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Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons.

Xiaoqiong Wei^1,2, Chengxin Zhang², Peter L Freddolino^2,3

¹State Key Laboratory of Biotherapy and Cancer Center/Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

Bioinformatics (Oxford, England)

|May 30, 2020

Summary

We developed a novel method to identify errors in protein function databases by comparing annotation rates across species. This approach successfully detected 250 potential misannotations in Gene Ontology (GO) terms, improving data reliability.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Protein function databases are crucial for biological research but often contain annotation errors due to automated curation.
Accurate protein annotations are essential for reliable downstream analyses and scientific discovery.

Purpose of the Study:

To introduce a new computational approach for detecting potentially incorrect Gene Ontology (GO) annotations in large-scale protein function databases.
To assess the prevalence and types of misannotations in recent UniProt-GOA releases.

Main Methods:

Developed a Ratio of Annotation Rates (RAR) method comparing GO term annotation frequencies across different taxonomic groups.
Applied the RAR approach to 20 species across two UniProt-GOA releases (2018-11-6 and 2019-6-3).
Analyzed the types of misannotations, focusing on 'Inferred from Biological aspect of Ancestor (IBA)' and 'Inferred from Sequence or structural Similarity (ISS)' evidence codes.

Main Results:

Identified 250 potential misannotations in the UniProt-GOA 2018-11-6 release using the RAR method.
Observed that only 25% of these detected misannotations were corrected in the subsequent 2019-6-3 release.
Found that 56% of misannotations were of the IBA type, contrasting with previous findings attributing most errors to ISS.

Conclusions:

The RAR method provides a simple yet effective strategy for detecting misannotations in protein function databases.
The findings suggest a potential shift in the primary sources of annotation errors within large-scale databases.
This approach is valuable for large-scale comparative protein function studies and database quality control.