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...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statgraphics01:10

Statgraphics

Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...

You might also read

Related Articles

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

Sort by
Same author

Genomic science and the nurse educator's role: Promoting integration from curriculum to clinical practice.

Nurse education today·2026
Same author

Retrospective Cohort Study of Palliative Care Patterns in Advanced Pancreatic Ductal Adenocarcinoma.

ANZ journal of surgery·2026
Same author

Monitoring rapid degradation of NANOG reveals UTP15 maintains pluripotency by regulating nascent transcripts.

Nature communications·2025
Same author

Transomic analysis reveals DNA methylation and transcription factor roles in obese liver protein expression.

NPJ systems biology and applications·2025
Same author

Rakeiora Genomics Platform: a pathfinder for genomic medicine research in Aotearoa New Zealand.

Journal of the Royal Society of New Zealand·2025
Same author

Assigning Targetable Molecular Pathways to Transdiagnostic Subgroups Across Autism and Related Neurodevelopmental Disorders.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: May 11, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

GeneSetDB: A comprehensive meta-database, statistical and visualisation framework for gene set analysis.

Hiromitsu Araki1, Christoph Knapp, Peter Tsai

  • 1Department of Molecular Medicine & Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, The University of Auckland, Private Bag 92019, Auckland, New Zealand.

FEBS Open Bio
|May 8, 2013
PubMed
Summary
This summary is machine-generated.

GeneSetDB integrates 26 databases for gene set analysis, aiding interpretation of "omics" data. This meta-database enhances biological insights, especially for human disease and pharmacology research.

Keywords:
DatabaseEnrichment analysisFunctional genomicsGSA, gene set analysisGene set

More Related Videos

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Related Experiment Videos

Last Updated: May 11, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Omics experiments generate large gene lists requiring biological interpretation.
  • Gene set analysis (GSA) tools aid interpretation but are limited by the breadth of available gene sets.
  • Few methods allow simultaneous analysis of multiple public gene set databases.

Purpose of the Study:

  • To construct a comprehensive meta-database integrating diverse public gene set resources.
  • To facilitate the analysis of gene lists across multiple databases, focusing on human disease and pharmacology.
  • To provide a centralized resource for gene set enrichment analysis.

Main Methods:

  • Developed GeneSetDB, a meta-database integrating 26 public databases.
  • Implemented functionalities for searching gene sets by identifier or keyword.
  • Enabled users to generate custom gene sets and perform enrichment analysis on uploaded gene lists.
  • Incorporated visualization tools, including clustered heat maps, for gene set enrichment and overlap.

Main Results:

  • GeneSetDB successfully integrates 26 diverse public gene set databases.
  • The platform allows comprehensive searching, custom gene set creation, and statistical enrichment testing.
  • Visualizations aid in understanding gene set enrichment and overlap patterns.

Conclusions:

  • GeneSetDB provides a valuable, integrated resource for gene set analysis in "omics" research.
  • The meta-database enhances biological interpretation, particularly in human disease and pharmacology.
  • Facilitates more robust and comprehensive GSA by leveraging multiple data sources.