TADsplimer reveals splits and mergers of topologically associating domains for epigenetic regulation of transcription

Wang, Guangyu; Meng, Qingshu; Xia, Bo; Zhang, Shuo; Lv, Jie; Zhao, Dongyu; Li, Yanqiang; Wang, Xin; Zhang, Lili; Cooke, John P.; Cao, Qi; Chen, Kaifu

doi:10.1186/s13059-020-01992-7

Cited by 8 publications

(9 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with TADsplimer which specifically identifies split and merge TADs 25 , DiffDomain identifies similar numbers of split and merge TADs between multiple pairs of human cell lines (Supplementary Fig. 8) .…”

Section: Resultsmentioning

confidence: 99%

DiffDomain enables identification of structurally reorganized topologically associating domains

Hua,

Gu,

Zhang

et al. 2024

Nat Commun

View full text Add to dashboard Cite

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 essential genome functions. However, computational methods for identifying 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 high-throughput chromosome conformation capture (Hi–C) contact maps. Method comparison using multiple real Hi–C datasets reveals that DiffDomain outperforms alternative methods for false positive rates, true positive rates, and identifying a new subtype of reorganized TADs. Applying DiffDomain to Hi–C data from different cell types and disease states demonstrates its biological relevance. Identified reorganized TADs are associated with structural variations and epigenomic changes such as changes in CTCF binding sites. 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 differential cell-to-population variability and heterogeneous cell-to-cell variability in TADs. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi–C and single-cell Hi–C data.

show abstract

Section: Resultsmentioning

confidence: 99%

DiffDomain enables identification of structurally reorganized topologically associating domains

Hua,

Gu,

Zhang

et al. 2024

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Compared with TADsplimer which specifically identifies split and merge TADs [25], DiffDomain identifies similar numbers of split and merge TADs between multiple pairs of human cell lines (Supplementary Fig. S7).…”

Section: Resultsmentioning

confidence: 99%

“…The majority of current methods call a reorganized TAD if at lesast one of its two boundaries changed between two conditions [11,12,[23][24][25][26][27][28]. These methods enable easy integration with other analysis piplines and identified reorganized TADs have clear biological interpretation.…”

Section: Introductionmentioning

confidence: 99%

DiffDomain enables identification of structurally reorganized topologically associating domains

Hua

Zhang

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…It was reported that a global flapping of A and B compartments played an important role in T cell lineage specification and in the process of cardiogenesis ( 38 , 45 ). Most TAD boundaries tend to remain stable across cell types, whereas a small subset of TAD boundaries could be cell type specific ( 10 , 44 , 46 ). Disruption of TAD boundary was reported to rewire gene-enhancer interactions and lead to distinct human limb malformations ( 47 ).…”

Section: Discussionmentioning

confidence: 99%

StripeDiff: Model-based algorithm for differential analysis of chromatin stripe

et al. 2022

Self Cite

View full text Add to dashboard Cite

Multiple recent studies revealed stripes as an architectural feature of three-dimensional chromatin and found stripes connected to epigenetic regulation of transcription. Whereas a couple of tools are available to define stripes in a single sample, there is yet no reported method to quantitatively measure the dynamic change of each stripe between samples. Here, we developed StripeDiff, a bioinformatics tool that delivers a set of statistical methods to detect differential stripes between samples. StripeDiff showed optimal performance in both simulation data analysis and real Hi-C data analysis. Applying StripeDiff to 12 sets of Hi-C data revealed new insights into the connection between change of chromatin stripe and change of chromatin modification, transcriptional regulation, and cell differentiation. StripeDiff will be a robust tool for the community to facilitate understanding of stripes and their function in numerous biological models.

show abstract

TADsplimer reveals splits and mergers of topologically associating domains for epigenetic regulation of transcription

Cited by 8 publications

References 47 publications

DiffDomain enables identification of structurally reorganized topologically associating domains

DiffDomain enables identification of structurally reorganized topologically associating domains

DiffDomain enables identification of structurally reorganized topologically associating domains

StripeDiff: Model-based algorithm for differential analysis of chromatin stripe

Contact Info

Product

Resources

About