Vesth, Tammi Camilla

I november 2013 var der omkring 21.000 prokaryote genom sekvenser i offentlige databaser og tallet vokser dagligt og det forventes at endnu 20.000 sekvenser vil blive publiceret i løbet af 2014. En essential del af analysen af alle disse genom sekvenser er den funktionelle annotering, den beskrivelse som forklarer hvilken funktion genet har i cellen. Denne process ebregrænses af flere forskellige faktorer, her iblandt definitionen af funktion, en definition der kan være mere eller mindre specifik, samt hvor mange gener der rent faktisk kan tilknyttes en kendt cellulær funktion.Dette speciale beskriver udviklingen af nye værktøjer til funktionel annotering og sammenligning af disse annoteringer mellem flere genomer samt general sammenligning af genom sekvenser. I takt med at der foreligger flere og flere sekvenser vokser behovet for standard procedure i analysen af disse data. Systemet CMG-biotools presenteres her som et exemple på et sådant system og blev brugt i analysen af genomer fra klassen Negativicutes, en gruppe organismer tæt beslægtet med Gram positive organismer men har en anderledes cell væg komposition der gør dem Gram negative, som navnet også antyder. Resultatet af denne analyse viste at denne klasse har meget lidt sekvens homologi med andre sekventerede genomer, og da funktionel annotering ofte hviler på netop sekvens homologi, besværligører dette annoteringen.Inspireret af denne problematik, startede udviklingen af an metode til funktionel annotering baseret på publiceret data, CMGfunc. Funktionelt relaterede grupper af proteiner blev defineret ud fra konserverede protein domæner således at hver gruppe representerede en protein funktion. Hver funktion blev derefter modeleret ud fra en neural netværk model og hver model blev evalueret ud fra dens even til at genkende sande positiver og negativer, det vil sige, proteiner der faktisk besad eller ikke besad den funktion some modellen representerede. Modellerne blev brug til at annotere en række protein uden kendt funktion og annoterede 98% af disse. Præcessionen af de funktionelle modeller blev estimeret ud fra data fra projektet Critical Assessment of Functional Annotation (CAFA), og in 60% af tilfældende forudsage CMGfunc modellerne den rigtige funktion.Dette projekt har belyst mulige løsninger på udfordringerne indenfor sammenlignelig funktionel annotering og computer analyse af sekvens data. I dette arbejde er der blevet udviklet en række forslag til reproducerbare analyse metoder samt modeller til funktionel annotering. Selvom der stadig er udfordringer der skal takles indenfor disse felter tilføres store resourcer til netop denne type analyse og det er klart fremskridt i syne i takt med at mange nye metoder testes.In November 2013, there was around 21.000 different prokaryotic genomes sequenced and publicly available, and the number is growing daily with another 20.000 or more genomes expected to be sequenced and deposited by the end of 2014. An important part of the analysis of this data is the functional annotation of genes – the descriptions assigned to genes that describe the likely function of the encoded proteins. This process is limited by several factors, including the definition of a function which can be more or less specific as well as how many genes can actually be assigned a function based on known functions.This thesis describes the development of new tools for comparative functional annotation and a system for comparative genomics in general. As novel sequenced genomes are becoming more readily available, there is a need for standard analysis tools. The system CMG-biotools is presented here as an example of such a system and was used to analyze a set of genomes from the Negativicutes class, a group of bacteria closely related to Gram positives but which has a different cell wall structure and stains Gram negative, as the name indicates. The results of this work show that genomes of this class have very little homology to other known genomes making functional annotation based on sequence similarity very difficult.Inspired in part by this analysis, an approach for comparative functional annotation was created based public sequenced genomes, CMGfunc. Functionally related groups of proteins were clustered based on sequence domains so that each group represented a protein function. Each function was then modeled using Arti- ficial Neural Networks (ANN) and the model was evaluated based on its ability to identify true positives and negatives, that is proteins with or without the function of the model. The models were used to annotate a number of proteins without functional annotations and predicted functions for 98% of the genes. Evaluation of the precision of the method was performed, using data from the Critical Assessment of Functional Annotation (CAFA) project, and correct predictions were made in about 60% of the cases.This project has highlighted the difficulties and challenges in functional annotation and computational analysis of sequence data. It has provided possible solutions for creating reproducible pipelines for comparative genomics as well as constructed a number of functional models not based on sequence similarity. Although much work is still left to be done, resources are flowing into the area of sequence analysis and progress is being made every day. As such, many different approach are being tried out and tested which will, in time, improve the knowledge gained from sequencing genomes

Online Research Database In Technology

Determining and comparing protein function in Bacterial genome sequences

http://orbit.dtu.dk/ws/files/103682316/PhDThesis..PDF

Determining and comparing protein function in Bacterial genome sequences

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology