Introduction to Single-Cell DNA Methylation Profiling Methods

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Abstract

DNA methylation is an epigenetic mechanism that is related to mammalian cellular differentiation, gene expression regulation, and disease. In several studies, DNA methylation has been identified as an effective marker to identify differences between cells. In this review, we introduce single-cell DNA-methylation profiling methods, including experimental strategies and approaches to computational data analysis. Furthermore, the blind spots of the basic analysis and recent alternatives are briefly described. In addition, we introduce well-known applications and discuss future development.

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Most cited references 113

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

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fastp: an ultra-fast all-in-one FASTQ preprocessor

Shifu Chen, Yanqing Zhou, Yaru Chen … (2018)

Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

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Full-length RNA-seq from single cells using Smart-seq2.

Simone Picelli, Omid R. Faridani, Asa K Björklund … (2014)

Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.

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Author and article information

Contributors

Hae-Ock Lee: Role: Academic Editor

Yeun-Jun Chung: Role: Academic Editor

Journal

Journal ID (nlm-ta): Biomolecules

Journal ID (iso-abbrev): Biomolecules

Journal ID (publisher-id): biomolecules

Title: Biomolecules

Publisher: MDPI

ISSN (Electronic): 2218-273X

Publication date (Electronic): 10 July 2021

Publication date Collection: July 2021

Volume: 11

Issue: 7

Electronic Location Identifier: 1013

Affiliations

[1 ]Department of Chemistry, Yonsei University, Seoul 03722, Korea; jongsungahn33@ 123456gmail.com (J.A.); 2011133049y@ 123456gmail.com (S.H.)

[2 ]Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, Seoul 02447, Korea

[3 ]Department of Biomedical Science and Technology, Kyung Hee University, Seoul 02447, Korea

Author notes

[* ]Correspondence: hyunihyuni@ 123456khu.ac.kr (J.L.); duheebang@ 123456yonsei.ac.kr (D.B.)

[†]

These authors equally contributed to this work.

Author information

Sunghoon Heo https://orcid.org/0000-0003-2878-6108

Article

Publisher ID: biomolecules-11-01013

DOI: 10.3390/biom11071013

PMC ID: 8301785

PubMed ID: 34356635

SO-VID: cbfc9dd6-3fbc-42d8-924a-2135fe458296

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

Introduction to Single-Cell DNA Methylation Profiling Methods

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RExPO Conference Series

Most cited references 113

Trimmomatic: a flexible trimmer for Illumina sequence data

fastp: an ultra-fast all-in-one FASTQ preprocessor

Full-length RNA-seq from single cells using Smart-seq2.

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