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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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Related Experiment Video

Updated: Nov 9, 2025

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

Published on: May 5, 2018

SMIXnorm: Fast and Accurate RNA-Seq Data Normalization for Formalin-Fixed Paraffin-Embedded Samples.

Shen Yin^1,2, Xiaowei Zhan¹, Bo Yao¹

¹Department of Population and Data Sciences, Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX, United States.

Frontiers in Genetics

|April 12, 2021

Summary

A new RNA-sequencing (RNA-seq) normalization method, SMIXnorm, offers faster computation for Formalin-Fixed Paraffin-Embedded (FFPE) samples. The RSeqNorm tool integrates SMIXnorm and other methods for both FFPE and fresh frozen (FF) RNA-seq data analysis.

Keywords:

FFPE RNA-sequencing archived samples formalin-fixed paraffin-embedded samples normalization statistical methods

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Last Updated: Nov 9, 2025

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

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Published on: May 5, 2018

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA-sequencing (RNA-seq) is crucial for quantifying transcriptomic activity.
Formalin-Fixed Paraffin-Embedded (FFPE) samples are abundant but require specific normalization for RNA-seq analysis.
Existing FFPE normalization methods, like MIXnorm, are computationally intensive.

Purpose of the Study:

To develop a computationally efficient normalization method for FFPE RNA-seq data.
To create an integrated tool for normalizing both FFPE and fresh frozen (FF) RNA-seq data.
To simplify and improve the accessibility of RNA-seq data normalization.

Main Methods:

Developed SMIXnorm, a simplified two-component mixture model for FFPE RNA-seq normalization.
Employed a nested Expectation-Maximization algorithm for parameter estimation.
Created a web-based tool, RSeqNorm, offering seven normalization methods for FFPE and FF data.

Main Results:

SMIXnorm significantly reduces computation time compared to MIXnorm without compromising performance.
The RSeqNorm tool provides a user-friendly platform for RNA-seq data normalization.
Both SMIXnorm and the RSeqNorm tool are available for public use.

Conclusions:

SMIXnorm offers an efficient and effective normalization solution for FFPE RNA-seq data.
The RSeqNorm tool enhances the accessibility of RNA-seq data normalization for researchers using FFPE and FF samples.
This work facilitates more robust transcriptomic analysis from diverse sample types.