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hic_tools's Introduction

A collection of tools and papers related to Hi-C data analysis

Slowly growing as notes from my Zotero collection are getting organized. A related repository holds references to Hi-C data, https://github.com/mdozmorov/HiC_data. Issues and/or Pull requests to add other data are welcome!

Table of content

Pipelines

  • A list of available pipelines, URLs. pipelines_list.csv, Source

  • Available analysis options in each pipeline. pipeline_comparison.csv, Source

  • cooler file format for storing Hi-C matrices, sparse, hierarchical, multi-resolution. cooler Python package for data loading, aggregation, merging, normalization (balancing), viewing, exporting data. Together with "pairs" text-based format (https://github.com/4dn-dcic/pairix/blob/master/pairs_format_specification.md), and hic, cooler is accepted by the 4D Nucleome consortium DAC.https://github.com/mirnylab/cooler,https://cooler.readthedocs.io/en/latest/

    • Abdennur, Nezar, and Leonid Mirny. “Cooler: Scalable Storage for Hi-C Data and Other Genomically-Labeled Arrays.” BioRxiv, February 22, 2019. https://doi.org/10.1101/557660.

  • distiller-nf - Java modular Hi-C mapping pipeline for reproducible data analysis, nextflow pipeline. Alignment, filtering, aggregating Hi-C matrices. https://github.com/mirnylab/distiller-nf

  • Juicer - Java full pipeline to convert raw reads into Hi-C maps, visualized in Juicebox. Call domains, loops, CTCF binding sites. .hic file format for storing multi-resolution Hi-C data. https://github.com/theaidenlab/juicebox/wiki/Download

    • 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): 95–98. https://doi.org/10.1016/j.cels.2016.07.002.
    • 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): 1665–80. https://doi.org/10.1016/j.cell.2014.11.021. - 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.

  • HiCExplorer - set of Python scripts to process, normalize, analyze and visualize Hi-C data, Python. https://hicexplorer.readthedocs.io/en/latest/, https://github.com/deeptools/HiCExplorer/

  • hiclib - Python tools to qc, map, normalize, filter and analyze Hi-C data, https://bitbucket.org/mirnylab/hiclib

  • HiC-bench - complete pipeline for Hi-C data analysis. https://github.com/NYU-BFX/hic-bench

    • Lazaris, Charalampos, Stephen Kelly, Panagiotis Ntziachristos, Iannis Aifantis, and Aristotelis Tsirigos. “HiC-Bench: Comprehensive and Reproducible Hi-C Data Analysis Designed for Parameter Exploration and Benchmarking.” BMC Genomics 18, no. 1 (December 2017). https://doi.org/10.1186/s12864-016-3387-6.

  • HiC-Pro - Python and command line-based optimized and flexible pipeline for Hi-C data processing, https://github.com/nservant/HiC-Pro

    • 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): 259. https://doi.org/10.1186/s13059-015-0831-x. - 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.

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

  • HiCUP - Perl-based pipeline, alignment only, output - BAM files. http://www.bioinformatics.babraham.ac.uk/projects/hicup/

    • 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): 1310. https://doi.org/10.12688/f1000research.7334.1. - HiCUP pipeline, alignment only, removes artifacts (religations, duplicate reads) creating BAM files. Details about Hi-C sequencing artefacts. Used in conjunction with other pipelines.

  • HiFi - Python/C++ tool for extracting restriction fragment resolution Hi-C data. https://github.com/BlanchetteLab/HIFI

  • HiTC - R package for High Throughput Chromosome Conformation Capture analysis, https://bioconductor.org/packages/release/bioc/html/HiTC.html

  • my5C- web-based tools, well-documented analysis and visualization of 5S data, http://my5c.umassmed.edu/

  • TADbit - Python-based pipeline, from iterative mapping, filtering, normalization. Similarity metrics: distance-centric Spearman, first principal eigenvector. TAD detection. TAD boundaries alignment, within 20kb. 3D modeling. Supplementary material - key functions, TAD detection algorithm, boundary comparison. https://github.com/3DGenomes/tadbit

    • Serra, François, Davide Baù, Mike Goodstadt, David Castillo, Guillaume J. Filion, and Marc A. Marti-Renom. “Automatic Analysis and 3D-Modelling of Hi-C Data Using TADbit Reveals Structural Features of the Fly Chromatin Colors.” PLoS Computational Biology 13, no. 7 (July 2017): e1005665. https://doi.org/10.1371/journal.pcbi.1005665.

Single-cell Hi-C

  • nuc_processing - Chromatin contact paired-read single-cell Hi-C processing module for Nuc3D and NucTools. https://github.com/TheLaueLab/nuc_processing.
    • Stevens, Tim J., David Lando, Srinjan Basu, Liam P. Atkinson, Yang Cao, Steven F. Lee, Martin Leeb, et al. “3D Structures of Individual Mammalian Genomes Studied by Single-Cell Hi-C.” Nature, March 13, 2017. https://doi.org/10.1038/nature21429.

Normalization

  • HiCNorm - removing biases in Hi-C data via Poisson regression, http://www.people.fas.harvard.edu/~junliu/HiCNorm/

    • Hu, Ming, Ke Deng, Siddarth Selvaraj, Zhaohui Qin, Bing Ren, and Jun S. Liu. “HiCNorm: Removing Biases in Hi-C Data via Poisson Regression.” Bioinformatics (Oxford, England) 28, no. 23 (December 1, 2012): 3131–33. https://doi.org/10.1093/bioinformatics/bts570. - Poisson normalization. Also tested negative binomial.

  • HiFive - handling and normalization or pre-aligned Hi-C and 5C data, https://www.taylorlab.org/software/hifive/

    • 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). https://doi.org/10.1186/s13059-015-0806-y. - 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 correlation between matrices.

  • HiTC - The HiTC R package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. https://bioconductor.org/packages/release/bioc/html/HiTC.html

    • Servant, Nicolas, Bryan R. Lajoie, Elphège P. Nora, Luca Giorgetti, Chong-Jian Chen, Edith Heard, Job Dekker, and Emmanuel Barillot. “HiTC: Exploration of High-Throughput ‘C’ Experiments.” Bioinformatics (Oxford, England) 28, no. 21 (November 1, 2012): 2843–44. https://doi.org/10.1093/bioinformatics/bts521. - HiTC paper. Processed data import from TXT/BED into GRanges. Quality control, visualization. Normalization using loess regression on genomic distance, 45-degree rotation and visualization of triangle TADs. Adding annotation at the bottom. PCA to detect A/B compartments.

  • ICE - Iterative Correction and Eigenvalue decomposition, normalization of HiC data.

CNV-aware normalization

  • 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 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

    • Wu, Hua-Jun, and Franziska Michor. “A Computational Strategy to Adjust for Copy Number in Tumor Hi-C Data.” Bioinformatics (Oxford, England) 32, no. 24 (December 15, 2016): 3695–3701. https://doi.org/10.1093/bioinformatics/btw540.

  • OneD - CNV bias-correction method, addresses the problem of partial aneuploidy. Bin-centric counts are modeled using negative binomial distribution, and its parameters are estimated using splines. A hidden Markov model is fit to infer copy number for each bin. Each Hi-C matrix entry is corrected by dividing its value by 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

    • Vidal, Enrique, François le Dily, Javier Quilez, Ralph Stadhouders, Yasmina Cuartero, Thomas Graf, Marc A Marti-Renom, Miguel Beato, and Guillaume J Filion. “OneD: Increasing Reproducibility of Hi-C Samples with Abnormal Karyotypes.” Nucleic Acids Research, January 31, 2018. https://doi.org/10.1093/nar/gky064.

Reproducibility

  • 3DChromatin_ReplicateQC - Comparison of four Hi-C reproducibility assessment tools, HiCRep, GenomeDISCO, HiC-Spector, QuASAR-Rep. Tested the effects of noise, sparsity, resolution. Spearman doesn't work well. All tools performed similarly, worsening expectedly. QuASAR has QC tool measuring the level of noise. https://github.com/kundajelab/3DChromatin_ReplicateQC

    • Yardimci, Galip, Hakan Ozadam, Michael E.G. Sauria, Oana Ursu, Koon-Kiu Yan, Tao Yang, Abhijit Chakraborty, et al. “Measuring the Reproducibility and Quality of Hi-C Data,” September 14, 2017. doi:10.1101/188755.

  • HiC-Spector - reproducibility metric to quantify the similarity between contact maps using spectral decomposition. Decomposing Laplacian matrices and sum the Euclidean distance between eigenvectors. https://github.com/gersteinlab/HiC-spector

    • Yan, Koon-Kiu, Galip Gürkan Yardimci, Chengfei Yan, William S. Noble, and Mark Gerstein. “HiC-Spector: A Matrix Library for Spectral and Reproducibility Analysis of Hi-C Contact Maps.” Bioinformatics (Oxford, England) 33, no. 14 (July 15, 2017): 2199–2201. https://doi.org/10.1093/bioinformatics/btx152.

  • localtadsim - Analysis of TAD similarity using 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. https://github.com/Kingsford-Group/localtadsim

    • Sauerwald, Natalie, and Carl Kingsford. “Quantifying the Similarity of Topological Domains across Normal and Cancer Human Cell Types.” Bioinformatics (Oxford, England) 34, no. 13 (July 1, 2018): i475–83. https://doi.org/10.1093/bioinformatics/bty265.

Significant interaction (peak) callers

  • 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. https://bioconductor.org/packages/release/bioc/html/Chicago.html

    • Cairns, Jonathan, Paula Freire-Pritchett, Steven W. Wingett, Csilla Várnai, Andrew Dimond, Vincent Plagnol, Daniel Zerbino, et al. “CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C Data.” Genome Biology 17, no. 1 (2016): 127. https://doi.org/10.1186/s13059-016-0992-2.

  • 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. https://github.com/yousra291987/ChiCMaxima

    • Ben Zouari, Yousra, Anne M Molitor, Natalia Sikorska, Vera Pancaldi, and Tom Sexton. “ChiCMaxima: A Robust and Simple Pipeline for Detection and Visualization of Chromatin Looping in Capture Hi-C,” October 16, 2018. https://doi.org/10.1101/445023.

  • Fit-Hi-C - Python tool for detection of significant chromatin interactions, https://noble.gs.washington.edu/proj/fit-hi-c/

    • 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): 999–1011. https://doi.org/10.1101/gr.160374.113. - Fit-Hi-C method, Splines to model distance dependence. Model mid-range interaction frequencies, decay with distance. Biases, methods for normalization. Two-step splines - use all dots for 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 http://www.replicationdomain.com/. Validation Hi-C data. http://chromosome.sdsc.edu/mouse/hi-c/download.html

  • GoTHIC - R package for peak calling in individual HiC datasets, while accounting for noise. https://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html

    • 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): e0174744. https://doi.org/10.1371/journal.pone.0174744. - The GOTHiC (genome organisation through HiC) algorithm uses a simple binomial distribution model to simultaneously remove coveralge-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.

  • 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. http://homer.ucsd.edu/homer/interactions/

  • HiCapTools - Software package that can design sequence capture probes for targeted chromosome capture applications and analyse 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. https://github.com/sahlenlab/HiCapTools

    • Anandashankar Anil, Rapolas Spalinskas, Örjan Åkerborg, Pelin Sahlén; HiCapTools: a software suite for probe design and proximity detection for targeted chromosome conformation capture applications, Bioinformatics, Volume 34, Issue 4, 15 February 2018, Pages 675–677, https://doi.org/10.1093/bioinformatics/btx625

Differential interactions

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

    • Cairns, Jonathan, William R. Orchard, Valeriya Malysheva, and Mikhail Spivakov. “Chicdiff: A Computational Pipeline for Detecting Differential Chromosomal Interactions in Capture Hi-C Data.” BioRxiv, January 1, 2019, 526269. https://doi.org/10.1101/526269.

  • diffHiC - Differential contacts using the full pipeline for Hi-C data. Explanation of the technology, binning. MA normalization, edgeR-based. Comparison with HOMER. https://bioconductor.org/packages/release/bioc/html/diffHic.html

    • Lun, Aaron T. L., and Gordon K. Smyth. “DiffHic: A Bioconductor Package to Detect Differential Genomic Interactions in Hi-C Data.” BMC Bioinformatics 16 (2015): 258. https://doi.org/10.1186/s12859-015-0683-0.

  • diffloop - Differential analysis of chromatin loops (ChIA-PET). edgeR framework. https://bioconductor.org/packages/release/bioc/html/diffloop.html

    • Lareau, Caleb A., and Martin J. Aryee. “Diffloop: A Computational Framework for Identifying and Analyzing Differential DNA Loops from Sequencing Data.” Bioinformatics (Oxford, England), September 29, 2017. https://doi.org/10.1093/bioinformatics/btx623.

  • DiffTAD - differential contact frequency in TADs between two conditions. Two tests - 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. https://bitbucket.org/rzaborowski/differential-analysis

    • Zaborowski, Rafal, and Bartek Wilczynski. “DiffTAD: Detecting Differential Contact Frequency in Topologically Associating Domains Hi-C Experiments between Conditions.” BioRxiv, January 1, 2016, 093625. https://doi.org/10.1101/093625.

  • 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 both 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 paclage https://bitbucket.org/nadhir/find/downloads/

    • Djekidel, Mohamed Nadhir, Yang Chen, and Michael Q. Zhang. “FIND: DifFerential Chromatin INteractions Detection Using a Spatial Poisson Process.” Genome Research, February 12, 2018. https://doi.org/10.1101/gr.212241.116.

  • 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. http://bioconductor.org/packages/release/bioc/html/HiCcompare.html

    • Stansfield, John C., Kellen G. Cresswell, Vladimir I. Vladimirov, and Mikhail G. Dozmorov. “HiCcompare: An R-Package for Joint Normalization and Comparison of HI-C Datasets.” BMC Bioinformatics 19, no. 1 (December 2018). https://doi.org/10.1186/s12859-018-2288-x.

  • 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. http://bioconductor.org/packages/release/bioc/html/multiHiCcompare.html

    • Stansfield, John C, Kellen G Cresswell, and Mikhail G Dozmorov. “MultiHiCcompare: Joint Normalization and Comparative Analysis of Complex Hi-C Experiments.” Edited by Inanc Birol. Bioinformatics, January 22, 2019. https://doi.org/10.1093/bioinformatics/btz048.

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

    • Roayaei Ardakany, Abbas, Ferhat Ay, and Stefano Lonardi. “Selfish: Discovery of Differential Chromatin Interactions via a Self-Similarity Measure.” BioRxiv, January 1, 2019, 540708. https://doi.org/10.1101/540708.

TAD callers

  • 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 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.

  • Armatus - TAD detection at different resolutions, https://www.cs.cmu.edu/~ckingsf/software/armatus/, https://github.com/kingsfordgroup/armatus

  • CaTCH - identification of hierarchical TAD structure, https://github.com/zhanyinx/CaTCH_R

    • Zhan, Yinxiu, Luca Mariani, Iros Barozzi, Edda G. Schulz, Nils Blüthgen, Michael Stadler, Guido Tiana, and Luca Giorgetti. “Reciprocal Insulation Analysis of Hi-C Data Shows That TADs Represent a Functionally but Not Structurally Privileged Scale in the Hierarchical Folding of Chromosomes.” Genome Research 27, no. 3 (2017): 479–90. https://doi.org/10.1101/gr.212803.116. - 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

  • 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 background. Outperforms diffHiC, Fit-Hi-C, GOTHiC, HiCCUPS, HOMER. https://github.com/YaqiangCao/cLoops

    • Cao, Yaqiang, Xingwei Chen, Daosheng Ai, Zhaoxiong Chen, Guoyu Chen, Joseph McDermott, Yi Huang, and Jing-Dong J. Han. “Accurate Loop Calling for 3D Genomic Data with CLoops,” November 8, 2018. https://doi.org/10.1101/465849.

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

    • Abbas Roayaei Ardakany, Stefano Lonardi, and Marc Herbstritt, “Efficient and Accurate Detection of Topologically Associating Domains from Contact Maps” (Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany, 2017), https://doi.org/10.4230/LIPIcs.WABI.2017.22. - 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 contigious non-overlapping domains maximizing the function. Restricted by maximum length of TADs Text boundaries for enrichment in CTCF, RNP PolII, H3K4me3, H3K27ac. https://github.com/ucrbioinfo/EAST

  • 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 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

    • Chen, Fengling, Guipeng Li, Michael Q. Zhang, and Yang Chen. “HiCDB: A Sensitive and Robust Method for Detecting Contact Domain Boundaries.” Nucleic Acids Research 46, no. 21 (November 30, 2018): 11239–50. https://doi.org/10.1093/nar/gky789.

  • hickit - TAD calling, phase imputation, 3D modeling and more for diploid single-cell Hi-C (Dip-C) and bulk Hi-C, https://github.com/lh3/hickit

  • 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, https://pypi.python.org/pypi/TADLib

    • Wang, Xiao-Tao, Wang Cui, and Cheng Peng. “HiTAD: Detecting the Structural and Functional Hierarchies of Topologically Associating Domains from Chromatin Interactions.” Nucleic Acids Research 45, no. 19 (November 2, 2017): e163. https://doi.org/10.1093/nar/gkx735.

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

    • Noelle Haddad, Cedric Vaillant, Daniel Jost. "IC-Finder: inferring robustly the hierarchical organization of chromatin folding." Nucleic Acids Res. 2017 Jun 2; 45(10). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5449546/. TAD-identification procedure that is essentially identical to my idea, including inferring hierarchical structure of TADs based on hierarchical clustering. Article does not explicity discuss A/B compartments so it may still be novel for compartment detection. - TAD finding by combined hierarchical clustering. Correlation-based distance, weighted-mean linkage similarity. Variance criterion to define cluster boundaries. http://membres-timc.imag.fr/Daniel.Jost/DJ-TIMC/Software.html

  • Insulation score: Giorgetti, Luca, Bryan R. Lajoie, Ava C. Carter, Mikael Attia, Ye Zhan, Jin Xu, Chong Jian Chen, et al. “Structural Organization of the Inactive X Chromosome in the Mouse.” Nature 535, no. 7613 (28 2016): 575–79. https://doi.org/10.1038/nature18589. - https://github.com/dekkerlab/cworld-dekker/tree/master/scripts/perl matrix2insulation.pl, Parameters: -is 480000 -ids 320000 -im iqrMean -nt 0 -ss 160000 -yb 1.5 -nt 0 -bmoe 0.

    • Used in Yardımcı, Galip Gürkan, Hakan Ozadam, Michael E.G. Sauria, Oana Ursu, Koon-Kiu Yan, Tao Yang, Abhijit Chakraborty, et al. “Measuring the Reproducibility and Quality of Hi-C Data.” BioRxiv, January 1, 2018. https://doi.org/10.1101/188755. - TADs detected by insulation score are robust to resolution and noise

  • HiCseg - TAD detection by maximization of likelihood based block-wise segmentation model, https://cran.r-project.org/web/packages/HiCseg/index.html

    • Lévy-Leduc, Celine, M. Delattre, T. Mary-Huard, and S. Robin. “Two-Dimensional Segmentation for Analyzing Hi-C Data.” Bioinformatics (Oxford, England) 30, no. 17 (September 1, 2014): i386-392. https://doi.org/10.1093/bioinformatics/btu443. - HiCseg paper. TAD detection by maximization of likelihood based block-wise segmentation model, HiCseg R package. 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, https://en.wikipedia.org/wiki/Hausdorff_distance. Parameters (from TopDom paper): nb_change_max = 500, distrib = 'G' and model = 'Dplus'.

  • OnTAD - hierarchical TAD caller, Optimal Nested TAD caller. Sliding window, adaptive local minimum search algorithm, similar to TOPDOM. https://github.com/anlin00007/OnTAD

    • 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. https://doi.org/10.1101/361147. - 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 proportion of variance in the contact frequencies explained by TAD moundaries. 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.

  • TADtree - TADtree is an algorithm the identification of hierarchical topological domains in Hi-C data, http://compbio.cs.brown.edu/software/

    • Weinreb, Caleb, and Benjamin J. Raphael. “Identification of Hierarchical Chromatin Domains.” Bioinformatics (Oxford, England) 32, no. 11 (June 1, 2016): 1601–9. https://doi.org/10.1093/bioinformatics/btv485. - TADtree paper. Hierarchical (nested) TAD identification. Two ways of TAD definition: 1D and 2D. Normalization by distance. Enrichment over background. Deep statistics of the method. How to compare TADs (VI measure (vi.dist in https://cran.r-project.org/web/packages/mcclust/mcclust.pdf), Precision/recall using Dixon as the true set, Fig. 5: number of TADs, TAD size boxplots, Enrichment within 50kb of a TAD boundary - CTCF, PolII, H3K4me3, housekeeping genes - stronger enrichment the better). http://compbio.cs.brown.edu/software/

  • TopDom - An efficient and Deterministic Method for identifying Topological Domains in Genomes, http://zhoulab.usc.edu/TopDom/

    • Shin, Hanjun, Yi Shi, Chao Dai, Harianto Tjong, Ke Gong, Frank Alber, and Xianghong Jasmine Zhou. “TopDom: An Efficient and Deterministic Method for Identifying Topological Domains in Genomes.” Nucleic Acids Research 44, no. 7 (April 20, 2016): e70. https://doi.org/10.1093/nar/gkv1505. - TopDom paper. Review of other methods. Method is based on 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 directionaliry index. Found expected enrichment in CTCF, histone marks. Housekeeping genes and overall gene density are close to TAD boundaries, differentially expressed genes are not. Figure 7 - how to detect common/unique boundaries using Jaccard-like statistics. http://zhoulab.usc.edu/TopDom/

Prediction of 3D features

  • 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). https://bitbucket.org/4dnucleome/3depiloop

    • Al Bkhetan, Ziad, and Dariusz Plewczynski. “Three-Dimensional Epigenome Statistical Model: Genome-Wide Chromatin Looping Prediction.” Scientific Reports 8, no. 1 (December 2018). https://doi.org/10.1038/s41598-018-23276-8.

  • 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 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. https://github.com/ma-compbio/SNIPER

    • Xiong, Kyle, and Jian Ma. “Revealing Hi-C Subcompartments by Imputing High-Resolution Inter-Chromosomal Chromatin Interactions.” BioRxiv, January 1, 2018, 505503. https://doi.org/10.1101/505503.

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, same info downloadable as text files.

  • 3DSNP - A database linking noncoding SNPs to 3D interacting genes. http://cbportal.org/3dsnp/

    • Lu, Yiming, Cheng Quan, Hebing Chen, Xiaochen Bo, and Chenggang Zhang. “3DSNP: A Database for Linking Human Noncoding SNPs to Their Three-Dimensional Interacting Genes.” Nucleic Acids Research 45, no. D1 (January 4, 2017): D643–49. https://doi.org/10.1093/nar/gkw1022. - 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. 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.” BioRxiv, 2017, 117531.

CNV and Structural variant detection

  • hic_breakfinder - SV identification in Hi-C data. https://github.com/dixonlab/hic_breakfinder

    • Dixon, Jesse R., Jie Xu, Vishnu Dileep, Ye Zhan, Fan Song, Victoria T. Le, Galip Gürkan Yardımcı, et al. “Integrative Detection and Analysis of Structural Variation in Cancer Genomes.” Nature Genetics, September 10, 2018. https://doi.org/10.1038/s41588-018-0195-8. - 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 - 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. CNV calling: https://github.com/ay-lab/HiCnv, Translocation calling: https://github.com/ay-lab/HiCtrans, Hi-C simulation: https://github.com/ay-lab/AveSim 

    • Chakraborty, Abhijit, and Ferhat Ay. “Identification of Copy Number Variations and Translocations in Cancer Cells from Hi-C Data.” Edited by Christina Curtis. Bioinformatics 34, no. 2 (January 15, 2018): 338–45. https://doi.org/10.1093/bioinformatics/btx664.

Visualization

  • 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. http://promoter.bx.psu.edu/hi-c/

    • Wang, Yanli, Bo Zhang, Lijun Zhang, Lin An, Jie Xu, Daofeng Li, Mayank NK Choudhary, et al. “The 3D Genome Browser: A Web-Based Browser for Visualizing 3D Genome Organization and Long-Range Chromatin Interactions.” BioRxiv, 2017, 112268.

  • Flyamer, Ilya M., Robert S. Illingworth, and Wendy A. Bickmore. “Coolpup.Py - a Versatile Tool to Perform Pile-up Analysis of Hi-C Data.” BioRxiv, January 1, 2019, 586537. https://doi.org/10.1101/586537. - Pile-up analysis of Hi-C data for visualizing and identifying chromatin loops, exploring Hi-C data transformation. Works on .cool files. https://github.com/Phlya/coolpuppy

  • CSynth - 3D genome interactive modeling on GPU, and visualization. http://csynth.org/

    • Todd, Stephen, Peter Todd, Simon J McGowan, James R Hughes, Yasutaka Kakui, Frederic Fol Leymarie, William Latham, and Stephen Taylor. “CSynth: A Dynamic Modelling and Visualisation Tool for 3D Chromatin Structure.” BioRxiv, January 1, 2019, 499806. https://doi.org/10.1101/499806.

  • GENOVA - GENome Organisation Visual Analytics, an R package for rich visual analysis of Hi-C data. Input - HiC-Pro processed files, BED, text formats. Single or two experiment analysis. Integration of external annotations, A/B compartments, cis-/trans-interactions, TADs and loops, genes, insluation score heatmap, differences. https://github.com/robinweide/GENOVA

  • HiGlass visualization server for Google maps-style navigation of Hi-C maps. Overlay genes, epigenomic tracks. http://higlass.io/, https://github.com/higlass/higlass, and many HiGlass-related developmend from the author, https://github.com/pkerpedjiev

    • Kerpedjiev, Peter, Nezar Abdennur, Fritz Lekschas, Chuck McCallum, Kasper Dinkla, Hendrik Strobelt, Jacob M Luber, et al. “HiGlass: Web-Based Visual Comparison And Exploration Of Genome Interaction Maps.” BioRxiv, 2017, 121889.

  • HiPiler - exploration and comparison of loops and domains as snippets-heatmaps of data. https://github.com/flekschas/hipiler

  • pyGenomeTracks - python module to plot beautiful and highly customizable genome browser tracks, https://github.com/deeptools/pyGenomeTracks

  • TADKit - 3D Genome Browser. Main web site, http://sgt.cnag.cat/3dg/tadkit/, and GitHub, https://github.com/3DGenomes/TADkit

De novo genome scaffolding

  • Tools for de novo genome assembly from Hi-C reads: https://omictools.com/assembly-scaffolding-1-category

  • dnaTri - genome scaffolding via probabilistic modeling using two constrains 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, http://my5c.umassmed.edu/triangulation/, code https://github.com/NoamKaplan/dna-triangulation

    • Kaplan, Noam, and Job Dekker. “High-Throughput Genome Scaffolding from in Vivo DNA Interaction Frequency.” Nature Biotechnology 31, no. 12 (December 2013): 1143–47. https://doi.org/10.1038/nbt.2768.

  • GRAAL - Genome (Re)Assembly Assessing Likelihood - genome assembly from Hi-C data. Gaps in genome assembly that can be filled by scaffolding. Superior than 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. https://github.com/koszullab/GRAAL and the next version instaGRAAL that uses https://github.com/koszullab/instaGRAAL

    • Marie-Nelly, Hervé, Martial Marbouty, Axel Cournac, Jean-François Flot, Gianni Liti, Dante Poggi Parodi, Sylvie Syan, et al. “High-Quality Genome (Re)Assembly Using Chromosomal Contact Data.” Nature Communications 5 (December 17, 2014): 5695. https://doi.org/10.1038/ncomms6695.

  • 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 normal human, mouse, drosophila genomes, and on HeLa cancer genome. https://github.com/shendurelab/LACHESIS

    • Burton, Joshua N., Andrew Adey, Rupali P. Patwardhan, Ruolan Qiu, Jacob O. Kitzman, and Jay Shendure. “Chromosome-Scale Scaffolding of de Novo Genome Assemblies Based on Chromatin Interactions.” Nature Biotechnology 31, no. 12 (December 2013): 1119–25. https://doi.org/10.1038/nbt.2727.

3D reconstruction

  • 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. https://github.com/jianlin-cheng/GenomeFlow

    • Trieu, Tuan, Oluwatosin Oluwadare, Julia Wopata, and Jianlin Cheng. “GenomeFlow: A Comprehensive Graphical Tool for Modeling and Analyzing 3D Genome Structure.” Bioinformatics (Oxford, England), September 12, 2018. https://doi.org/10.1093/bioinformatics/bty802.

  • ShRec3D - shortest-path reconstruction in 3D. Genome reconstruction by translation a Hi-C matrix into a distance matrix, then multidimensional scaling. Uses binary contact maps. https://sites.google.com/site/julienmozziconacci/home/softwares

    • Lesne, Annick, Julien Riposo, Paul Roger, Axel Cournac, and Julien Mozziconacci. “3D Genome Reconstruction from Chromosomal Contacts.” Nature Methods 11, no. 11 (November 2014): 1141–43. https://doi.org/10.1038/nmeth.3104.

Miscellaneous

  • 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. Can be used for differential analysis (comparison) of two Hi-C maps. https://github.com/LeopoldC/Boost-HiC

    • Carron, Leopold, Jean-baptiste Morlot, Vincent Matthys, Annick Lesne, and Julien Mozziconacci. “Boost-HiC : Computational Enhancement of Long-Range Contacts in Chromosomal Contact Maps,” November 18, 2018. https://doi.org/10.1101/471607.

  • GOPHER - probe design for Capture Hi-C. All, or selected, promoters, or around GWAS hits. Two other tools, CapSequm and HiCapTools. https://github.com/TheJacksonLaboratory/Gopher

    • Hansen, Peter, Salaheddine Ali, Hannah Blau, Daniel Danis, Jochen Hecht, Uwe Kornak, Darío G. Lupiáñez, Stefan Mundlos, Robin Steinhaus, and Peter N. Robinson. “GOPHER: Generator Of Probes for Capture Hi-C Experiments at High Resolution.” BMC Genomics 20, no. 1 (December 2019). https://doi.org/10.1186/s12864-018-5376-4.

  • hic2cool - Lightweight converter between hic and cool contact matrices. https://github.com/4dn-dcic/hic2cool

  • HiCPlus - increasing resolution of Hi-C data using convolutional neural network. Basically, smoothing parts of Hi-C image, then binning into smaller parts. Performs better than bilinear/biqubic smoothing. https://github.com/zhangyan32/HiCPlus

    • Zhang, Yan, Lin An, Ming Hu, Jijun Tang, and Feng Yue. “HiCPlus: Resolution Enhancement of Hi-C Inte

  • 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 is improved. Novel interactions. Enrichment of TAD boundaries in LINE and SINE repetitive elements. Multi-mapping not sensitive to trimming. Read filtering strategy (Figure 1, supplementary figures are very visual). https://github.com/keleslab/mHiC

    • Zheng, Ye, Ferhat Ay, and Sunduz Keles. “Generative Modeling of Multi-Mapping Reads with MHi-C Advances Analysis of High Throughput Genome-Wide Conformation Capture Studies,” October 3, 2018. https://doi.org/10.1101/301705.

  • peaktools - tools for BEDPE-style peaks. https://github.com/sergpolly/peaktools

  • scMerge - R package for batch effect removal and normalizing of multipe scRNA-seq datasets. fastRUVIII batch removal method. Tested on 14 datasets, compared with scran, MNN, ComBat, Seurat, ZINB-WaVE using Silhouette, ARI - better separation of clusters, pseudotime reconstruction. https://github.com/SydneyBioX/scMerge/

    • Lin, Yingxin, Shila Ghazanfar, Kevin Wang, Johann A. Gagnon-Bartsch, Kitty K. Lo, Xianbin Su, Ze-Guang Han, et al. “ScMerge: Integration of Multiple Single-Cell Transcriptomics Datasets Leveraging Stable Expression and Pseudo-Replication,” September 12, 2018. https://doi.org/10.1101/393280.

Papers

A four-cutter enzyme yields a resolution of ∼256 bp and a six-cutter a resolution of ∼4,096 bp.

Methodological Reviews

  • Ay, Ferhat, and William S. Noble. “Analysis Methods for Studying the 3D Architecture of the Genome.” Genome Biology 16 (September 2, 2015): 183. https://doi.org/10.1186/s13059-015-0745-7. - Hi-C technology and methods review. Table 1 - list of tools. Biases, normalization, matrix balancing. Extracting significant contacts, obs/exp ratio, parametric (powerlaw, neg binomial, double exponential), non-parametric (splines). 3D enrichment. References. TAD identification, directionality index. Outlook, importance of comparative analysis

  • Chang, Pearl, Moloya Gohain, Ming-Ren Yen, and Pao-Yang Chen. “Computational Methods for Assessing Chromatin Hierarchy.” Computational and Structural Biotechnology Journal 16 (2018): 43–53. https://doi.org/10.1016/j.csbj.2018.02.003. - Review of higher-order (chromatin conformation capture) and primary order (DNAse, ATAC) technologies and analysis tools. Table 1 - technology summaries. Table 2 - tool summaries. Inter-chromosomal calls using Binarized contact maps. Visualization. Primary order technologies - details and peak calling.

  • 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. https://doi.org/10.1038/nmeth.4325. - 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 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. https://images.nature.com/full/nature-assets/nmeth/journal/v14/n7/extref/nmeth.4325-S1.pdf

  • Nicoletti, Chiara, Mattia Forcato, and Silvio Bicciato. “Computational Methods for Analyzing Genome-Wide Chromosome Conformation Capture Data.” Current Opinion in Biotechnology 54 (December 2018): 98–105. https://doi.org/10.1016/j.copbio.2018.01.023. - 3C-Hi-C tools review, Table 1 lists categorizes main tools, Figure 1 displays all steps in technology and analysis (alignment, resolution, normalization, including accounting for CNVs, A/B compartments, TAD detection, visualization). Concise description of all tools.

  • Pal, Koustav, Mattia Forcato, and Francesco Ferrari. “Hi-C Analysis: From Data Generation to Integration.” Biophysical Reviews, December 20, 2018. https://doi.org/10.1007/s12551-018-0489-1. - Hi-C technology, data, 3D structures, analysis, and tools. Technology improvement and increasing resolution. FASTQ processing steps ("Hi-C data analysis: from FASTQ to interaction maps" section), pipelines, finding minimum resolution, normalization. Downstream analysis: A/B compartment detection, TAD callers, Hierarchical TADs, interaction callers. Data formats (pairix, sparse matrix format, cool, hic, butlr, hdf5, pgl). Hi-C visualization tools. Table 2 - summary and comparison of all tools.https://link.springer.com/article/10.1007%2Fs12551-018-0489-1#Tab2

  • Yardımcı, Galip Gürkan, and William Stafford Noble. “Software Tools for Visualizing Hi-C Data.” Genome Biology 18, no. 1 (December 2017). https://doi.org/10.1186/s13059-017-1161-y. - Hi-C technology, data, and visualization review. Suggestion about graph representation.

  • Waldispühl, Jérôme, Eric Zhang, Alexander Butyaev, Elena Nazarova, and Yan Cyr. “Storage, Visualization, and Navigation of 3D Genomics Data.” Methods, May 2018. https://doi.org/10.1016/j.ymeth.2018.05.008. - Review of tools for visualization of 3C-Hi-C data, challenges, analysis (Table 1). Data formats (hic, cool, BUTLR, ccmap). Database to quickly access 3D data. Details of each visualization tool in Section 4

General Reviews

  • Yu, Miao, and Bing Ren. “The Three-Dimensional Organization of Mammalian Genomes.” Annual Review of Cell and Developmental Biology 33 (06 2017): 265–89. https://doi.org/10.1146/annurev-cellbio-100616-060531. - 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), association of TAD boundaries with 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): 852–852. doi:10.15252/msb.20156492. http://msb.embopress.org/content/msb/11/12/852.full.pdf - 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): 390–403. https://doi.org/10.1038/nrg3454. https://www.nature.com/articles/nrg3454 - 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): 3849–55. https://doi.org/10.1093/nar/gks012.

Normalization

  • 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): 1059–65. https://doi.org/10.1038/ng.947. - 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

  • Dali, Rola, and Mathieu Blanchette. “A Critical Assessment of Topologically Associating Domain Prediction Tools.” Nucleic Acids Research 45, no. 6 (April 7, 2017): 2994–3005. doi:10.1093/nar/gkx145. - TAD definition, tools. Meta-TADs, hierarchy, overlapping TADs. HiCPlotter for visualization. Manual annotation as a gold standard. Sequencing depth and resolution affects things. Code, manual annotations

  • 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): 296–300. https://doi.org/10.1038/nature20158. - 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.

  • 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): 240–44. https://doi.org/10.1038/nature14450. - InsulationScore, https://github.com/dekkerlab/crane-nature-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 at https://github.com/dekkerlab/crane-nature-2015. ICE normalized data. OK to analyze data at two different resolutions

  • "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.

TAD prediction

  • Bednarz, Paweł, and Bartek Wilczyński. “Supervised Learning Method for Predicting Chromatin Boundary Associated Insulator Elements.” Journal of Bioinformatics and Computational Biology 12, no. 06 (December 2014): 1442006. doi:10.1142/S0219720014420062. http://www.worldscientific.com/doi/pdf/10.1142/S0219720014420062 - 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. 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", https://arxiv.org/abs/1707.09587. - 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. https://doi.org/10.1101/268318. - 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 Laplasian 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. https://doi.org/10.1101/089011. - 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 TAD overlap.

  • Chen, Jie, Alfred O. Hero, and Indika Rajapakse. “Spectral Identification of Topological Domains.” Bioinformatics (Oxford, England) 32, no. 14 (15 2016): 2151–58. https://doi.org/10.1093/bioinformatics/btw221. - 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.

  • Fotuhi Siahpirani, Alireza, Ferhat Ay, and Sushmita Roy. “A Multi-Task Graph-Clustering Approach for Chromosome Conformation Capture Data Sets Identifies Conserved Modules of Chromosomal Interactions.” Genome Biology 17, no. 1 (December 2016). https://doi.org/10.1186/s13059-016-0962-8 - 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 regulatory genomic 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

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