Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants
<p>Abstract</p> <p>Background</p> <p>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 ba...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2007-08-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/8/278 |
| _version_ | 1818641544448573440 |
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| author | Huygens Flavia Thiruvenkataswamy Venugopal Inman-Bamber John Price Erin P Giffard Philip M |
| author_facet | Huygens Flavia Thiruvenkataswamy Venugopal Inman-Bamber John Price Erin P Giffard Philip M |
| author_sort | Huygens Flavia |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>Campylobacter jejuni</it>, <it>Yersinia enterocolitica </it>and <it>Clostridium difficile</it>.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-12-16T23:28:51Z |
| format | Article |
| id | doaj.art-0be21eb6ed7148258e21135714417e0b |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-16T23:28:51Z |
| publishDate | 2007-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0be21eb6ed7148258e21135714417e0b2022-12-21T22:11:56ZengBMCBMC Bioinformatics1471-21052007-08-018127810.1186/1471-2105-8-278Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variantsHuygens FlaviaThiruvenkataswamy VenugopalInman-Bamber JohnPrice Erin PGiffard Philip M<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>Campylobacter jejuni</it>, <it>Yersinia enterocolitica </it>and <it>Clostridium difficile</it>.</p> <p>Conclusion</p> <p>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.</p>http://www.biomedcentral.com/1471-2105/8/278 |
| spellingShingle | Huygens Flavia Thiruvenkataswamy Venugopal Inman-Bamber John Price Erin P Giffard Philip M Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants BMC Bioinformatics |
| title | Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants |
| title_full | Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants |
| title_fullStr | Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants |
| title_full_unstemmed | Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants |
| title_short | Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants |
| title_sort | computer aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants |
| url | http://www.biomedcentral.com/1471-2105/8/278 |
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