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Lu C, Wang J, Zhang Z, et al. NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Comput Biol Chem. 2016;65:203-211doi: 10.1016/j.compbiolchem.2016.09.005.
Lu, C., Wang, J., Zhang, Z., Yang, P., & Yu, G. (2016). NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Computational biology and chemistry, 65203-211. https://doi.org/10.1016/j.compbiolchem.2016.09.005
Lu, Chang, et al. "NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity." Computational biology and chemistry vol. 65 (2016): 203-211. doi: https://doi.org/10.1016/j.compbiolchem.2016.09.005
Lu C, Wang J, Zhang Z, Yang P, Yu G. NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Comput Biol Chem. 2016 Dec;65:203-211. doi: 10.1016/j.compbiolchem.2016.09.005. Epub 2016 Sep 13. PMID: 27670689.
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Comput Biol Chem. 2016 Dec;65:203-211. doi: 10.1016/j.compbiolchem.2016.09.005. Epub 2016 Sep 13.

NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity.

Computational biology and chemistry

Chang Lu, Jun Wang, Zili Zhang, Pengyi Yang, Guoxian Yu

Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing 400715, China.
  2. School of Mathematics and Statistics, The University of Sydney, New South Wales, Australia.
  3. College of Computer and Information Science, Southwest University, Chongqing 400715, China. Electronic address: [email protected].

PMID: 27670689 DOI: 10.1016/j.compbiolchem.2016.09.005

Abstract

Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the correctness of annotations. Due to resources limitations, only a small portion of annotations are manually added/checked by GO curators, and a large portion of available annotations are computationally inferred. While computationally inferred annotations provide greater coverage of known genes, they may also introduce annotation errors (noise) that could mislead the interpretation of the gene functions and their roles in cellular and biological processes. In this paper, we investigate how to identify noisy annotations, a rarely addressed problem, and propose a novel approach called NoisyGOA. NoisyGOA first measures taxonomic similarity between ontological terms using the GO hierarchy and semantic similarity between genes. Next, it leverages the taxonomic similarity and semantic similarity to predict noisy annotations. We compare NoisyGOA with other alternative methods on identifying noisy annotations under different simulated cases of noisy annotations, and on archived GO annotations. NoisyGOA achieved higher accuracy than other alternative methods in comparison. These results demonstrated both taxonomic similarity and semantic similarity contribute to the identification of noisy annotations. Our study shows that annotation errors are predictable and removing noisy annotations improves the performance of gene function prediction. This study can prompt the community to study methods for removing inaccurate annotations, a critical step for annotating gene and pathway functions.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Keywords: GO annotations; Gene Ontology; Semantic similarity; Taxonomic similarity

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