Gene function hypotheses for the Campylobacter jejuni glycome generated by a logic-based approach

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DOIResolve DOI: http://doi.org/10.1016/j.jmb.2012.10.014
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleJournal of Molecular Biology
ISSN0022-2836
Volume425
Issue1
Pages186197; # of pages: 12
Subjectglucuronosyltransferase; heptose guanosyltransferase; heptose kinase; methyltransferase; sedoheptulose isomerase; sugar transferase; unclassified drug; article; Campylobacter jejuni; DNA microarray; gene expression; gene function; knockout gene; machine learning; nonhuman; priority journal; serotype; Artificial Intelligence; Bacterial Capsules; Biosynthetic Pathways; Campylobacter jejuni; Gene Knockout Techniques; Genes, Bacterial; Glycomics; Logic; Metabolomics; Models, Biological; Molecular Sequence Annotation; Mutation; Oligonucleotide Array Sequence Analysis; Phenotype; Polysaccharides, Bacterial; Systems Biology; Campylobacter jejuni
AbstractIncreasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning - the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. © 2012 Elsevier Ltd.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Biological Sciences (IBS-ISB); NRC Institute for Marine Biosciences (IMB-IBM)
Peer reviewedYes
NPARC number21269810
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Record identifierc567c96c-e878-4326-b202-05390750300b
Record created2013-12-13
Record modified2016-05-09
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