Microbial Typing by Machine Learned DNA Melt Signatures
There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) regi...
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| Veröffentlicht in: | Scientific reports 2017-02, Vol.7 (1), p.42097-42097, Article 42097 |
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| creator | Andini, Nadya Wang, Bo Athamanolap, Pornpat Hardick, Justin Masek, Billie J. Thair, Simone Hu, Anne Avornu, Gideon Peterson, Stephen Cogill, Steven Rothman, Richard E. Carroll, Karen C. Gaydos, Charlotte A. Wang, Jeff Tza-Huei Batzoglou, Serafim Yang, Samuel |
| description | There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption. |
| doi_str_mv | 10.1038/srep42097 |
| format | Article |
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For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/srep42097</identifier><identifier>PMID: 28165067</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>38/71 ; 631/326/107 ; 631/326/2521 ; Algorithms ; Automation ; Bacteria ; Bacteria - classification ; Bacteria - genetics ; Bacterial Typing Techniques - methods ; Bayes Theorem ; Bayesian analysis ; Blood culture ; Classification ; Deoxyribonucleic acid ; DNA ; DNA, Bacterial - genetics ; DNA, Ribosomal Spacer - genetics ; Genomes ; Humanities and Social Sciences ; Machine Learning ; multidisciplinary ; Phylogenetics ; Phylogeny ; Ribosomal DNA ; rRNA 16S ; Science ; Spacer ; Statistical analysis ; Transition Temperature ; Typing</subject><ispartof>Scientific reports, 2017-02, Vol.7 (1), p.42097-42097, Article 42097</ispartof><rights>The Author(s) 2017</rights><rights>Copyright Nature Publishing Group Feb 2017</rights><rights>Copyright © 2017, The Author(s) 2017 The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-1bcf258c9993b430dfa68563d2994aeca7c5658129ac135d2a80ebc9a6d4c45d3</citedby><cites>FETCH-LOGICAL-c438t-1bcf258c9993b430dfa68563d2994aeca7c5658129ac135d2a80ebc9a6d4c45d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292719/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292719/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,41096,42165,51551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28165067$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Andini, Nadya</creatorcontrib><creatorcontrib>Wang, Bo</creatorcontrib><creatorcontrib>Athamanolap, Pornpat</creatorcontrib><creatorcontrib>Hardick, Justin</creatorcontrib><creatorcontrib>Masek, Billie J.</creatorcontrib><creatorcontrib>Thair, Simone</creatorcontrib><creatorcontrib>Hu, Anne</creatorcontrib><creatorcontrib>Avornu, Gideon</creatorcontrib><creatorcontrib>Peterson, Stephen</creatorcontrib><creatorcontrib>Cogill, Steven</creatorcontrib><creatorcontrib>Rothman, Richard E.</creatorcontrib><creatorcontrib>Carroll, Karen C.</creatorcontrib><creatorcontrib>Gaydos, Charlotte A.</creatorcontrib><creatorcontrib>Wang, Jeff Tza-Huei</creatorcontrib><creatorcontrib>Batzoglou, Serafim</creatorcontrib><creatorcontrib>Yang, Samuel</creatorcontrib><title>Microbial Typing by Machine Learned DNA Melt Signatures</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. 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classification</topic><topic>Bacteria - genetics</topic><topic>Bacterial Typing Techniques - methods</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Blood culture</topic><topic>Classification</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA, Bacterial - genetics</topic><topic>DNA, Ribosomal Spacer - genetics</topic><topic>Genomes</topic><topic>Humanities and Social Sciences</topic><topic>Machine Learning</topic><topic>multidisciplinary</topic><topic>Phylogenetics</topic><topic>Phylogeny</topic><topic>Ribosomal DNA</topic><topic>rRNA 16S</topic><topic>Science</topic><topic>Spacer</topic><topic>Statistical analysis</topic><topic>Transition Temperature</topic><topic>Typing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Andini, Nadya</creatorcontrib><creatorcontrib>Wang, Bo</creatorcontrib><creatorcontrib>Athamanolap, Pornpat</creatorcontrib><creatorcontrib>Hardick, Justin</creatorcontrib><creatorcontrib>Masek, Billie J.</creatorcontrib><creatorcontrib>Thair, Simone</creatorcontrib><creatorcontrib>Hu, Anne</creatorcontrib><creatorcontrib>Avornu, Gideon</creatorcontrib><creatorcontrib>Peterson, Stephen</creatorcontrib><creatorcontrib>Cogill, Steven</creatorcontrib><creatorcontrib>Rothman, Richard E.</creatorcontrib><creatorcontrib>Carroll, Karen C.</creatorcontrib><creatorcontrib>Gaydos, Charlotte A.</creatorcontrib><creatorcontrib>Wang, Jeff Tza-Huei</creatorcontrib><creatorcontrib>Batzoglou, Serafim</creatorcontrib><creatorcontrib>Yang, Samuel</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Andini, Nadya</au><au>Wang, Bo</au><au>Athamanolap, Pornpat</au><au>Hardick, Justin</au><au>Masek, Billie J.</au><au>Thair, Simone</au><au>Hu, Anne</au><au>Avornu, Gideon</au><au>Peterson, Stephen</au><au>Cogill, Steven</au><au>Rothman, Richard E.</au><au>Carroll, Karen C.</au><au>Gaydos, Charlotte A.</au><au>Wang, Jeff Tza-Huei</au><au>Batzoglou, Serafim</au><au>Yang, Samuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Microbial Typing by Machine Learned DNA Melt Signatures</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2017-02-06</date><risdate>2017</risdate><volume>7</volume><issue>1</issue><spage>42097</spage><epage>42097</epage><pages>42097-42097</pages><artnum>42097</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>28165067</pmid><doi>10.1038/srep42097</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 38/71 631/326/107 631/326/2521 Algorithms Automation Bacteria Bacteria - classification Bacteria - genetics Bacterial Typing Techniques - methods Bayes Theorem Bayesian analysis Blood culture Classification Deoxyribonucleic acid DNA DNA, Bacterial - genetics DNA, Ribosomal Spacer - genetics Genomes Humanities and Social Sciences Machine Learning multidisciplinary Phylogenetics Phylogeny Ribosomal DNA rRNA 16S Science Spacer Statistical analysis Transition Temperature Typing |
| title | Microbial Typing by Machine Learned DNA Melt Signatures |
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