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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,...
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,...
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...
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...
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...
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.

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Related Experiment Video

Updated: May 22, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Analyzing large biological datasets with association networks.

Tatiana V Karpinets1, Byung H Park, Edward C Uberbacher

  • 1Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. k2n@otrnl.gov

Nucleic Acids Research
|May 29, 2012
PubMed
Summary

This study introduces a computational framework to uncover patterns in biological data by creating networks of associated annotations. The approach maps sequenced prokaryotic organisms, revealing distinct groups of pathogens, environmental isolates, and plant symbionts.

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

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Last Updated: May 22, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput biotechnologies generate vast biological data.
  • Novel computational methods are needed to extract knowledge from this data.
  • Discovering relationships in complex biological datasets is challenging.

Purpose of the Study:

  • To propose a computational framework for discovering modular structure and relationships in complex biological data.
  • To develop a method for converting biological annotations into networks (Anets).
  • To facilitate the discovery of significant relationships through clustering and visualization.

Main Methods:

  • Utilized a semantic-preserving vocabulary to convert biological annotations into networks (Anets).
  • Defined annotation association based on co-occurrence patterns with other annotations.
  • Applied clustering and visualization techniques to analyze the Anet.
  • Tested the framework on metadata from the Genomes OnLine Database.

Main Results:

  • Developed a computational framework for biological data analysis.
  • Created networks (Anets) representing relationships between biological annotations.
  • Generated a biological map of sequenced prokaryotic organisms.
  • Identified three major clusters: pathogens, environmental isolates, and plant symbionts.

Conclusions:

  • The proposed framework effectively discovers modular structure and relationships in complex biological data.
  • The Anet approach facilitates the identification of significant biological patterns.
  • The biological map provides insights into the classification of prokaryotic organisms.