seGOsa: Software environment for Gene Ontology-driven similarity assessment

Huiru Zheng*, Francisco Azuaje, Haiying Wang

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Citations (Scopus)

    Abstract

    In recent years there has been a growing trend towards the adoption of ontologies to support comprehensive, large-scale functional genomics research. This paper introduces seGOsa, a user-friendly cross-platform system to support large-scale assessment of Gene Ontology (GO)-driven similarity among gene products. Using information-theoretic approaches, the system exploits both topological features of the GO (i.e., between-term relationships in the hierarchy) and statistical features of the model organism databases annotated to the GO (i.e., term frequency) to assess functional similarity among gene products. Based on the assumption that the more information two terms share in common, the more similar they are, three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics) have been implemented to measure between-term similarity within each of the GO hierarchies. Meanwhile, seGOsa offers two approaches (simple and highest average similarity) to assessing the similarity between gene products based on the aggregation of between-term similarities. The program is freely available for non-profit use on request from the authors.

    Original languageEnglish
    Title of host publicationProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
    PublisherIEEE Computer Society
    Pages539-542
    Number of pages4
    ISBN (Print)9781424483075
    DOIs
    Publication statusPublished - 2010

    Publication series

    NameProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

    Keywords

    • Gene ontology
    • Information content
    • Semantic similarity

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