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Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

邮发代号 80-971

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Quantitative Biology  2022, Vol. 10 Issue (1): 55-66   https://doi.org/10.15302/J-QB-022-0289
  本期目录
The progress on the estimation of DNA methylation level and the detection of abnormal methylation
Shicai Fan1,2(), Likun Wang3, Liang Liang4, Xiaohong Cao5, Jianxiong Tang2, Qi Tian2
1. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
2. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
3. Institute of Systems Biomedicine, Beijing Key Laboratory of Tumor Systems Biology, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
4. Cancer Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
5. Department of Geriatric Endocrinology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
 全文: PDF(1346 KB)   HTML
Abstract：

Background: DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes. The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.

Results: In this review, the main high-throughput detection technologies are summarized, and then the three trends of computational estimation of DNA methylation levels were analyzed, especially the expanding of the methylation data with lower coverage. Furthermore, the detection methods of differential methylation patterns for sequencing and array data were presented.

Conclusions: More and more research indicated the great importance of DNA methylation changes across different diseases, such as cancers. Although a lot of enormous progress has been made in understanding the role of DNA methylation, only few methylated genes or functional elements serve as clinically relevant cancer biomarkers. The bottleneck in DNA methylation advances has shifted from data generation to data analysis. Therefore, it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers.
Key words： DNA methylation    genome-wide profiling    computational estimation    single-cell methylome    differential methylation detection
收稿日期: 2021-08-12      出版日期: 2022-03-28
Corresponding Author(s): Shicai Fan   
作者简介:

Peng Lu, Renxing Wang, and Yue Xing contributed equally to this work.
 引用本文:   
. [J]. Quantitative Biology, 2022, 10(1): 55-66.
Shicai Fan, Likun Wang, Liang Liang, Xiaohong Cao, Jianxiong Tang, Qi Tian. The progress on the estimation of DNA methylation level and the detection of abnormal methylation. Quant. Biol., 2022, 10(1): 55-66.
 链接本文:  
https://academic.hep.com.cn/qb/CN/10.15302/J-QB-022-0289
https://academic.hep.com.cn/qb/CN/Y2022/V10/I1/55
Fig.1  
Fig.2  
Method Algorithm Features Original coverage Expanded profiling Tool
Fan et al. [60] SVM a) DNA sequenceb) Neighboring methylation values 450K ~11 folds of 450K /
Fan et al. [61] LR a) DNA sequenceb) Neighboring methylation valuesc) Methylation values from similarity tissues 450K ~18.5 folds of 450K http://wanglab.ucsd.edu/star/LR450K/
Fan et al. [42] LR a) Neighboring methylation valuesb) Methylation values from similarity tissues 450K ~18.9 folds of 450K, more than 10 times faster speed http://114.55.236.67:8013/Integrative_Analysis/home
Zheng et al. [62] RF a) RE CpG density and RE lengthb) Smith-Waterman (SW) scorec) Number of neighboring profiled CpGsd) Genomic region of the target CpG 450K/850K 2.7–3.7 times Alu and about 20% more LINE-1 http://bioconductor.org/packages/release/bioc/html/REMP.html
Li et al. [63] Ensemble model Methylation values in 450K 450K 850K /
Tang et al. [64] LR Methylation feature that shared the most similar methylation pattern with the CpG locus to be predicted 450K 850K https://github.com/JxTang-bioinformatics/PretiMeth
Yu et al. [65] MRM Local methylation profile from both regions and subjects RRBS ~ 300 K CpGs in the promoter regions of chromosome 17 https://github.com/yuft2003/MRM
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