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

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...

You might also read

Related Articles

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

Sort by

Same author

MST4: A Potential Oncogene and Therapeutic Target in Breast Cancer.

Cells·2022

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 24, 2025

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

A Zipf-plot based normalization method for high-throughput RNA-seq data.

¹Department of Mathematics and Statistics, University of South Alabama, Mobile, AL, United States of America.

|April 10, 2020

Summary

This summary is machine-generated.

A new Zipf plot based normalization (ZN) method addresses challenges in RNA-seq data analysis, particularly with excessive zeros. This robust method improves normalization outcomes for gene expression data, even with low or no expression.

More Related Videos

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

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

Related Experiment Videos

Last Updated: Dec 24, 2025

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

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
Computational Biology
Genomics

Background:

RNA-sequencing (RNA-seq) data analysis requires normalization due to challenges like excessive zeros and small expression values.
Existing normalization methods struggle to identify reliable parameters, especially when many genes have low or no expression.

Purpose of the Study:

To introduce a novel Zipf plot based normalization (ZN) method for RNA-seq data.
To address the limitations of current normalization techniques in handling zero-inflated and low-expression gene profiles.

Main Methods:

Developed a Zipf plot based normalization (ZN) method utilizing the assumption of similar upper tail behaviors in gene expression distributions.
Implemented two normalization schemes within ZN: a linear rescaling scheme and a non-linear transformation scheme.
Benchmarked ZN against five popular linear normalization methods for RNA-seq data.

Main Results:

The linear rescaling scheme of ZN demonstrated robust and effective normalization performance.
The non-linear normalization scheme further enhanced normalization outcomes, proving optional when Zipf plots exhibit parallel patterns.
ZN is applicable to datasets with a high proportion of weakly or non-expressed genes.

Conclusions:

The Zipf plot based normalization (ZN) method offers a reliable approach for RNA-seq data normalization, outperforming existing methods in specific scenarios.
ZN effectively handles challenges posed by excessive zeros and low gene expression, improving the accuracy of downstream analyses.
The method's flexibility with linear and non-linear schemes allows for tailored normalization strategies based on data characteristics.