scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data

被引:3
|
作者
Zhu, Hao [1 ]
Liu, Tong [1 ]
Wang, Zheng [2 ,3 ]
机构
[1] Univ Miami, Dept Comp Sci, Coral Gables, FL USA
[2] Univ Miami, Dept Comp Sci, Dept Biol, Coral Gables, FL 33146 USA
[3] Univ Miami, Sylvester Comprehens Canc Ctr, Coral Gables, FL 33146 USA
基金
美国国家卫生研究院;
关键词
single-cell DNA methylation prediction; single-cell Hi-C; graph transformer; CHROMOSOME; GENOME; PRINCIPLES; DOMAINS;
D O I
10.1093/bib/bbad223
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Recently a biochemistry experiment named methyl-3C was developed to simultaneously capture the chromosomal conformations and DNA methylation levels on individual single cells. However, the number of data sets generated from this experiment is still small in the scientific community compared with the greater amount of single-cell Hi-C data generated from separate single cells. Therefore, a computational tool to predict single-cell methylation levels based on single-cell Hi-C data on the same individual cells is needed. We developed a graph transformer named scHiMe to accurately predict the base-pair-specific (bp-specific) methylation levels based on both single-cell Hi-C data and DNA nucleotide sequences. We benchmarked scHiMe for predicting the bp-specific methylation levels on all of the promoters of the human genome, all of the promoter regions together with the corresponding first exon and intron regions, and random regions on the whole genome. Our evaluation showed a high consistency between the predicted and methyl-3C-detected methylation levels. Moreover, the predicted DNA methylation levels resulted in accurate classifications of cells into different cell types, which indicated that our algorithm successfully captured the cell-to-cell variability in the single-cell Hi-C data. scHiMe is freely available at http://dna.cs.miami.edu/scHiMe/.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Single-cell Hi-C reveals cell-to-cell variability in chromosome structure
    Takashi Nagano
    Yaniv Lubling
    Tim J. Stevens
    Stefan Schoenfelder
    Eitan Yaffe
    Wendy Dean
    Ernest D. Laue
    Amos Tanay
    Peter Fraser
    Nature, 2013, 502 : 59 - 64
  • [22] Single-cell Hi-C data enhancement with deep residual and generative adversarial networks
    Wang, Yanli
    Guo, Zhiye
    Cheng, Jianlin
    BIOINFORMATICS, 2023, 39 (08)
  • [23] Single-cell Hi-C: how modeling can augment experiment?
    Mali, Samira
    Tolokh, Igor S.
    Sharakhov, Igor V.
    Onufriev, Alexey V.
    BIOPHYSICAL JOURNAL, 2022, 121 (03) : 362A - 362A
  • [24] A mini-review of single-cell Hi-C embedding methods
    Ma, Rui
    Huang, Jingong
    Jiang, Tao
    Ma, Wenxiu
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2024, 23 : 4027 - 4035
  • [25] scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data
    Liu, Tong
    Wang, Zheng
    BIOINFORMATICS, 2018, 34 (06) : 1046 - 1047
  • [26] Enhancing Single-Cell and Bulk Hi-C Data Using a Generative Transformer Model
    Gao, Ruoying
    Ferraro, Thomas N.
    Chen, Liang
    Zhang, Shaoqiang
    Chen, Yong
    BIOLOGY-BASEL, 2025, 14 (03):
  • [27] ScHiCAtt: Enhancing single-cell Hi-C data resolution using attention-based models
    Menon, Rohit
    Chowdhury, H. M. A. Mohit
    Oluwadare, Oluwatosin
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2025, 27 : 978 - 991
  • [28] ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data
    Fan, Shichen
    Dang, Dachang
    Gao, Lin
    Zhang, Shihua
    ADVANCED SCIENCE, 2024, 11 (42)
  • [29] Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization
    Wolff, Joachim
    Rabbani, Leily
    Gilsbach, Ralf
    Richard, Gautier
    Manke, Thomas
    Backofen, Rolf
    Gruening, Bjoern A.
    NUCLEIC ACIDS RESEARCH, 2020, 48 (W1) : W177 - W184
  • [30] SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data
    Miao Yu
    Armen Abnousi
    Yanxiao Zhang
    Guoqiang Li
    Lindsay Lee
    Ziyin Chen
    Rongxin Fang
    Taylor M. Lagler
    Yuchen Yang
    Jia Wen
    Quan Sun
    Yun Li
    Bing Ren
    Ming Hu
    Nature Methods, 2021, 18 : 1056 - 1059