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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,...
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.
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...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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mRNA Interactome Capture from Plant Protoplasts
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mRNA Interactome Capture from Plant Protoplasts

Published on: July 28, 2017

Quantifying protein function specificity in the gene ontology.

Brenton Louie, Silas Bergen, Roger Higdon

    Standards in Genomic Sciences
    |February 10, 2011
    PubMed
    Summary
    This summary is machine-generated.

    Quantitative metrics for Gene Ontology (GO) function specificity enable protein function comparisons. This study details four GO specificity metrics: ancestor count, offspring count, term proportion, and Information Content (IC), discussing their strengths and weaknesses.

    Keywords:
    function specificityprotein annotationprotein function

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    Published on: August 15, 2019

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • The Gene Ontology (GO) provides a standardized vocabulary for describing protein functions.
    • Quantitative metrics are needed to measure the specificity of GO terms for comparing protein functions.
    • Existing methods may not fully capture the nuances of GO term specificity.

    Purpose of the Study:

    • To introduce and describe four distinct quantitative metrics for measuring Gene Ontology (GO) function specificity.
    • To enable the development of robust distance and similarity measures between protein functions.
    • To provide a comprehensive analysis of the strengths and weaknesses of each proposed metric.

    Main Methods:

    • Calculation of four specificity metrics for GO terms: number of ancestor terms, number of offspring terms, proportion of terms, and Information Content (IC).
    • Comparative analysis of the relationships between these metrics.
    • Evaluation of the advantages and disadvantages inherent in each metric.

    Main Results:

    • Successfully defined and calculated four distinct metrics for GO term specificity.
    • Demonstrated the utility of these metrics in quantifying protein function specificity.
    • Identified the unique characteristics and limitations associated with each specificity measure.

    Conclusions:

    • The four presented metrics offer valuable quantitative approaches to assess Gene Ontology (GO) function specificity.
    • These metrics facilitate the development of improved methods for comparing protein functions based on GO annotations.
    • Understanding the properties of each metric is crucial for appropriate application in bioinformatics and computational biology research.