Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Extreme value theory for individuals control charts: a semiparametric approach to ensuring in-control performance.

Journal of applied statistics·2026

Same author

Obesity-Related Metabolites are Associated with Incident Coronary Heart Disease and Respond to Metabolic and Bariatric Surgery.

medRxiv : the preprint server for health sciences·2026

Same author

ImmuSeeker: deep mining of immune-related gene family signatures through lineage reconstruction.

Genome biology·2026

Same author

<i>BDNF</i> Val<sup>66</sup>Met protects oxaliplatin-induced peripheral neuropathy in patients with colorectal cancer.

Science translational medicine·2026

Same author

Physiological foundation modeling for subclinical disease assessment: a prospective pilot.

JAMIA open·2026

Same author

Body Composition Changes After Bariatric Surgery or Treatment With GLP-1 Receptor Agonists.

JAMA network open·2026

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

Statistical strategies for microRNAseq batch effect reduction.

Yan Guo¹, Shilin Zhao¹, Pei-Fang Su²

¹Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, Nashville, TN 37232, USA.

Translational Cancer Research

|September 27, 2014

Summary

This summary is machine-generated.

Batch effect adjustment is crucial for reliable RNA sequencing (RNAseq) data analysis, especially in microRNA sequencing (miRNAseq) studies. Our findings demonstrate that correcting for batch effects improves the identification of differentially expressed genes.

Keywords:

batch effect removal miRNA sequencing normalization

More Related Videos

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

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

Published on: September 16, 2019

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Published on: November 18, 2014

Related Experiment Videos

Last Updated: Apr 23, 2026

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

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

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

Published on: September 16, 2019

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Published on: November 18, 2014

Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

RNA sequencing (RNAseq) offers richer gene expression data than microarrays but presents analytical challenges.
Batch effects significantly impact RNAseq data, particularly in microRNA sequencing (miRNAseq).
Accurate adjustment for batch effects is critical for large-scale RNAseq studies.

Purpose of the Study:

To evaluate batch removal techniques for miRNAseq data.
To demonstrate the impact of batch effect adjustment on identifying differentially expressed genes.
To provide guidance for future miRNAseq studies encountering batch effects.

Main Methods:

Utilized real miRNA sequencing (miRNAseq) datasets.
Assessed the effectiveness of various batch removal methods.
Compared results with and without batch effect correction.

Main Results:

Batch effect adjustment enhances the reliability of differential gene expression analysis in miRNAseq.
Identified specific techniques that effectively mitigate batch effects.
Showcased improved identification of significant differentially expressed genes post-correction.

Conclusions:

Batch effect correction is essential for robust miRNAseq data analysis.
The study provides a practical guideline for handling batch effects in future miRNAseq research.
Implementing appropriate batch removal techniques leads to more trustworthy biological insights.