Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

RNA-seq

9.9K
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...
9.9K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K

You might also read

Related Articles

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

Sort by
Same author

NTAQ1 Promotes Hepatocellular Carcinoma Growth by Facilitating the Protein Degradation of the Tumor Suppressor PRDM2.

Cancer science·2026
Same author

Epidermoid Cysts in an Intrapancreatic Accessory Spleen Mimicking Pancreatic Neoplasms: Two Case Reports and a Literature Review of Japanese Cases.

Surgical case reports·2026
Same author

Novel driver gene FIRRM regulates the cell cycle for promoting tumor growth in hepatocellular carcinoma.

Journal of gastroenterology·2026
Same author

Determination of the preliminary discriminating concentration of broflanilide against malaria vector mosquito Anopheles gambiae by multi-centre susceptibility testing.

Malaria journal·2026
Same author

Multicenter ex vivo evaluation of an α-mannosidase-targeted fluorescent probe for intraoperative margin assessment in breast cancer.

Scientific reports·2026
Same author

Identification of the Inflammatory Cytokine Tumor Necrosis Factor Superfamily 4 as an Oncogenic Driver and Potential Druggable Target in Hepatocellular Carcinoma.

Hepatology research : the official journal of the Japan Society of Hepatology·2026

Related Experiment Video

Updated: Jun 18, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

37.1K

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering.

Yuki Ando1, Asanao Shimokawa2

  • 126413 Tokyo University of Science , Shinjuku-ku, 162-8601, Tokyo, Japan.

The International Journal of Biostatistics
|July 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fuzzy clustering method for identifying differentially expressed genes (DEGs) in RNA sequencing data, improving accuracy over traditional tests, especially with small or biased sample sizes.

Keywords:
DEGsexpression levelfold-changetwo group comparison

More Related Videos

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

12.0K
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.3K

Related Experiment Videos

Last Updated: Jun 18, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

37.1K
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

12.0K
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.3K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential gene expression analysis is crucial for RNA sequencing (RNA-Seq) data interpretation.
  • Conventional two-group comparison tests for detecting differentially expressed genes (DEGs) suffer from low accuracy with small sample sizes.
  • Existing methods often struggle with imbalanced group sizes in gene expression studies.

Purpose of the Study:

  • To develop a novel, test-free method for identifying differentially expressed genes (DEGs) using fuzzy clustering.
  • To enhance the accuracy of DEG detection in RNA sequencing data, particularly when sample sizes are small or biased.
  • To demonstrate the robustness and superiority of the proposed fuzzy clustering approach compared to conventional methods.

Main Methods:

  • A fuzzy clustering approach is proposed to artificially generate expression data mimicking DEGs.
  • Genes are identified based on their likelihood of belonging to the same cluster as initial data.
  • The method avoids traditional statistical testing, focusing on pattern recognition and clustering.

Main Results:

  • The proposed fuzzy clustering method demonstrates superior accuracy in identifying DEGs across all simulated scenarios.
  • Accuracy is maintained even with biased sample sizes, and in some cases, bias can improve performance.
  • The impact of expression level differences between groups on accuracy is more pronounced with biased sample sizes.

Conclusions:

  • Fuzzy clustering offers a robust and accurate alternative for detecting differentially expressed genes (DEGs) in RNA sequencing data.
  • The method excels in scenarios with limited or imbalanced sample sizes, outperforming conventional statistical tests.
  • This approach provides a valuable tool for genomic data analysis, enhancing the reliability of DEG identification.