Документ взят из кэша поисковой машины. Адрес оригинального документа : http://mccmb.belozersky.msu.ru/2013/abstracts/abstracts/192.pdf Дата изменения: Mon Jun 3 04:44:48 2013 Дата индексирования: Thu Feb 27 21:07:44 2014 Кодировка:

Tax onom ic Unit Ide ntificati on T ool: a bioinf orm atical a pproac h for the im prov eme nt of hum an corne a m icrobiom e classification using 16S r RNA ge ne se que ncing data

Alexa nder I. T uzhi kov
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, School of Medicine, M iami, FL, United States , alexander.tuzhikov@gmail.com

Alexa nder Y. Pa nc hin
Institute for Information Transmission Problems, Russian Academy of Science , alexpanchin@yahoo.com

Qunfe ng Do ng
Departments of Biological Sciences, Computer Science and Engineering, University of North Texas, Texas, TX, United States , qunfeng.don g@unt.edu

David Nelson
Department of Biology, Indiana University, Bloomington, IN, United States nelson@indiana.edu

Terrence O'Brien
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, School of Medicine, Miami, FL, United States , tpob@hotmail.com

Valery I. S hestopalov
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, School of Medicine, Miami, FL, United States , VShestopalov@med.miami.edu

The healthy cor nea is typically culture -ne ga tive and is considered nearly free of microbiota d ue to the bactericidal action o f tear fil m. However, our new e xperime ntal data ob tained using next ge neration DNA seque nci ng of 16S RNA genes i ndicates the presence of a resident microbial community o n the healthy oc ular surface (OS). Whe n co mpared to c ulture-based me thods, deep sequenci ng of the 16S rRNA ge ne libraries of total DNA fro m healthy co nj unctiva and cornea has detec ted a muc h more diverse bacterial community. However, some bacterial ge nera are consistently "overlooke d" b y mai nstrea m seq ue nce classification algorithms suc h as the RDP- II Classifier and ma ny 16S rR NA ge ne seq uences identified in the huma n oc ular surface re main unclassified if only available bioinfor matical approaches are used . To become


clinically feasible, patho ge n identification based o n ne xt ge neratio n seque nci ng has to deliver the species or even the strain levels of identification for as ma ny microbes as possible. Due to the redunda nt seq ue nce si milarity between closely related species, a clear discern remai ns a challenging task. A plain ho molo gy-based search consta ntly provides a vast a mo unt o f highl y similar sequences fro m di ffere nt ta xo no mic sub gro ups, a nd thereb y it is imperative tha t a robust algorithm would e xplicitly identify a pivotal ta xo no mic s ub unit within the o utp ut. T he purpose of this stud y was to i mprove microbial classification usi ng a co mbi natio n of the BlastN algorithm a gainst the most c urrent GenBa nk database and a data filteri ng al gorithm for the anal ysis of microbiota at the huma n cor nea. In order to address the problem o f micriobial classification based on 16S rRNA ge ne seque nces, we developed a new algorithm T UIT (Ta xo no mic Unit Ide ntificatio n Tool) and applied it to anno tate (classify) 16S reads rejected (unclassified beyond Bac teria) by RDP- II Classification tool. T UIT utilizes BlastN­based search agai nst the latest seque nci ng data available with the most c urrent s eque nci ng Ge nBa nk databases. For 1300 16S RNA ge ne seque nces obtained fro m the huma n cornea that re mai ned "unclassified beyond Bacteria" with RDP-II Classification our algorithm was able to 1) classify 29% of the seque nces at the ge nus level, 2) classify 1% of the seq ue nces at the species level . Importa ntly, o ut of 165 bac terial ge nera identified o n the huma n oc ular surface, 105 were only detected b y T UIT a nd not b y RDP-II Classifier. This includes so me bacterial genera tha t appeared to be well-represented in the huma n oc ular surface suc h as Rickettsia, Holospora, Chitinophaga, Geobacter, Spirillum and Cardiobacterium Our approach to co mbi ne R DP-II Classifier and T UIT significantl y i mproved the characterizatio n of the oc ular microbiome diversity b y red ucing the fractio n of unclassified phylotypes a nd allowed us to achieve species -level characterization for so me representatives of the ocular surface microbiota. T he T UIT pro gra m ma y be applied for the s tud y o f other microbio mal data and mi ght be of i nterest for other researchers in the field of microbio me analysis.