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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Closing the loop: from paper to protein annotation using supervised Gene Ontology classification.

Julien Gobeill¹, Emilie Pasche², Dina Vishnyakova²

¹BiTeM group, University of Applied Sciences-HEG, Library and Information Sciences, Rte de Drize 7, 1227 Geneva, Switzerland, Division of Medical Information Sciences, University and Hospitals of Geneva, Geneva, Switzerland and SIBtex group, SIB Swiss Institute of Bioinformatics, Rue Michel-Servet 1, 1206 Geneva, Switzerland BiTeM group, University of Applied Sciences-HEG, Library and Information Sciences, Rte de Drize 7, 1227 Geneva, Switzerland, Division of Medical Information Sciences, University and Hospitals of Geneva, Geneva, Switzerland and SIBtex group, SIB Swiss Institute of Bioinformatics, Rue Michel-Servet 1, 1206 Geneva, Switzerland Julien.gobeill@hesge.ch.

Database : the Journal of Biological Databases and Curation

|September 6, 2014

Summary

Automated gene function annotation using the GOCat system significantly improves upon previous methods. This machine learning approach aids in semi-automatic curation workflows by selecting evidence and predicting Gene Ontology (GO) concepts.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Gene function curation using Gene Ontology (GO) concepts is a bottleneck in genomics.
Previous automated methods, especially supervised approaches, lacked sufficient training data and performance for real-world curation.

Purpose of the Study:

To evaluate the GOCat supervised classifier for automatic GO concept assignment in the BioCreative IV challenge.
To develop a complete workflow for semi-automatic gene function curation.

Main Methods:

Utilized a supervised statistical approach with a Naïve Bayes classifier for evidence sentence selection (Subtask A).
Applied the GOCat classifier to predict GO concepts from selected sentences (Subtask B).
Leveraged a knowledge base of curated instances for similarity computations.

Main Results:

Achieved fair results in selecting GO evidence sentences.
Reached leading performance in GO concept prediction, with up to 65% hierarchical recall in the top 20 concepts.
Demonstrated that machine learning outperforms dictionary-based approaches.

Conclusions:

The developed workflow, combining sentence selection and GO concept prediction, is suitable for semi-automatic curation.
GOCat shows significant potential to accelerate the gene function curation process.
Machine learning-based systems are now competitive for automated GO annotation.