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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Proteomics01:33

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...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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...

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Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

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Related Experiment Video

Updated: Jun 21, 2026

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

Functional analysis of OMICs data and small molecule compounds in an integrated "knowledge-based" platform.

Yuri Nikolsky1, Eugene Kirillov, Roman Zuev

  • 1GeneGo, Inc., Saint Joseph, MI, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 15, 2009
PubMed
Summary

MetaDiscovery offers an integrated platform for analyzing complex omics data. It uses a detailed knowledge base and analytical tools to identify biomarkers, drug targets, and advance personalized medicine.

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Last Updated: Jun 21, 2026

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

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput omics data (microarray, SNPs, proteomics) present significant biological complexity and noise.
  • Effective analysis requires a high-fidelity, computer-readable knowledge base of protein interactions, pathways, and functional ontologies.
  • Existing tools often lack integration for comprehensive data management, analysis, and reporting.

Purpose of the Study:

  • To present MetaDiscovery, an integrated platform designed for functional analysis of omics data.
  • To provide a robust solution for managing, analyzing, and reporting complex biological datasets.
  • To facilitate the identification of potential biomarkers, drug targets, and hypotheses in biological systems.

Main Methods:

  • Development of MetaDiscovery, an 8-year project integrating a comprehensive database of protein interactions, pathways, and 10 functional ontologies for human, mouse, and rat.
  • Implementation of an analytical toolkit including gene/protein list enrichment analysis, a statistical interactome tool, and a network module with generation algorithms and filters.
  • Inclusion of MetaSearch for combinatorial database searching and MapEditor for pathway map creation and editing.

Main Results:

  • MetaDiscovery provides a unified platform for managing and analyzing diverse omics data.
  • The platform enables identification of over- and under-connected proteins and facilitates network generation.
  • The integrated knowledge base supports functional interpretation and hypothesis generation.

Conclusions:

  • MetaDiscovery offers a powerful solution for overcoming the challenges of omics data analysis.
  • Applications include biomarker discovery, drug target identification, and advancing translational and personalized medicine.
  • The platform supports hypothesis generation and analysis of biological effects for various applications.