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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: Jun 3, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Highly effective batch effect correction method for RNA-seq count data.

¹Department of Computer Science and Information Science, California State University San Marcos, 333 S. Twin Oaks Valley Rd, San Marcos, CA 92096, USA.

Computational and Structural Biotechnology Journal

|January 13, 2025

Summary

This summary is machine-generated.

Batch effects in RNA sequencing (RNA-seq) data can obscure true biological signals. ComBat-ref is a new method that corrects these variations, improving the accuracy and reliability of gene expression analysis.

Keywords:

Batch effect correction Generalized linear model Minimum dispersion Negative binomial distribution RNA-seq data

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Last Updated: Jun 3, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

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

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Published on: September 16, 2019

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

Area of Science:

Transcriptomics
Bioinformatics
Genomics

Background:

RNA sequencing (RNA-seq) is crucial for gene expression analysis.
Batch effects are non-biological variations that reduce RNA-seq data reliability.
Existing methods struggle to effectively correct batch effects while preserving biological signals.

Purpose of the Study:

Introduce ComBat-ref, an advanced batch effect correction method for RNA-seq data.
Enhance the statistical power and reliability of differential expression analysis.
Improve the accuracy and interpretability of transcriptomic studies.

Main Methods:

ComBat-ref refines the ComBat-seq approach using a negative binomial model.
It selects a reference batch with minimal dispersion and adjusts other batches towards it.
The method preserves count data from the reference batch.

Main Results:

ComBat-ref demonstrated superior performance in simulated and real-world datasets.
The method significantly improved sensitivity and specificity in differential expression analysis.
Validated on growth factor receptor network (GFRN) and NASA GeneLab data.

Conclusions:

ComBat-ref effectively mitigates batch effects in RNA-seq data.
The method maintains high detection power, enhancing data accuracy.
ComBat-ref offers a robust solution for reliable transcriptomic analysis.