MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps

Ye, Yusen; Gao, Lin; Zhang, Shihua

doi:10.1093/nar/gkz201

Cited by 16 publications

(9 citation statements)

References 44 publications

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“…Ye et al [ 80 ] proposed a fast density-based clustering method called MSTD. MSTD aims to call TADs by clustering points with rectangular shapes, and it has two main steps: 1- Determining cluster centers via the density of chromosome contact frequencies, and 2- Assigning the remaining elements to the same cluster as its nearest-neighbor element of higher density layer by layer.…”

Section: Methodsmentioning

confidence: 99%

A comparison of topologically associating domain callers over mammals at high resolution

Sefer

2022

BMC Bioinformatics

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Background Topologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in. Result We have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties. Conclusion We have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.

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Section: Methodsmentioning

confidence: 99%

A comparison of topologically associating domain callers over mammals at high resolution

Sefer

2022

BMC Bioinformatics

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“…So far, more than two dozen [59] , [63] , [64] , [65] , [66] programs or algorithms have been developed to predict TADs. Most of TAD prediction methods can be grouped into four major categories: one-dimension linear score method (e.g., TopDom [67] ), two-dimension clustering method (e.g., MSTD [68] ), feature method (e.g., pTADS [69] ), and statistical method (e.g., HiCseg [70] ). Recently, a systematic comparison of 22 computational methods for predicting TADs was carried out [66] , providing a detailed description illustrating the pros and cons of each of the 22 methods.…”

Section: Computational Methods In Mapping 3d Chromatin Domains and In...mentioning

confidence: 99%

Mapping nucleosome and chromatin architectures: A survey of computational methods

Fang

Wang²,

Liu³

et al. 2022

Computational and Structural Biotechnology Journal

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“…Inspired by a fast density-based clustering method designed for grouping data points [47, 48], we take advantage of finding the cluster centers to detect domain boundaries for each chromosome of individual cells ( Fig. 1b and c ).…”

Section: Methodsmentioning

confidence: 99%

Deciphering Hierarchical Chromatin Domains and Preference of Genomic Position Forming Boundaries in Single Mouse Embryonic Stem Cells

Zhang

Gao

et al. 2022

Preprint

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Recent developments in the three-dimensional (3D) genome have revealed chromatin organization with hierarchical architectures at different genomic scales. Transcriptional regulators and their specific interactions implicate gene expression programs by complex chromatin structures. The exploration of single-cell 3D genome maps reveals that chromatin domains are indeed physical structures presenting in single cells and domain boundaries vary from cell to cell. However, the formation mechanism and functional implication of hierarchical chromatin structures in single cells is still blurry. To this end, we first develop a hierarchical chromatin structure identification algorithm (named as HiCS) from individual single-cell Hi-C maps, with superior performance in both accuracy and efficiency. The enrichment of hundreds of regulatory factors in domain boundaries suggests that in addition to the CTCF-cohesin complex, Polycomb, TrxG, pluripotent protein families and other multiple factors also contribute to shaping chromatin landscapes in single embryonic stem cells. We further decipher the preference of genomic positions-function relationship by grouping and annotating chromatin landscape clusters with different types of regulatory factors, and the relationship corresponds to precisely the largest five types of retrotransposons. Our results suggest that multiple types of regulatory factors interplaying with each other in specific genomic positions may navigate the preference of genomic positions, and then shape chromosome architecture and cell functions of single cells.

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MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps

Cited by 16 publications

References 44 publications

A comparison of topologically associating domain callers over mammals at high resolution

A comparison of topologically associating domain callers over mammals at high resolution

Mapping nucleosome and chromatin architectures: A survey of computational methods

Deciphering Hierarchical Chromatin Domains and Preference of Genomic Position Forming Boundaries in Single Mouse Embryonic Stem Cells

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