Comparison of computational methods for Hi-C data analysis

Forcato, Mattia; Nicoletti, Chiara; Pal, Koustav; Livi, Carmen Maria; Ferrari, Francesco; Bicciato, Silvio

doi:10.1038/nmeth.4325

Cited by 297 publications

(390 citation statements)

References 41 publications

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“…Some ROIs are directly extracted by pattern recognition algorithms that work on interaction matrices [18]. In addition, other measures that are derived from different data types, such as protein-binding probability or gene expression, or existing metadata, such as genes or structural variants, are used to define ROIs too.…”

Section: Background—visual Analysis In Spatial Genome Organizationmentioning

confidence: 99%

“…However, these algorithms can be very complex and often identify tens of thousands of specific pattern instances, many of questionable quality. Results of algorithms designed to identify the same type of pattern often differ substantially [18] and the lack of a ground-truth pattern collection hinders the evaluation of these algorithms. Thus, even if patterns can be retrieved automatically, assessing pattern quality requires human inspection.…”

Section: Introductionmentioning

confidence: 99%

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HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Lekschas

Bach

Kerpedjiev

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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This paper presents an interactive visualization interface—HiPiler—for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like “snippets”. Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

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Section: Background—visual Analysis In Spatial Genome Organizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Lekschas

Bach

Kerpedjiev

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…The functional role for TADs and subTADs, and the extent to which they change across biological conditions, remains poorly understood, in part because of the paucity of methods for sensitive and accurate detection of sub-Mb-scale domains. Two recent comparative analyses reported that existing domain-calling methods accurately identify TADs but show poor ability to capture the full nested hierarchy of partially overlapping subTADs 11,12 . Thus, there is a great need for computational tools that accurately and sensitively detect the full nested hierarchy of chromatin domains across length scales.…”

mentioning

confidence: 99%

Detecting hierarchical genome folding with network modularity

et al. 2018

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Mammalian genomes are folded in a hierarchy of compartments, topologically associating domains (TADs), subTADs and looping interactions. Here, we describe 3DNetMod, a graph theory-based method for sensitive and accurate detection of chromatin domains across length scales in Hi-C data. We identify nested, partially overlapping TADs and subTADs genome wide by optimizing network modularity and varying a single resolution parameter. 3DNetMod can be applied broadly to understand genome reconfiguration in development and disease.

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“…A/B compartments can readily be identified by using principal component analysis (PCA), since these compartments tend to be captured by the first principal component [20]. TADs can be mapped when interaction data is binned at 40 kb or less, using any one of a number of available algorithms (reviewed in [57] and [58]). Unlike chromosome territories, A/B compartments, or TADs, whose identification requires only moderate Hi-C data resolutions, chromatin loops can only be identified at much higher resolution (e.g., a 10 kb or higher resolution) [27].…”

Section: Identifying Architectural Features Of 3d Genome Organizationmentioning

confidence: 99%

Using DNase Hi-C techniques to map global and local three-dimensional genome architecture at high resolution

Lee

et al. 2017

Preprint

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The folding and three-dimensional (3D) organization of chromatin in the nucleus critically impacts genome function. The past decade has witnessed rapid advances in genomic tools for delineating 3D genome architecture. Among them, chromosome conformation capture (3C)-based methods such as Hi-C are the most widely used techniques for mapping chromatin interactions. However, traditional Hi-C protocols rely on restriction enzymes (REs) to fragment chromatin and are therefore limited in resolution. We recently developed DNase Hi-C for mapping 3D genome organization, which uses DNase I for chromatin fragmentation. DNase Hi-C overcomes RE-related limitations associated with traditional Hi-C methods, leading to improved methodological resolution. Furthermore, combining this method with DNA capture technology provides a high-throughput approach (targeted DNase Hi-C) that allows for mapping fine-scale chromatin architecture at exceptionally high resolution. Hence, targeted DNase Hi-C will be valuable for delineating the physical landscapes of cis-regulatory networks that control gene expression and for characterizing phenotype-associated chromatin 3D signatures. Here, we provide a detailed description of method design and step-by-step working protocols for these two methods.

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Comparison of computational methods for Hi-C data analysis

Cited by 297 publications

References 41 publications

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Detecting hierarchical genome folding with network modularity

Using DNase Hi-C techniques to map global and local three-dimensional genome architecture at high resolution

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