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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: Dec 28, 2025

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review.

Xueyan Liu¹, Nan Li², Sheng Liu¹

¹Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China.

Frontiers in Bioengineering and Biotechnology

|February 11, 2020

Summary

This summary is machine-generated.

This review categorizes normalization methods for skewed gene expression data based on reference selection. It offers a comprehensive overview of preprocessing algorithms for unbalanced transcriptome data.

Keywords:

RNA-seq microarray normalization regression subset reference transcriptome

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Last Updated: Dec 28, 2025

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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Published on: June 27, 2020

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

High-throughput expression data analysis requires robust normalization to correct experimental variation and bias.
Numerous normalization methods exist, with many addressing data skewness, surpassing conventional techniques like loess and quantile normalization.

Purpose of the Study:

To classify and review normalization methods for skewed gene expression data based on reference selection strategies.
To provide a comprehensive summary of preprocessing algorithms for unbalanced transcriptome data from both microarray and RNA-seq.

Main Methods:

Classification of normalization methods into three categories: data-driven reference, foreign reference, and entire gene set.
Introduction and summarization of methods designed for gene expression data exhibiting global shifts between conditions.

Main Results:

Normalization methods for skewed expression data were categorized based on reference selection.
A comprehensive review of preprocessing algorithms for unbalanced transcriptome data was presented, covering both microarray and RNA-seq.

Conclusions:

This review provides a structured understanding of normalization methods for skewed gene expression data.
The summarization aids in the appropriate selection and application of preprocessing techniques for transcriptome data analysis.