dcHiC detects differential compartments across multiple Hi-C datasets

Chakraborty, Abhijit; Wang, Jeffrey G.; Ay, Ferhat

doi:10.1038/s41467-022-34626-6

Cited by 40 publications

(29 citation statements)

References 95 publications

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“…b Chromosome 17, 33.5 Mb to 49 Mb. Benjamini-Hochberg adjusted p-values are 9.3e-57 and 1.5e-22 for the highlighted chr3 and chr17 regions, respectively, based on differential compartment analysis of 100 kb genomic regions using tool dcHiC 96 . c .…”

Section: Resultsmentioning

confidence: 99%

A DNA tumor virus globally reprograms host 3D genome architecture to achieve immortal growth

et al. 2023

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Epstein-Barr virus (EBV) immortalization of resting B lymphocytes (RBLs) to lymphoblastoid cell lines (LCLs) models human DNA tumor virus oncogenesis. RBL and LCL chromatin interaction maps are compared to identify the spatial and temporal genome architectural changes during EBV B cell transformation. EBV induces global genome reorganization where contact domains frequently merge or subdivide during transformation. Repressed B compartments in RBLs frequently switch to active A compartments in LCLs. LCLs gain 40% new contact domain boundaries. Newly gained LCL boundaries have strong CTCF binding at their borders while in RBLs, the same sites have much less CTCF binding. Some LCL CTCF sites also have EBV nuclear antigen (EBNA) leader protein EBNALP binding. LCLs have more local interactions than RBLs at LCL dependency factors and super-enhancer targets. RNA Pol II HiChIP and FISH of RBL and LCL further validate the Hi-C results. EBNA3A inactivation globally alters LCL genome interactions. EBNA3A inactivation reduces CTCF and RAD21 DNA binding. EBNA3C inactivation rewires the looping at the CDKN2A/B and AICDA loci. Disruption of a CTCF site at AICDA locus increases AICDA expression. These data suggest that EBV controls lymphocyte growth by globally reorganizing host genome architecture to facilitate the expression of key oncogenes.

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Section: Resultsmentioning

confidence: 99%

A DNA tumor virus globally reprograms host 3D genome architecture to achieve immortal growth

et al. 2023

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“…While it is intriguing how well genomic sequence can predict compartments, it is generally accepted that compartments vary between cell types ( Kim et al, 2020 ; Nichols and Corces, 2021 ; Chakraborty et al, 2022 ). Implementing cell-type specific signals, a preprint article describes CoRNN, which uses histone modification ChIP-seq data with recurrent neural networks to predict chromosome compartments at 100 kb ( Zheng et al, 2022 ).…”

Section: Compartment Predictionmentioning

confidence: 99%

“…Surprisingly, there are relatively few algorithms to statistically identify differential compartment intervals. One recently developed algorithm, dcHiC, compares quantile normalized eigenvectors, using the Mahalanobis distance with chi-square tests and p -value correction to assign statistical significance ( Chakraborty et al, 2022 ). This method provides a statistical test to identify significantly differential compartment intervals between maps.…”

Section: Potential Limitations In Compartment Analysismentioning

confidence: 99%

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Considerations and caveats for analyzing chromatin compartments

Kalluchi

Harris

Reznicek

et al. 2023

Front. Mol. Biosci.

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Genomes are organized into nuclear compartments, separating active from inactive chromatin. Chromatin compartments are readily visible in a large number of species by experiments that map chromatin conformation genome-wide. When analyzing these maps, a common step is the identification of genomic intervals that interact within A (active) and B (inactive) compartments. It has also become increasingly common to identify and analyze subcompartments. We review different strategies to identify A/B and subcompartment intervals, including a discussion of various machine-learning approaches to predict these features. We then discuss the strengths and limitations of current strategies and examine how these aspects of analysis may have impacted our understanding of chromatin compartments.

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“…Importantly, identifying reorganized TADs using emerging single-cell Hi-C (scHi-C) data is largely under-explored. Other methods are developed for comparing Hi-C matrices at differnet scales and for different purposes: quantifying similarities of genome-wide Hi-C contact matrices [34, 35], identifying differential A/B compartments [36], and identifying differnetial chromatin interactions [37–39]. However, these methods are not tailored to compare Hi-C contact matrices at TAD-level, not optimal for identifying reorganized TADs (see our own comparison later).…”

Section: Introductionmentioning

confidence: 99%

DiffDomain enables identification of structurally reorganized topologically associating domains

Hua

Zhang

et al. 2022

Preprint

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Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with transcription and other essential genome functions. However, computational methods that can identify reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using chromatin contact maps. Method comparison using multiple real Hi-C datasets reveals that DiffDomain outperforms alternative methods for FPRs, TPRs, and identifying a new subtype of reorganized TADs. The robustness of DiffDomain and its biological applications are demonstrated by applying on Hi-C data from different cell types and disease states. Identified reorganized TADs are associated with structural variations and changes in CTCF binding sites and other epigenomic changes. By applying to a single-cell Hi-C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi-C data from as few as 100 cells per condition. Moreover, DiffDomain reveals that TADs have clear differential cell-to-population variability and heterogeneous cell-to-cell variability. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi-C and single-cell Hi-C data.

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dcHiC detects differential compartments across multiple Hi-C datasets

Cited by 40 publications

References 95 publications

A DNA tumor virus globally reprograms host 3D genome architecture to achieve immortal growth

A DNA tumor virus globally reprograms host 3D genome architecture to achieve immortal growth

Considerations and caveats for analyzing chromatin compartments

DiffDomain enables identification of structurally reorganized topologically associating domains

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