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Related Experiment Video

Updated: Aug 17, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Improving automatic GO annotation with semantic similarity.

Bishnu Sarker^1,2,3, Navya Khare^1,4, Marie-Dominique Devignes¹

¹CNRS, Inria, LORIA, University of Lorraine, 54000, Nancy, France.

BMC Bioinformatics

|December 12, 2022

Summary

This summary is machine-generated.

This study introduces GrAPFI-GO, an enhanced method for automatic protein function annotation using Gene Ontology (GO) terms. The approach improves accuracy by leveraging semantic and hierarchical relationships within GO, aiding bioinformatics research.

Keywords:

Domain similarity network Gene ontology annotation GrAPFI K-nearest neighbor Label propagation Protein function annotation Semantic similarity

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Automatic protein functional annotation is a significant challenge in bioinformatics.
Manual annotation is time-consuming and resource-intensive.
There is a need for computational tools to automate protein annotation.

Purpose of the Study:

To extend the GrAPFI method for automatic protein annotation with Gene Ontology (GO) terms, creating GrAPFI-GO.
To incorporate semantic similarity and hierarchical relations of GO terms into the annotation process.
To evaluate the performance of the proposed method.

Main Methods:

Adapted the graph-based automatic protein function inference (GrAPFI) method.
Developed GrAPFI-GO for protein annotation using GO terms.
Explored similarity measures based on common neighbors in protein similarity graphs.
Implemented a pruning and post-processing technique considering GO term semantic similarity and hierarchy.

Main Results:

The GrAPFI-GO method was compared with and without common neighbor similarity.
Performance of GrAPFI-GO and other annotation tools was tested.
Experimental results demonstrated the effectiveness of the proposed approach.

Conclusions:

The proposed semantic hierarchical post-processing improves GrAPFI-GO performance.
The method also enhances the performance of other annotation tools.
GrAPFI-GO offers an efficient procedure to improve automatic protein function annotation by exploiting GO term semantic relations.