Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

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...

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

You might also read

Related Articles

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

Sort by

Same author

Erythrocyte Count, Anemia, and the Human Natural Lifespan Limit: Evidence from the Long Life Family Study.

bioRxiv : the preprint server for biology·2026

Same author

Gene-by-Sleep Duration Interaction for Glycemic Traits in over 480,000 Individuals.

medRxiv : the preprint server for health sciences·2026

Same author

Finding Significant Hits in Networks: a network-based tool for analyzing gene-level P-values to identify significant genes missed by standard methods.

Briefings in bioinformatics·2026

Same author

Large-Scale Gene-Smoking Interactions and Fine Mapping Study Identifies Multiple Novel Blood Pressure Loci in over 1 Million Individuals.

medRxiv : the preprint server for health sciences·2026

Same author

Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants.

Atherosclerosis·2025

Same author

Plasma Ceramide C24:0/C16:0 Relates to Outcomes in Patients With Heart Failure With Preserved Ejection Fraction.

JACC. Advances·2025

Search research articles

Related Experiment Video

Updated: May 14, 2026

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

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

Published on: May 17, 2019

A correlated meta-analysis strategy for data mining "OMIC" scans.

Michael A Province¹, Ingrid B Borecki

¹Division of Statistical Genomics, Washington University School of Medicine, Box 8506, 4444 Forest Park Blvd, St. Louis, MO 63105, USA. mprovince@wustl.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 21, 2013

Summary

This study introduces a method to correct for hidden dependencies in meta-analyses of omics data, ensuring accurate results. The approach enhances the reliability of integrating findings across multiple studies and omics dimensions.

More Related Videos

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Related Experiment Videos

Last Updated: May 14, 2026

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

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

Published on: May 17, 2019

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Meta-analysis is a powerful tool for integrating omics data across studies.
Hidden dependencies between studies can inflate Type I errors in meta-analyses.
Existing methods may not adequately address non-independence in omics scans.

Purpose of the Study:

To present a simple method for meta-analysis that estimates and corrects for non-independence between omics scans.
To maintain the correct Type I error rate in meta-analytic inference.
To provide a robust method applicable to various omics data integration scenarios.

Main Methods:

The method automatically estimates the degree of non-independence between omics scans.
It corrects statistical inference based on the estimated non-independence.
The approach operates solely on summary analysis results, not requiring original data.

Main Results:

The proposed method successfully corrects for non-independence, preserving proper Type I error rates.
It accurately identifies independent scans, yielding traditional meta-analysis results when appropriate.
The method is versatile, applicable to genome-wide association studies (GWAS), sequencing scans, and correlated trait analyses.

Conclusions:

This novel method enhances the reliability of omics meta-analysis by addressing hidden dependencies.
It offers a safe and effective approach for integrating diverse omics study results, even with suspected correlations.
The method supports robust detection of pleiotropic genetic effects and improves overall omics data integration strategies.