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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 18, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

A practical handbook on single-cell RNA sequencing data quality control and downstream analysis.

Gyeong Dae Kim¹, Chaemin Lim¹, Jihwan Park¹

¹School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.

Molecules and Cells

|August 2, 2024

Summary

High-quality single-cell analysis requires careful cell selection and data processing. This review offers guidelines to improve single-cell transcriptome studies and streamline research.

Keywords:

Downstream analysis Quality control Single-cell RNA sequencing

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Last Updated: Jun 18, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

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Published on: September 16, 2019

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Single-cell analysis offers high-resolution transcriptome insights.
Current standards for cell quality and data analysis are inconsistent.
Variability in methods impacts the reliability of single-cell data.

Purpose of the Study:

To establish guidelines for selecting high-quality cells in single-cell analysis.
To provide considerations for robust data analysis pipelines.
To improve the overall quality and reproducibility of single-cell transcriptome studies.

Main Methods:

Literature review of current single-cell analysis methodologies.
Delineation of best practices for cell isolation and preparation.
Compilation of recommendations for data processing and quality control.

Main Results:

Identification of critical factors for high-quality cell selection.
Framework for consistent data analysis across different studies.
Guidelines to minimize technical variability in single-cell experiments.

Conclusions:

Standardized approaches are crucial for advancing single-cell research.
This review provides a foundational guide for researchers.
Implementing these guidelines will enhance the utility of single-cell transcriptome data.