30kb matrix is built on frequencies of co-occurrences of regions in multiple slices. Applied to mouse ESCs. Normalization using linkage disequilibrium (better than ICE). GAM and Hi-C matrices are highly correlated, A/B compartments, TADs significantly overlap. Enhancers and active genes significantly interact. Multi-way interactions, triplets, super-enhancers are enriched in three-way interactions, confirmed by FISH. SLICE (Statistical Inference of co-segregation) mathematical model to assign significance of interactions (Supplementary Note 1). Negative binomial modeling of significant interactions, log-normal noise. Supplementary Table 2 - genomic coordinates of triplet (three-way interacting) TADs. Raw data, processed matrices, Python scripts

  • SPRITE (Split-Pool Recognition of Interactions by Tag Extension) - Multi-way interactions technology, barcode system based on repeated pooling ans splitting of crosslinked DNAhairballs to uncover clustered DNA fragments (SPRITE clusters). Does not involve proximity ligation. Transcriptionally active interaction hubs around duclear speckles, inactive - around nucleolus. These nuclear bodies constrain the 3D packaging. Applied to mouse embryonic stem cells (mESCs) and GM12878, data correlates well with Hi-C. Data: 25kb-1Mb inter- and intra-chromosomal interactions, Processing Pipeline

  • C-walks, multi-way technology, genome-wide. TADs organize chromosomal territories. Active and inactive TAD properties. Methods: Good mathematical description of insulation score calculations. Filter TADs smaller than 250kb. Inter-chromosomal contacts are rare, ~7-10%. Concatemers (more than two contacts) are unlikely.

  • TM3C - Tethered multiple 3C technology to probe multi-point contacts. NHEK, KBM7 cells, detected the Philadelphia chromosome, investigated triple contacts in the IGF2-H19 locus at 40kb, detected typical genomic structures (chromosomal compartments, distance-decay, TADs), reconstructed 3D genome at 1Mb resolution. A two-phase mapping strategy that separately maps chimeric subsequences within a single read (Methods). Multiple 4-cutter restriction enzymes

  • INGRID - chromatin conformation capture technology using in-gel replication of interacting DNA segments. Detects multi-way interactions. Protocol, demonstration of beta-globin gene locus.


  • Lyu, Hongqiang, Erhu Liu, and Zhifang Wu. “Comparison of Normalization Methods for Hi-C Data.” BioTechniques 68, no. 2 (2020) - a comprehensive analysis of six Hi-C normalization methods for their ability to remove systematic biases. The introduction provides a good classification and overview of different normalization methods, including the latest methods for cross-sample normalization, such as "multiHiCcompare." Human and mouse Hi-C data were used, only cis interaction matrices are considered. A systematic protocol for benchmarking is presented. Several benchmarks were performed, including statistical quality, the influence of resolution, consistency of distance-dependent changes in interaction frequency, reproducibility of the 3D architecture. multiHiCcompare is reported as outperforming other methods on a range of performance metrics.

  • Imakaev, Maxim, Geoffrey Fudenberg, Rachel Patton McCord, Natalia Naumova, Anton Goloborodko, Bryan R. Lajoie, Job Dekker, and Leonid A. Mirny. “Iterative Correction of Hi-C Data Reveals Hallmarks of Chromosome Organization.” Nature Methods 9, no. 10 (October 2012) - ICE - Iterative Correction and Eigenvalue decomposition, normalization of HiC data. Assumption - all loci should have equal visibility. Deconvolution into eigenvectors/values.

  • Yaffe, Eitan, and Amos Tanay. “Probabilistic Modeling of Hi-C Contact Maps Eliminates Systematic Biases to Characterize Global Chromosomal Architecture.” Nature Genetics 43, no. 11 (November 2011) - Sources of biases: 1) non-specific ligation (large distance between pairs); 2) length of each ligated fragments; 3) CG content and nucleotide composition; 4) Mappability. Normalization. Enrichment of long-range interactions in active promoters. General aggregation of active chromosomal domains. Chromosomal territories, high-activity and two low-activity genomic clusters

TAD detection

  • Brief description of 22 TAD calling methods. Source: Zufferey et al., “Comparison of Computational Methods for the Identification of Topologically Associating Domains.”

  • Dali, Rola, and Mathieu Blanchette. “A Critical Assessment of Topologically Associating Domain Prediction Tools.” Nucleic Acids Research 45, no. 6 (April 7, 2017) - TAD definition, tools. Meta-TADs, hierarchy, overlapping TADs. HiCPlotter for visualization. Manual annotation as a gold standard. Sequencing depth and resolution affect things. Code, manual annotations

  • Forcato, Mattia, Chiara Nicoletti, Koustav Pal, Carmen Maria Livi, Francesco Ferrari, and Silvio Bicciato. “Comparison of Computational Methods for Hi-C Data Analysis.” Nature Methods, June 12, 2017 - Hi-C processing and TAD calling tools benchmarking, Table 1, simulated (Lun and Smyth method) and real data. Notes about pluses and minuses of each tool. TAD reproducibility is higher than chromatin interactions, increases with a larger number of reads. Consistent enrichment of TAD boundaries in CTCF, irrespectively of TAD caller. Hi-C replication is poor, just a bit more than random. Supplementary table 2 - technical details about each program, Supplementary Note 1 - Hi-C preprocessing tools, Supplementary Note 2 - TAD callers. Supplementary note 3 - how to simulate Hi-C data. Supplementary note 6 - how to install tools. Tools for TAD comparison, and simulated matrices,

  • Olivares-Chauvet, Pedro, Zohar Mukamel, Aviezer Lifshitz, Omer Schwartzman, Noa Oded Elkayam, Yaniv Lubling, Gintaras Deikus, Robert P. Sebra, and Amos Tanay. “Capturing Pairwise and Multi-Way Chromosomal Conformations Using Chromosomal Walks.” Nature 540, no. 7632 (November 30, 2016) - TADs organize chromosomal territories. Active and inactive TAD properties. Methods: Good mathematical description of insulation score calculations. Filter TADs smaller than 250kb. Inter-chromosomal contacts are rare, ~7-10%. Concatemers (more than two contacts) are unlikely.

  • Zufferey, Marie, Daniele Tavernari, Elisa Oricchio, and Giovanni Ciriello. “Comparison of Computational Methods for the Identification of Topologically Associating Domains.” Genome Biology 19, no. 1 (10 2018) - Comparison of 22 TAD callers across different conditions. Callers are classified as linear score-based, statistical model-based, clustering, graph theory. Table 1 and Additional file 1 summarizes each caller. The effect of data resolution, normalization, hierarchy. Test on Rao 2014 data, chromosome 6. ICE or LGF (local genomic feature,) normalization. The measure of Concordance (MoC) to compare TADs. CTCF/cohesin as a measure of biological significance. TopDom, HiCseg, CaTCH, CHDF are the top performers. R scripts, including for calculation MoC,

  • Rocha, Pedro P., Ramya Raviram, Richard Bonneau, and Jane A. Skok. “Breaking TADs: Insights into Hierarchical Genome Organization.” Epigenomics 7, no. 4 (2015) - Textbook overview of TADs in 3 pages with key references. 3D organization discovery using FISH, 3C, Hi-C. Discovery of A/B compartments (euchromatin, heterochromatin), TADs as regulatory units conserved even across syntenic regions in different organisms. TADs coordinate gene expression. TAD boundaries are not created equal. Examples of changes of TAD boundaries (Hox gene cluster, ES differentiation). Hierarchy of TADs.

  • Crane, Emily, Qian Bian, Rachel Patton McCord, Bryan R. Lajoie, Bayly S. Wheeler, Edward J. Ralston, Satoru Uzawa, Job Dekker, and Barbara J. Meyer. “Condensin-Driven Remodelling of X Chromosome Topology during Dosage Compensation.” Nature 523, no. 7559 (July 9, 2015). - Insulation Score to define TADs - sliding square along the diagonal, aggregating signal within it. This aggregated score is normalized and binned into TADs, boundaries. See Methods and implementation., Parameters: -is 480000 -ids 320000 -im iqrMean -nt 0 -ss 160000 -yb 1.5 -nt 0 -bmoe 0.

  • "Hierarchical Regulatory Domain Inference from Hi-C Data" - presentation by Bartek Wilczyński about TAD detection, existing algorithms, new SHERPA and OPPA methods. Video, PDF, Web site, GitHub - SHERPA and OPPA code there.

Spectral clustering

  • Y. X Rachel Wang, Purnamrita Sarkar, Oana Ursu, Anshul Kundaje and Peter J. Bickel, "Network modelling of topological domains using Hi-C data" - TAD analysis using graph-theoretical (network-based) methods. Treats TADs as a "community" within the network. Shows that naive spectral clustering is generally ineffective, leaving gaps in the data.

  • Liu, Sijia, Pin-Yu Chen, Alfred Hero, and Indika Rajapakse. “Dynamic Network Analysis of the 4D Nucleome.” BioRxiv, January 1, 2018. - Temporal Hi-C data analysis using graph theory. Integrated with RNA-seq data. Network-based approaches such as von Neumann graph entropy, network centrality, and multilayer network theory are applied to reveal universal patterns of the dynamic genome. Toeplitz normalization. Graph Laplacian matrix. Detailed statistics.

  • Norton, Heidi K, Harvey Huang, Daniel J Emerson, Jesi Kim, Shi Gu, Danielle S Bassett, and Jennifer E Phillips-Cremins. “Detecting Hierarchical 3-D Genome Domain Reconfiguration with Network Modularity,” November 22, 2016. - Graph theory for TAD identification. Louvain-like local greedy algorithm to maximize network modularity. Vary resolution parameter, hierarchical TAD identification. Hierarchical spatial variance minimization method. ROC analysis to quantify performance. Adjusted RAND score to quantify the TAD overlap.

  • Chen, Jie, Alfred O. Hero, and Indika Rajapakse. “Spectral Identification of Topological Domains.” Bioinformatics (Oxford, England) 32, no. 14 (15 2016) - Spectral algorithm to define TADs. Laplacian graph segmentation using Fiedler vector iteratively. Toeplitz normalization to remove distance effect. Spectral TADs do not overlap with Dixon's, but better overlap with CTCF. Python implementation



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