Hi-C as a molecular rangefinder to examine genomic rearrangements

Kim, Kyukwang; Kim, Mooyoung; Kim, Dae Young; Lee, Dong-Sung; Jung, Inkyung

doi:10.1016/j.semcdb.2021.04.024

Cited by 10 publications

(7 citation statements)

References 68 publications

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“…Recently, we and other groups showed that Hi-C, a technique that was originally proposed to study three-dimensional (3D) genomic architectures, can also be used for systematic SV detection with as little as 1× genome coverage (11,(17)(18)(19)(20). As SVs induce de novo chromatin interactions across the breakpoints, when Hi-C reads are mapped to the reference genome, different types of SVs are characterized by aberrant interaction blocks with different orientations.…”

Section: Introductionmentioning

confidence: 99%

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

2022

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The Hi-C technique has been shown to be a promising method to detect structural variations (SVs) in human genomes. However, algorithms that can use Hi-C data for a full-range SV detection have been severely lacking. Current methods can only identify interchromosomal translocations and long-range intrachromosomal SVs (>1 Mb) at less-than-optimal resolution. Therefore, we develop EagleC, a framework that combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution. We show that EagleC can uniquely capture a set of fusion genes that are missed by whole-genome sequencing or nanopore. Furthermore, EagleC also effectively captures SVs in other chromatin interaction platforms, such as HiChIP, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), and capture Hi-C. We apply EagleC in more than 100 cancer cell lines and primary tumors and identify a valuable set of high-quality SVs. Last, we demonstrate that EagleC can be applied to single-cell Hi-C and used to study the SV heterogeneity in primary tumors.

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Section: Introductionmentioning

confidence: 99%

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

2022

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“…The chromatin contacts matrix file (.hic) was imported into Juicerbox (version: 1.9.8, https://github.com/aidenlab/Juicebox) software for visualization. The translocations and large structural variations were identified according to the inter-/intra-chromosome interaction patterns ( 15, 57 ). The types and breakpoints of translocations were identified according to the split reads and CNV information.…”

Section: Methodsmentioning

confidence: 99%

Robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing (MGA-Seq)

Lin

Zou

Wang

et al. 2022

Preprint

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Genomic abnormalities, including structural variation (SV), copy number variation (CNV), single-nucleotide polymorphism (SNP), homogenously staining regions (HSR) and extrachromosomal DNA (ecDNA), are strongly associated with cancer, rare diseases and infertility. A robust technology to simultaneously detect these genomic abnormalities is highly desired for clinical diagnosis and basic research. In this study, we developed a simple and cost-effective method - multiple genetic abnormality sequencing (MGA-Seq) - to simultaneously detect SNPs, CNVs, SVs, ecDNA and HSRs in a single tube. This method has been successfully applied in both cancer cell lines and clinical tumour samples and revealed that focal amplification in tumour tissue is substantially heterogeneous. Notably, we delineated the architecture of focal amplification and the ecDNA network by MGA-Seq, which facilitated the exploration of the regulation of gene expression in ecDNA. This method could be extensively applied for diagnosis and may greatly facilitate the investigation of the genomic mechanism for genetic diseases.

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“…Unlike sn-m3C-seq, methyl-HiC includes biotin ligation and pull-down steps 67 . Methyl-HiC is reported to detect 38,827 short-range contacts (<1 kb) and 77,811 long-range contacts (≥1 kb), whereas sn-m3C-seq can detect a significantly greater number of contacts (646,971 short-range and 195,160 long-range on average per cell) 68 . The observed discrepancy might be attributed to differences in the methodologies and protocols used for the two methods.…”

Section: Objectives Of Single-cell Mono-omicsmentioning

confidence: 99%

Advances in single-cell omics and multiomics for high-resolution molecular profiling

Lim,

Park,

Kim

et al. 2024

Exp Mol Med

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Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

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Hi-C as a molecular rangefinder to examine genomic rearrangements

Cited by 10 publications

References 68 publications

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

Robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing (MGA-Seq)

Advances in single-cell omics and multiomics for high-resolution molecular profiling

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