SuperTAD: robust detection of hierarchical topologically associated domains with optimized structural information.
Yu Wei ZhangMeng Bo WangShuai Cheng LiPublished in: Genome biology (2021)
Topologically associating domains (TADs) are the organizational units of chromosome structures. TADs can contain TADs, thus forming a hierarchy. TAD hierarchies can be inferred from Hi-C data through coding trees. However, the current method for computing coding trees is not optimal. In this paper, we propose optimal algorithms for this computation. In comparison with seven state-of-art methods using two public datasets, from GM12878 and IMR90 cells, SuperTAD shows a significant enrichment of structural proteins around detected boundaries and histone modifications within TADs and displays a high consistency between various resolutions of identical Hi-C matrices.
Keyphrases
- induced apoptosis
- machine learning
- cell cycle arrest
- healthcare
- dna methylation
- high resolution
- mental health
- big data
- endoplasmic reticulum stress
- electronic health record
- deep learning
- hiv infected
- emergency department
- cell death
- health information
- cell proliferation
- gene expression
- antiretroviral therapy
- oxidative stress
- single cell
- mass spectrometry