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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,...
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-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...

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Updated: Jun 19, 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

Published on: October 19, 2021

Detailing regulatory networks through large scale data integration.

Curtis Huttenhower1, K Tsheko Mutungu, Natasha Indik

  • 1Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.

Bioinformatics (Oxford, England)
|October 15, 2009
PubMed
Summary
This summary is machine-generated.

We developed COALESCE, a computational tool that identifies gene regulatory networks by analyzing gene expression and DNA sequences. This system accurately predicts regulatory modules and transcription factor targets in metazoan genomes.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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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

Published on: October 19, 2021

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Cellular regulation heavily relies on transcriptional control involving transcription factors (TFs), microRNAs, and epigenetic modifications.
  • These elements form complex regulatory networks that can be modeled as modules comprising co-regulated genes, their conditions, and regulatory motifs.

Purpose of the Study:

  • To present the Combinatorial Algorithm for Expression and Sequence-based Cluster Extraction (COALESCE) for predicting regulatory modules.
  • To provide an efficient and flexible platform for integrating diverse data types to predict metazoan regulatory networks.

Main Methods:

  • COALESCE employs biclustering and motif prediction algorithms.
  • It integrates diverse data, including gene expression, sequence data, evolutionary conservation, and nucleosome placement, using Bayesian data integration.
  • The system is validated using functional evaluation, known TF targets, synthetic data, and metazoan datasets.

Main Results:

  • COALESCE efficiently discovers expression biclusters and regulatory motifs in large metazoan genomes and microarray datasets.
  • The system demonstrates high accuracy in functional and TF/target assignments, outperforming existing tools.
  • Zero false positives were observed on synthetic data, indicating high reliability.

Conclusions:

  • COALESCE offers an efficient and flexible platform for predicting metazoan regulatory networks.
  • The system's ability to integrate diverse data types enhances the accuracy of regulatory module prediction.
  • COALESCE is a valuable tool for dissecting complex gene regulation in higher organisms.