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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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PrOnto database : GO term functional dissimilarity inferred from biological data.

Charles E Chapple¹, Carl Herrmann¹, Christine Brun²

¹Inserm, UMR_S1090 TAGC Marseille, France ; Aix-Marseille Université, UMR_S1090 TAGC Marseille, France.

Frontiers in Genetics

|June 20, 2015

Summary

We introduce two novel metrics to identify moonlighting proteins by assessing functional dissimilarity of Gene Ontology (GO) terms using biological information, not just GO tree structure.

Keywords:

database functional similarity gene ontology moonlighting protein protein-protein interactions

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Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

Moonlighting proteins perform multiple, unrelated functions.
Predicting moonlighting proteins computationally requires assessing cellular process similarity.
Existing Gene Ontology (GO) term similarity measures often lack biological relevance.

Purpose of the Study:

To develop novel metrics for GO term functional dissimilarity.
To identify moonlighting proteins using biologically-derived dissimilarity measures.
To create a database (PrOnto) for storing and querying these metrics.

Main Methods:

Developed two metrics for GO term functional dissimilarity.
One metric is based on protein annotations.
The other metric is based on protein-protein interactions.

Main Results:

The proposed metrics are derived from biological information.
These metrics offer a more biologically relevant assessment of functional dissimilarity.
The PrOnto database integrates these metrics for practical application.

Conclusions:

The novel dissimilarity metrics aid in the identification of moonlighting proteins.
The PrOnto database provides a valuable tool for computational biologists.
This approach enhances the prediction accuracy of proteins with multiple functions.