De novo sastavljanje metagenomskih podataka korištenjem grupiranja podataka temeljenih na Bayesovom modelu

master's thesis

• Title (english): De novo metagenomic assembly using Bayesian model-based clustering
• Author: Mirta Dvorničić
• Mentor: Mile Šikić (mentor)
• Committee member: Mile Šikić (predsjednik povjerenstva)
• Committee member: Mladen Vučić (član povjerenstva)
• Committee member: Damir Seršić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2014-07-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Mikrobne zajednice utječu na gotovo svaki aspekt našeg života. Metagenomika nastoji otkriti nove spoznaje o tim zajednicama analizom DNA uzoraka izuzetih direktno iz njihovih okoliša. Metagenomska istraživanja se i dalje uglavnom oslanjaju na ručne intervencije i alate namijenjene za sastavljanje genomskih podataka koji ne uzimaju u obzir specifičnosti metagenomskih podataka. U ovom radu je predstavljena metoda za de novo sastavljanje metagenomskih podataka korištenjem grupiranja podataka temeljenih na Bayesovom modelu (Sigma). Pokazano je da se kombiniranjem te metode s optimalnim skafolderom genomskih podataka (Opera) problem sastavljanja metagenomskih podataka može točno i automatski svesti na problem sastavljanja genomskih podataka iskorištavanjem informacija dostupnih tijekom sastavljanja genoma. Usporedbe na simuliranim i stvarnim metagenomskim podacima pokazuju da kombinacija ovih dvaju metoda (OperaMS) daje bolje rezultate od često korištenih alata za sastavljanje genomskih (Velvet, SOAPdenovo) i metagenomskih (MetaVelvet, Bambus2) podataka.

Abstract (english)

Microbial communities influence almost every aspect of our lives. Metagenomics aims to expand our knowledge of those communities by analyzing DNA samples extracted directly from their environments. Metagenomic studies still rely mostly on manual interventions and single genome assembly tools which are unaware of the nature of metagenomic data. In this thesis, a Bayesian model-based hierarchical clustering approach to aid in metagenomic assembly called Sigma is presented. Sigma is combined with an optimal single genome scaffolder Opera to show that metagenomic assembly problem can be accurately and automatically reduced to single genome assembly problem by systematically exploiting assembly information. Comparisons on simulated and real datasets show that this pipeline (OperaMS) outperforms state-of-the-art single genome (Velvet, SOAPdenovo) and metagenomic (MetaVelvet, Bambus2) assembly tools.

• Keywords: metagenomika; de novo sastavljanje genoma; hijerarhijsko grupiranje; Bayesov informacijski kriterij
• Keywords (english): metagenomics; de novo assembly; hierarchical clustering; Bayesian information criterion
• Language: english
• URN:NBN: urn:nbn:hr:168:123532
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2018-02-06 08:37:39