单细胞DNA甲基化测序数据处理流程与分析方法

doi:10.16288/j.yczz.24-154

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Hereditas(Beijing) ›› 2024, Vol. 46 ›› Issue (10): 807-819.doi: 10.16288/j.yczz.24-154

• Review • Previous Articles Next Articles

Processing pipelines and analytical methods for single-cell DNA methylation sequencing data

Yanni Wang¹(), Jia Li¹^,²^,³()

1. Guangzhou Laboratory, Guangzhou 510200, China
2. State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou 510200, China
3. Cancer Research Institute, Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou 510200, China

Received:2024-05-30 Revised:2024-08-30 Online:2024-09-12 Published:2024-09-12
Contact: Jia Li E-mail:wang_yanni@gzlab.ac.cn;li_jia@gzlab.ac.cn
Supported by:
National Natural Science Foundation of China(82370148);Rural and Social Development Science and Technology Office/Guangzhou Medical University/Guangzhou Medical University First Affiliated Hospital Key R&D Plan(2024B03J0046);Guangzhou National Laboratory Special Project(GZNL2023A02003);Independent Open Subject Project of the State Key Laboratory of Respiratory Diseases(J19112006202302);Guangzhou Medical University/Affiliated Cancer Hospital Scientific Research Capacity Improvement Project(02-410-2302244XM);Independent Research Fund of the State Key Laboratory of Respiratory Diseases(SKLRD-Z-202307)

Abstract

Abstract:

Single-cell DNA methylation sequencing technology has seen rapid advancements in recent years, playing a crucial role in uncovering cellular heterogeneity and the mechanisms of epigenetic regulation. As sequencing technologies have progressed, the quality and quantity of single-cell methylation data have also increased, making standardized preprocessing workflows and appropriate analysis methods essential for ensuring data comparability and result reliability. However, a comprehensive data analysis pipeline to guide researchers in mining existing data has yet to be established. This review systematically summarizes the preprocessing steps and analysis methods for single-cell methylation data, introduces relevant algorithms and tools, and explores the application prospects of single-cell methylation technology in neuroscience, hematopoietic differentiation, and cancer research. The aim is to provide guidance for researchers in data analysis and to promote the development and application of single-cell methylation sequencing technology.

Key words: single-cell methylation sequencing, epigenetics, data preprocessing, data analysis

Yanni Wang, Jia Li. Processing pipelines and analytical methods for single-cell DNA methylation sequencing data[J]. Hereditas(Beijing), 2024, 46(10): 807-819.

Figures/Tables 3

Table 1

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技术名称	比对率	一次处理的通量	每个细胞唯一读数数目(测序深度)	CpGs位点	亚硫酸氢盐转化率	优点
Drop-BS	65%	2天10,000细胞	41,013 (231,382)	24,230	99%	短时间可处理大量细胞
snmC-seq	55%	每板384细胞	1.7 M(6.9 M)	1.7 M	99%	-
snmC-seq2	65%	结合机器对6144细胞混合测序	-	-	99%	覆盖均匀
sciMET	60%	每次实验500~700细胞	186,710 (755,529)	162,045	99%	-
sciMET v2	69%	每次实验1000细胞	3.1 M(4.9 M)	2.2 M	99%	成本低
scBS-seq	20%	文章中测序44细胞	3.0 M(19.4 M)	3.7 M	98.5%	测序深度高

去接头工具名	优点	是否支持多线程	来源
Cutadapt	适用范围广，应用较为广泛	是	https://github.com/marcelm/cutadapt
TrimGalore	集成了FastQC的功能，有专门用于RRBS数据的模式	是	https://github.com/FelixKrueger/TrimGalore
Fastp	集成了FastQC和Trimmomatic的功能，处理速度快、内存占用低	是	https://github.com/OpenGene/fastp
Trimmomatic	灵活、高效	是	https://github.com/timflutre/trimmomatic

Processing pipelines and analytical methods for single-cell DNA methylation sequencing data

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