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A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets

Author

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  • Aarón Ayllón-Benítez
  • Fleur Mougin
  • Julien Allali
  • Rodolphe Thiébaut
  • Patricia Thébault

Abstract

Motivation: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations. Results: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a “good” synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms’ characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used.

Suggested Citation

  • Aarón Ayllón-Benítez & Fleur Mougin & Julien Allali & Rodolphe Thiébaut & Patricia Thébault, 2018. "A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-22, November.
  • Handle: RePEc:plo:pone00:0208037
    DOI: 10.1371/journal.pone.0208037
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    References listed on IDEAS

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    1. Fran Supek & Matko Bošnjak & Nives Škunca & Tomislav Šmuc, 2011. "REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-9, July.
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