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Metaanaliza podatkov o humani mikrobioti
ID Bajuk, Jerca (Author), ID Stres, Blaž (Mentor) More about this mentor... This link opens in a new window

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PID: 20.500.12556/rul/20ca9217-37b4-41fe-823e-6aeb3b07dcdb

Abstract
Zanimanje znanstvenikov za raziskovanje mikrobiote prebavnega trakta narašča z razumevanjem njenih številnih vplivov na človekovo zdravje. Na področju prebavne mikrobiote odkrivamo vedno več korelacij med strukturo mikrobne združbe in različnimi okoljskimi dejavniki. O tej povezavi je bilo objavljenih že mnogo raziskav, vendar si podatki le-teh velikokrat nasprotujejo. Z metaanalizo združenih podatkov iz več raziskav smo v magistrskem delu želeli ugotoviti razlike v deležih bakterijskih vrst glede na starostne skupine in življenjski slog preiskovancev ter opredeliti vpliv razlik v analitskih pristopih med različnimi študijami. Ob tem smo tudi neodvisno preverili ugotovitve, predstavljene v objavljenih člankih. Metagenome mikrobiote prebavil ljudi različnih starosti in geografskega porekla smo zbrali s strežnika MG-RAST. Za boljše razumevanje mikrobne diverzitete v odvisnosti od zunanjih parametrov smo generirali rarefakcijske krivulje metagenomov. Nadaljnje statistične analize smo opravili s programom PAST z nemetričnim večdimenzionalnim lestvičenjem (NM-MDS), s katerim smo grafično primerjali različne mikrobne združbe in vpliv okoljskih parametrov na njihovo grupiranje. Ugotovili smo, da imajo na rezultate o mikrobni sestavi največji vpliv uporabljene analitske metode. Njihova optimizacija in standardizacija bi nam omogočila globalno primerljivost takih podatkov in jasnejši pregled nad mikrobno raznolikostjo prebavnega trakta in njenim vplivom na človekovo zdravje.

Language:Slovenian
Keywords:medicinska mikrobiologija, mikrobne združbe, črevesna mikrobiota, prebavni trakt, genomika, metagenomika, metagenom, metaanaliza, statistične metode, nemetrično večdimenzionalno lestvičenje, NM-MDS
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:BF - Biotechnical Faculty
Publisher:[J. Bajuk]
Year:2017
PID:20.500.12556/RUL-94216 This link opens in a new window
UDC:579.61:616.3:575.111:519.237
COBISS.SI-ID:4797560 This link opens in a new window
Publication date in RUL:20.07.2017
Views:2070
Downloads:762
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Secondary language

Language:English
Title:Meta-analysis of data about human microbiota
Abstract:
Scientific interest in the gut microbiota research is increasing with our understanding of its many effects on human health. More and more correlations between the composition of gut microbiota and different environmental factors are being discovered. Many articles have been published on this matter but the data are often contradictory. The meta-analysis of the pooled data from several studies used in this master thesis was focused on the differences in the proportions of different bacterial species depending on the subject’s age and lifestyle, and on the effects of different analytical methods on the data outcome. At the same time we also independently verified the findings presented in the published works. The gut microbiota metagenomes of subjects of various age and geographic origin were collected from MG-RAST server. They were used to generate rarefaction curves, to better understand microbiota diversity depending on external parameters. The further statistical analyses were performed with PAST programme, using non-metric multidimensional scaling (NM-MDS), which was used to graphically compare different microbial communities and the impact of environmental parameters on their grouping. We concluded that the chosen analytical methods had the biggest influence on the data about the composition of microbial community. The optimization and standardization of analytical methods is needed in order to enable global comparability of such data and better insight into the microbial diversity in the gut and its effects on human health.

Keywords:medical microbiology, microbial communities, gut microbiota, digestive tract, genomics, metagenomics, metagenome, statistical methods, non-metric multidimensional scaling, NM-MDS

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