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 Experiment Videos

From 'omes to biology.

J Quackenbush1

  • 1Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA. johnq@jimmy.harvard.edu

Animal Genetics
|August 5, 2006
PubMed
Summary
This summary is machine-generated.

New strategies are needed to analyze large biological datasets. Integrating data from genomics, proteomics, and metabolomics experiments with ancillary information provides a successful approach for biological data analysis.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Computational approaches to analysis of DNA microarray data.

Yearbook of medical informatics·2006
Same author

RESOURCERER: a database for annotating and linking microarray resources within and across species.

Genome biology·2005
Same author

The transcriptional landscape of the mammalian genome.

Science (New York, N.Y.)·2005
Same author

Grape-seed derived procyanidins interfere with adipogenesis of 3T3-L1 cells at the onset of differentiation.

International journal of obesity (2005)·2005
Same author

BmiGI: a database of cDNAs expressed in Boophilus microplus, the tropical/southern cattle tick.

Insect biochemistry and molecular biology·2005
Same author

The TIGR Gene Indices: clustering and assembling EST and known genes and integration with eukaryotic genomes.

Nucleic acids research·2004
Same journal

Genome-Wide Association Study and Genomic Prediction for Maximum Litter Size in Sows.

Animal genetics·2026
Same journal

Transcriptomic Analysis Reveals Multilayer Regulation Underlying Development and Pregnancy in the Malayan Pangolin.

Animal genetics·2026
Same journal

Gestational Feed Restriction Induces Differential Expression of Novel Long Non-Coding RNAs in Offspring Skeletal Muscle.

Animal genetics·2026
Same journal

A NECAP1 Nonsense Variant is Associated With Leukoencephalomyelopathy With Oligodendroglial Dysplasia in a Belgian Malinois With Spinocerebellar Ataxia.

Animal genetics·2026
Same journal

Enrichment Analysis of Differentially Expressed Genes During Feather Regeneration in Landes Geese (Anser anser).

Animal genetics·2026
Same journal

Multi-Omics Analysis Reveals Impacts of SCARB1, TYR, and TYRP1 Knockouts on Pigmentation and Metabolic Pathways in Oujiang Color Common Carp.

Animal genetics·2026
See all related articles

Area of Science:

  • Genomics
  • Proteomics
  • Metabolomics
  • Systems Biology

Background:

  • Genome project technologies have accelerated biological data generation.
  • Analyzing large-scale biological datasets (genomics, proteomics, metabolomics) presents significant challenges.
  • Current data analysis capabilities lag behind data generation rates.

Purpose of the Study:

  • To address the analytical challenges of large biological datasets.
  • To present a novel strategy for integrating diverse biological data.
  • To demonstrate the application of this integrated approach using case studies.

Main Methods:

  • Treating omics data (microarrays, proteomics, metabolomics) as interconnected elements within a larger biological information framework.

Related Experiment Videos

  • Integrating independent datasets across various biological scales.
  • Interpreting data within the context of ancillary biological information.

    • Main Results:

    • Successful application of the integrated data analysis strategy in three published studies.
    • Demonstrated ability to interpret complex biological responses, developmental processes, and disease alterations.
    • Enhanced understanding through contextualization of omics data.

    Conclusions:

    • Integrating diverse biological datasets and ancillary information is a powerful strategy for data analysis.
    • This approach enhances the interpretation of complex biological phenomena.
    • The outlined strategy offers a robust framework for advancing biological research.