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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

You might also read

Related Articles

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

Sort by
Same author

Use of double-J ureteric stents post-laparoscopic pyeloplasty to treat ureteropelvic junction obstruction in hydronephrosis for pediatric patients: a single-center experience.

The Journal of international medical research·2020
Same author

Preferential Expression of B7-H6 in Glioma Stem-Like Cells Enhances Tumor Cell Proliferation via the c-Myc/RNMT Axis.

Journal of immunology research·2020
Same author

Identification of microRNAs and their Endonucleolytic Cleavaged target mRNAs in colorectal cancer.

BMC cancer·2020
Same author

Ligustrazine ameliorates lipopolysaccharide‑induced neurocognitive impairment by activating autophagy via the PI3K/AKT/mTOR pathway.

International journal of molecular medicine·2020
Same author

Over-Expression of the Heat-Responsive Wheat Gene <i>TaHSP23.9</i> in Transgenic <i>Arabidopsis</i> Conferred Tolerance to Heat and Salt Stress.

Frontiers in plant science·2020
Same author

The different expression of glycogen phosphorylases in renal clear cell renal carcinoma and chromophobe renal carcinoma.

Clinical proteomics·2020
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

Related Experiment Video

Updated: Jun 17, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Disease gene characterization through large-scale co-expression analysis.

Allen Day1, Jun Dong, Vincent A Funari

  • 1Department of Human Genetics, Molecular Biology Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.

Plos One
|January 5, 2010
PubMed
Summary
This summary is machine-generated.

The UCLA Gene Expression Tool (UGET) leverages the vast Celsius dataset to identify novel genes and prioritize disease-causing genes, significantly aiding genetic research.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Related Experiment Videos

Last Updated: Jun 17, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

  • Genomics
  • Bioinformatics
  • Gene Expression Analysis

Background:

  • Post-genome era research focuses on identifying specific gene functions.
  • A new genomic tool, the UCLA Gene Expression Tool (UGET), is introduced for gene characterization.

Purpose of the Study:

  • To develop and validate a novel genomic tool (UGET) for gene characterization and prioritization.
  • To assess UGET's capability in identifying novel tissue-selective genes and disease-causing genes.

Main Methods:

  • Utilized the Celsius microarray dataset for co-normalization and gene pair correlation analysis.
  • Developed web-searchable indexes for efficient gene querying.
  • Tested UGET with known cartilage-selective genes and in linkage intervals for genetic disorders.
  • Compared UGET's performance against other gene expression-based prioritization tools.

Main Results:

  • UGET identified 32 new cartilage-selective genes, with 70% validated by qPCR (e.g., SDK2, FLJ41170).
  • Demonstrated UGET's utility in identifying disease-causing genes for disorders like Joubert syndrome and LGMD2.
  • Observed significantly higher gene correlation within disease networks for similar disorders.

Conclusions:

  • UGET is a valuable resource for geneticists, enabling rapid integration of expression data for gene prioritization in disease-linked genomic intervals.
  • UGET outperforms other tools, particularly for rare tissue disorders and complex biological processes, by analyzing thousands of arrays.
  • The tool is critical for prioritizing candidate genes for sequence analysis in genetic research.