Literature DB >> 16873517

Protein classification using ontology classification.

K Wolstencroft1, P Lord, L Tabernero, A Brass, R Stevens.   

Abstract

MOTIVATION: The classification of proteins expressed by an organism is an important step in understanding the molecular biology of that organism. Traditionally, this classification has been performed by human experts. Human knowledge can recognise the functional properties that are sufficient to place an individual gene product into a particular protein family group. Automation of this task usually fails to meet the 'gold standard' of the human annotator because of the difficult recognition stage. The growing number of genomes, the rapid changes in knowledge and the central role of classification in the annotation process, however, motivates the need to automate this process.
RESULTS: We capture human understanding of how to recognise members of the protein phosphatases family by domain architecture as an ontology. By describing protein instances in terms of the domains they contain, it is possible to use description logic reasoners and our ontology to assign those proteins to a protein family class. We have tested our system on classifying the protein phosphatases of the human and Aspergillus fumigatus genomes and found that our knowledge-based, automatic classification matches, and sometimes surpasses, that of the human annotators. We have made the classification process fast and reproducible and, where appropriate knowledge is available, the method can potentially be generalised for use with any protein family. AVAILABILITY: All components described in this paper are freely available. OWL ontology http://www.bioinf.man.ac.uk/phosphabase myGrid http://www.mygrid.org.uk Instance Store http://instancestore.man.ac.uk.

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Year:  2006        PMID: 16873517     DOI: 10.1093/bioinformatics/btl208

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  18 in total

Review 1.  Bio-ontologies: current trends and future directions.

Authors:  Olivier Bodenreider; Robert Stevens
Journal:  Brief Bioinform       Date:  2006-08-09       Impact factor: 11.622

2.  An ontology of bacteria to help physicians to compare antibacterial spectra.

Authors:  Catherine Duclos; Jérome Nobécourt; Gian Luigi Cartolano; Anis Ellini; Alain Venot
Journal:  AMIA Annu Symp Proc       Date:  2007-10-11

3.  A common layer of interoperability for biomedical ontologies based on OWL EL.

Authors:  Robert Hoehndorf; Michel Dumontier; Anika Oellrich; Sarala Wimalaratne; Dietrich Rebholz-Schuhmann; Paul Schofield; Georgios V Gkoutos
Journal:  Bioinformatics       Date:  2011-02-21       Impact factor: 6.937

4.  Bioinformatic identification of novel protein phosphatases in the dog genome.

Authors:  Mrigendra B Karmacharya; Jae-Won Soh
Journal:  Mol Cell Biochem       Date:  2011-01-15       Impact factor: 3.396

Review 5.  Diverse array-designed modes of combination therapies in Fangjiomics.

Authors:  Jun Liu; Zhong Wang
Journal:  Acta Pharmacol Sin       Date:  2015-04-13       Impact factor: 6.150

Review 6.  Computational tools for comparative phenomics: the role and promise of ontologies.

Authors:  Georgios V Gkoutos; Paul N Schofield; Robert Hoehndorf
Journal:  Mamm Genome       Date:  2012-07-20       Impact factor: 2.957

7.  Adding a little reality to building ontologies for biology.

Authors:  Phillip Lord; Robert Stevens
Journal:  PLoS One       Date:  2010-09-03       Impact factor: 3.240

8.  Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning.

Authors:  Robert Hoehndorf; Michel Dumontier; Anika Oellrich; Dietrich Rebholz-Schuhmann; Paul N Schofield; Georgios V Gkoutos
Journal:  PLoS One       Date:  2011-07-18       Impact factor: 3.240

9.  Multi-label literature classification based on the Gene Ontology graph.

Authors:  Bo Jin; Brian Muller; Chengxiang Zhai; Xinghua Lu
Journal:  BMC Bioinformatics       Date:  2008-12-08       Impact factor: 3.169

10.  Identification of protein complex associated with LYT1 of Trypanosoma cruzi.

Authors:  C Lugo-Caballero; G Ballesteros-Rodea; S Martínez-Calvillo; Rebeca Manning-Cela
Journal:  Biomed Res Int       Date:  2013-03-17       Impact factor: 3.411

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