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...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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.
Genomic DNA in Eukaryotes00:58

Genomic DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

You might also read

Related Articles

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

Sort by
Same author

Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease.

bioRxiv : the preprint server for biology·2026
Same author

A shared genetic regulator of metabolism and addiction-related behavior in mice and humans.

bioRxiv : the preprint server for biology·2026
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

Correction to 'New origin firing is inhibited by APC/CCdh1 activation in S-phase after severe replication stress'.

Nucleic acids research·2026
Same journal

VeloRM: disentangling pre- and post-splicing RNA modification dynamics at single-cell resolution.

Nucleic acids research·2026
Same journal

Accessibility of telomeric overhangs to stabilizing small-molecule ligands.

Nucleic acids research·2026
Same journal

Multivalent interactions mediate SNAIL transcription factor stimulation of the nucleosome deacetylase activity of the CoREST complex.

Nucleic acids research·2026
Same journal

Genome-wide mapping of DNA G-quadruplexes in Trypanosoma brucei chromatin reveals enrichment in coding regions and transcription start sites.

Nucleic acids research·2026
Same journal

Correction to 'The Gene Ontology knowledgebase in 2026'.

Nucleic acids research·2026
See all related articles

Related Experiment Video

Updated: May 27, 2026

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers
08:12

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers

Published on: July 18, 2025

GeneWeaver: a web-based system for integrative functional genomics.

Erich J Baker1, Jeremy J Jay, Jason A Bubier

  • 1School of Engineering & Computer Science, Baylor University, Waco, TX 76798, USA.

Nucleic Acids Research
|November 15, 2011
PubMed
Summary
This summary is machine-generated.

GeneWeaver integrates diverse genomic experimental data, enabling biologists to uncover gene-function associations. This approach facilitates discovery of biological relationships from aggregated experimental knowledge.

More Related Videos

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Related Experiment Videos

Last Updated: May 27, 2026

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers
08:12

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers

Published on: July 18, 2025

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • High-throughput genome technologies generate vast data linking genes to biological functions.
  • Integrating disparate genomic experimental results (e.g., GWAS, RNA-seq) is challenging for researchers.
  • Current data integration methods are complex, unscalable, and require extensive computational analysis.

Purpose of the Study:

  • To present GeneWeaver, a platform for integrating diverse genomic experimental results.
  • To enable interactive exploration of gene-function associations across multiple studies.
  • To facilitate the discovery of novel biological relationships from aggregated genomic data.

Main Methods:

  • GeneWeaver utilizes the Ontological Discovery Environment for data integration.
  • It provides a curated repository of genomic experimental results.
  • Users can interactively query and analyze integrated datasets to explore gene-function relationships.

Main Results:

  • GeneWeaver allows dynamic integration of data from various genomic studies.
  • It enables users to address complex questions about biological functions and gene sets.
  • The platform facilitates the discovery of empirical 'ontologies' from experimental knowledge.

Conclusions:

  • GeneWeaver offers a scalable solution for integrating and analyzing diverse genomic data.
  • It empowers researchers to uncover gene-function associations and biological insights.
  • This approach shifts from pre-defined frameworks to data-driven discovery of biological relationships.