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Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants

Price, Erin P., Inman-Bamber, John, Thiruvenkataswamy, Venugopal, Huygens, Flava and Giffard, Philip M. (2007). Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants. BMC Bioinformatics,8:278-285.

Document type: Journal Article
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IRMA ID 10603xPUB35
Title Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants
Author Price, Erin P.
Inman-Bamber, John
Thiruvenkataswamy, Venugopal
Huygens, Flava
Giffard, Philip M.
Journal Name BMC Bioinformatics
Publication Date 2007
Volume Number 8
ISSN 1471-2105   (check CDU catalogue open catalogue search in new window)
Start Page 278
End Page 285
Total Pages 8
Place of Publication United Kingdom
Publisher BioMed Central Ltd.
Field of Research 0601 - Biochemistry and Cell Biology
0801 - Artificial Intelligence and Image Processing
0802 - Computation Theory and Mathematics
HERDC Category C1 - Journal Article (DEST)
Abstract Background
Single nucleotide polymorphisms (SNPs) and genes that exhibit presence/absence variation have provided informative marker sets for bacterial and viral genotyping. Identification of marker sets optimised for these purposes has been based on maximal generalized discriminatory power as measured by Simpson's Index of Diversity, or on the ability to identify specific variants. Here we describe the Not-N algorithm, which is designed to identify small sets of genetic markers diagnostic for user-specified subsets of known genetic variants. The algorithm does not treat the user-specified subset and the remaining genetic variants equally. Rather Not-N analysis is designed to underpin assays that provide 0% false negatives, which is very important for e.g. diagnostic procedures for clinically significant subgroups within microbial species.


The Not-N algorithm has been incorporated into the "Minimum SNPs" computer program and used to derive genetic markers diagnostic for multilocus sequence typing-defined clonal complexes, hepatitis C virus (HCV) subtypes, and phylogenetic clades defined by comparative genome hybridization (CGH) data for Campylobacter jejuni, Yersinia enterocolitica and Clostridium difficile. Conclusion Not-N analysis is effective for identifying small sets of genetic markers diagnostic for microbial sub-groups. The best results to date have been obtained with CGH data from several bacterial species, and HCV sequence data.
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