University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Engineering and Information Technology
  • Mechanical Engineering
  • Mechanical Engineering - Research Publications
  • View Item
  • Minerva Access
  • Engineering and Information Technology
  • Mechanical Engineering
  • Mechanical Engineering - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Gene function prediction based on genomic context clustering and discriminative learning: an application to bacteriophages

    Thumbnail
    Download
    Gene function prediction based on genomic context clustering and discriminative learning: an application to bacteriophages (297.8Kb)

    Citations
    Scopus
    Web of Science
    Altmetric
    11
    7
    Author
    Li, J; Halgamuge, SK; Kells, CI; Tang, SL
    Date
    2007
    Source Title
    BMC Bioinformatics
    Publisher
    BMC
    University of Melbourne Author/s
    Halgamuge, Saman; KELLS, CHRISTOPHER IAN; Li, Jason
    Affiliation
    Mechanical Engineering
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Li, J., Halgamuge, S. K., Kells, C. I. & Tang, S. L. (2007). Gene function prediction based on genomic context clustering and discriminative learning: an application to bacteriophages. BMC Bioinformatics, 8 Suppl 4 (Suppl 4), pp.S6-S6. https://doi.org/10.1186/1471-2105-8-S4-S6.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/31770
    DOI
    10.1186/1471-2105-8-S4-S6
    Abstract
    BACKGROUND: Existing methods for whole-genome comparisons require prior knowledge of related species and provide little automation in the function prediction process. Bacteriophage genomes are an example that cannot be easily analyzed by these methods. This work addresses these shortcomings and aims to provide an automated prediction system of gene function. RESULTS: We have developed a novel system called SynFPS to perform gene function prediction over completed genomes. The prediction system is initialized by clustering a large collection of weakly related genomes into groups based on their resemblance in gene distribution. From each individual group, data are then extracted and used to train a Support Vector Machine that makes gene function predictions. Experiments were conducted with 9 different gene functions over 296 bacteriophage genomes. Cross validation results gave an average prediction accuracy of ~80%, which is comparable to other genomic-context based prediction methods. Functional predictions are also made on 3 uncharacterized genes and 12 genes that cannot be identified by sequence alignment. The software is publicly available at http://www.synteny.net/. CONCLUSION: The proposed system employs genomic context to predict gene function and detect gene correspondence in whole-genome comparisons. Although our experimental focus is on bacteriophages, the method may be extended to other microbial genomes as they share a number of similar characteristics with phage genomes such as gene order conservation.
    Keywords
    Information Systems

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [52369]
    • Mechanical Engineering - Research Publications [387]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors