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Tytuł pozycji:

SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering.

Tytuł:
SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering.
Autorzy:
Cresswell KG; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Stansfield JC; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Dozmorov MG; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA. .
Źródło:
BMC bioinformatics [BMC Bioinformatics] 2020 Jul 20; Vol. 21 (1), pp. 319. Date of Electronic Publication: 2020 Jul 20.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
Algorithms*
Gene Expression Regulation*
Genome, Human*
Software*
Chromatin/*genetics
Computational Biology/*methods
Cluster Analysis ; Humans ; Models, Genetic
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Grant Information:
P50 AA022537 United States AA NIAAA NIH HHS; NA Pharmaceutical Research and Manufacturers of America Foundation
Contributed Indexing:
Keywords: Chromosome conformation capture; Hi-C; Hierarchy; SpectralTAD; TADs; Topologically associated domains
Substance Nomenclature:
0 (Chromatin)
Entry Date(s):
Date Created: 20200722 Date Completed: 20200813 Latest Revision: 20210401
Update Code:
20240104
PubMed Central ID:
PMC7372752
DOI:
10.1186/s12859-020-03652-w
PMID:
32689928
Czasopismo naukowe
Background: The three-dimensional (3D) structure of the genome plays a crucial role in gene expression regulation. Chromatin conformation capture technologies (Hi-C) have revealed that the genome is organized in a hierarchy of topologically associated domains (TADs), sub-TADs, and chromatin loops. Identifying such hierarchical structures is a critical step in understanding genome regulation. Existing tools for TAD calling are frequently sensitive to biases in Hi-C data, depend on tunable parameters, and are computationally inefficient.
Methods: To address these challenges, we developed a novel sliding window-based spectral clustering framework that uses gaps between consecutive eigenvectors for TAD boundary identification.
Results: Our method, implemented in an R package, SpectralTAD, detects hierarchical, biologically relevant TADs, has automatic parameter selection, is robust to sequencing depth, resolution, and sparsity of Hi-C data. SpectralTAD outperforms four state-of-the-art TAD callers in simulated and experimental settings. We demonstrate that TAD boundaries shared among multiple levels of the TAD hierarchy were more enriched in classical boundary marks and more conserved across cell lines and tissues. In contrast, boundaries of TADs that cannot be split into sub-TADs showed less enrichment and conservation, suggesting their more dynamic role in genome regulation.
Conclusion: SpectralTAD is available on Bioconductor, http://bioconductor.org/packages/SpectralTAD/ .
Erratum in: BMC Bioinformatics. 2020 Aug 27;21(1):373. (PMID: 32854628)
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