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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-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...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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.
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...

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Related Experiment Video

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Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions
08:07

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Detecting phenotype-specific interactions between biological processes from microarray data and annotations.

Nadeem A Ansari1, Riyue Bao, Călin Voichiţa

  • 1Microsoft Corp., Redmond, WA 98052, USA. nadeem.ansari@microsoft.com

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 2, 2012
PubMed
Summary

This study introduces a new method to analyze how biological processes interact differently in diseases like cancer. The approach identifies significant changes in these interactions, offering new research directions.

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Area of Science:

  • Genomics and Bioinformatics
  • Systems Biology
  • Cancer Research

Background:

  • High-throughput technologies generate vast genomic data, posing challenges for biological interpretation.
  • Existing tools focus on over/under-represented Gene Ontology (GO) terms, neglecting process interactions.
  • Understanding dynamic interactions between biological processes is crucial for disease research.

Purpose of the Study:

  • To develop a novel computational approach for identifying significant changes in biological process interactions in disease phenotypes.
  • To compare these interactions between diseased states and normal conditions.
  • To uncover novel insights into the mechanisms of breast and lung cancer.

Main Methods:

  • Vector-space representation of biological processes.
  • Singular Value Decomposition (SVD)-based dimensionality reduction.
  • Differential weighting and bootstrapping for significance assessment of process interactions.

Main Results:

  • The approach successfully identified significant changes in biological process interactions in breast and lung cancer datasets.
  • Over 88% of identified interactions were validated through extensive literature review.
  • A subset of novel interactions with potential research implications was highlighted.

Conclusions:

  • The novel method effectively captures and quantifies changes in biological process interactions.
  • This approach provides a powerful tool for interpreting complex genomic data in disease contexts.
  • Findings offer new avenues for understanding and potentially treating lung and breast cancer.