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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jun 9, 2025

Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain

Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain

Published on: July 12, 2012

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

Yan-Ni Wang¹, Jia Li^1,2,3

¹Guangzhou Laboratory, Guangzhou 510200, China.

Yi Chuan = Hereditas

|October 23, 2024

Summary

This summary is machine-generated.

Single-cell DNA methylation sequencing offers insights into cellular differences and epigenetics. This review provides a guide to analyzing this data, covering preprocessing, methods, and applications in key research areas.

Keywords:

data analysis data preprocessing epigenetics single-cell methylation sequencing

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Last Updated: Jun 9, 2025

Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain

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Published on: July 12, 2012

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Published on: October 31, 2016

Area of Science:

Epigenetics and Genomics
Computational Biology
Molecular Biology

Background:

Single-cell DNA methylation sequencing is advancing rapidly, revealing cellular heterogeneity and epigenetic regulation mechanisms.
Increased data quality and quantity necessitate standardized analysis workflows for reliable results.
A comprehensive pipeline for analyzing single-cell methylation data is currently lacking.

Purpose of the Study:

To systematically review preprocessing steps and analysis methods for single-cell methylation data.
To introduce relevant algorithms and tools for single-cell methylation data analysis.
To explore application prospects in neuroscience, hematopoietic differentiation, and cancer research.

Main Methods:

Systematic literature review of single-cell DNA methylation sequencing data analysis.
Summary of preprocessing techniques and computational methods.
Identification and description of existing bioinformatics tools and algorithms.

Main Results:

Detailed overview of essential preprocessing steps for single-cell methylation data.
Categorization and explanation of various computational analysis approaches.
Exploration of current and potential applications across diverse biological fields.

Conclusions:

Standardized analysis pipelines are crucial for maximizing the utility of single-cell methylation data.
This review provides essential guidance for researchers navigating complex data analysis.
The technology holds significant promise for advancing neuroscience, hematopoiesis, and cancer research.