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A collection of tools for Hi-C data analysis

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Hi-C data analysis tools and papers

Tools are sorted by publication date, newest on top. Unpublished tools are listed at the end of each section. Related repositories: HiC_data, scHiC_notes. Please, open an issue or a pull request to add other information, tools, or user experience.

Table of content

Pipelines

Capture-C

HiChIP

4C

Resolution improvement

Simulation

  • 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 HiCcompare, multiHiCcompare, diffHiC, and Selfish. 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: multiHiCcompare, HiCcompare, diffHiC, Selfish.

  • 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 https://zenodo.org/record/3345896. Python3 implementation https://github.com/keleslab/FreeHiC

Normalization

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, https://github.com/hiclib/iced

  • 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. https://bitbucket.org/mthjwu/hicapp

  • 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. https://github.com/qenvio/dryhic

Reproducibility

Loop callers

Capture-C peaks

Differential interactions

TAD callers

  • 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. https://github.com/princeps091-binf/BHi-Cect

  • 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 https://github.com/bioinfo-lab/TADBD/

  • 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). https://github.com/3DGenomes/TADpole

  • 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. https://github.com/ChenFengling/RHiCDB

  • OnTAD - hierarchical TAD caller, Optimal Nested TAD caller. Sliding window, adaptive local minimum search algorithm, similar to TOPDOM. C++ implementation. https://github.com/anlin00007/OnTAD. OnTAD for coolers - a Python wrapper to work with .cool files.

    • 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 https://bitbucket.org/creminslab/3dnetmod_method_v1.0_10_06_17

    • 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. https://doi.org/10.1038/nmeth.4560.

  • 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) https://github.com/yinxc/structural-information-minimisation

    • 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. https://doi.org/10.1038/s41467-018-05691-7.

  • 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. https://github.com/zhanyinx/CaTCH_R

  • 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. https://github.com/XiaoTaoWang/TADLib

  • IC-Finder - Segmentations of HiC maps into hierarchical interaction compartments, http://membres-timc.imag.fr/Daniel.Jost/DJ-TIMC/Software.html

  • ClusterTAD - A clustering method for identifying topologically associated domains (TADs) from Hi-C data, https://github.com/BDM-Lab/ClusterTAD

  • 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. https://github.com/ucrbioinfo/EAST

  • TADtree - Hierarchical (nested) TAD identification. Two ways of TAD definition: 1D and 2D. Normalization by distance. Enrichment over the background. http://compbio.cs.brown.edu/software/

  • 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. http://zhoulab.usc.edu/TopDom/

  • TADtool - wrapper for directionality index and insulation score TAD callers. https://github.com/vaquerizaslab/tadtool

  • 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 https://zenodo.org/record/49767, and Arboretum-HiC https://bitbucket.org/roygroup/arboretum-hic

  • Armatus - TAD detection at different resolutions, Dynamic programming method. https://github.com/kingsfordgroup/armatus

  • 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): nb_change_max = 500, distrib = 'G' and model = 'Dplus'. https://cran.r-project.org/web/packages/HiCseg/index.html

  • domaincaller - A Python implementation of the original DI domain caller, https://github.com/XiaoTaoWang/domaincaller

Differential TAD analysis

Prediction of 3D features

SNP-oriented

  • 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. http://iregnet3d.yulab.org/index/

  • 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. http://cbportal.org/3dsnp/

  • 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. http://yunliweb.its.unc.edu/HUGIn/

    • 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

Visualization

De novo genome scaffolding

3D modeling

Papers

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)

Technology

  • 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.

  • 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 https://github.com/gerlichlab/scshic_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)

  • 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 https://bitbucket.org/dnaase/bisulfitehic/src/master/, 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)

Micro-C

  • 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 https://data.4dnucleome.org/

  • 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

  • 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

  • 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.

Normalization

  • 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

Hi-C prediction

  • 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. https://github.com/Roy-lab/HiC-Reg

  • 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

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 https://github.com/shappiron/TAD-Laplacian-Identification

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A collection of tools for Hi-C data analysis

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