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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

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

Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.

Vikas Singh¹, Nikhil Kirtipal¹, Byeongsop Song¹

¹School of Life Sciences, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, 61005, Gwangju, South Korea.

Briefings in Bioinformatics

|May 21, 2024

Summary

This summary is machine-generated.

This study introduces an adaptive method for RNA sequencing data normalization, improving upon the trimmed mean of M values (TMM) by estimating the trimming factor M. This novel approach enhances differential expression analysis accuracy.

Keywords:

AUC RNA-seq differential expression jaeckel’s estimator normalization α trimmed mean

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-Seq) data normalization is crucial for accurate downstream analysis.
Current methods like trimmed mean of M values (TMM) and DESeq are widely used.
The heuristic nature of the TMM trimming factor (M) presents a significant limitation.

Purpose of the Study:

To develop an adaptive method for estimating the TMM trimming factor M.
To improve the accuracy and robustness of RNA-Seq data normalization.
To enhance differential gene expression analysis.

Main Methods:

Estimation of the adaptive trimming factor M using Jaeckel's Estimator.
Utilizing each sample as a reference to determine scale factors.
Validation on multiple public datasets (SEQC, MAQC2, MAQC3, PICKRELL) and simulated data.

Main Results:

The proposed adaptive M estimation method demonstrates improved performance.
Outperforms state-of-the-art normalization methods in terms of ROC AUC and differential expression detection.
Robustness shown across varying differential expression percentages and replicate numbers.

Conclusions:

The adaptive estimation of M offers a more reliable approach to TMM normalization.
This method enhances the precision of differential gene expression analysis in RNA-Seq.
Provides a valuable advancement for genomic data analysis pipelines.