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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 25, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms.

Enze Deng¹, Qingmei Shen², Jingna Zhang³

¹MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, Guangdong Province, China.

Journal of Advanced Research

|May 23, 2024

Summary

Third-generation sequencing (TGS) technologies like PacBio and Oxford Nanopore Technologies (ONT) enable single-cell RNA sequencing (scRNA-seq) to analyze gene splicing and identify novel isoforms. PacBio demonstrates superior accuracy over ONT for transcript identification and allele-specific expression analysis.

Keywords:

Allele-specific gene/isoform Cell type identification Novel isoform Single cell RNA-sequencing Third-generation sequencing

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Last Updated: Jun 25, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

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

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) excels at gene expression quantification but is limited by short read lengths, hindering transcript structure (isoform) analysis.
Third-generation sequencing (TGS) technologies, such as Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), offer long read lengths capable of sequencing intact cDNA molecules.

Purpose of the Study:

To systematically evaluate the performance of ONT and PacBio TGS platforms in single-cell RNA sequencing applications.
To compare the capabilities of TGS-based scRNA-seq with traditional NGS-based scRNA-seq for comprehensive transcriptome analysis.

Main Methods:

Generated ONT and PacBio sequencing data from identical single-cell cDNA libraries with varying cell inputs.
Utilized Next-generation sequencing (NGS) as a control for performance assessment.

Main Results:

Assessed the performance of ONT and PacBio platforms in cell type identification compared to NGS.
Verified the reliability of both TGS platforms in identifying novel isoforms and allele-specific gene/isoform expression.
Provided a systematic evaluation to guide sequencing strategy design for single-cell transcriptome studies.

Conclusions:

TGS-based scRNA-seq extends beyond gene expression analysis to include gene splicing and novel isoform identification, surpassing NGS limitations.
Pacific Biosciences (PacBio) exhibits higher sequencing quality and outperforms Oxford Nanopore Technologies (ONT) in the accuracy of novel transcript identification and allele-specific gene/isoform expression analysis.