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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: Jul 27, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Practical bioinformatics pipelines for single-cell RNA-seq data analysis.

Jiangping He¹, Lihui Lin², Jiekai Chen^1,2

¹Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China.

Biophysics Reports

|June 8, 2023

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) analysis involves complex computational workflows. This review offers best-practice guidelines for analyzing scRNA-seq data, aiding researchers in selecting appropriate tools and pipelines.

Keywords:

Practical bioinformatics pipeline Single-cell RNA sequencing (scRNA-seq)scRNA-seq analysis

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Last Updated: Jul 27, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution for cellular analysis.
The proliferation of scRNA-seq data analysis tools presents a challenge for researchers in tool selection and performance comparison.

Purpose of the Study:

To provide a comprehensive overview of the computational analysis workflow for scRNA-seq data.
To offer best-practice guidelines for experimentalists and users updating their analysis pipelines.

Main Methods:

Detailed review of scRNA-seq analysis steps: experimental design, pre-processing, quality control, feature selection, dimensionality reduction, cell clustering, annotation, and downstream analyses.
Inclusion of advanced techniques such as batch correction, trajectory inference, and cell-cell communication analysis.

Main Results:

A structured workflow for scRNA-seq data analysis is presented.
Guidelines for best practices in each stage of the analysis are provided.

Conclusions:

This review serves as a valuable resource for researchers analyzing scRNA-seq data.
It aims to assist users in navigating the complexities of scRNA-seq analysis and optimizing their pipelines.