Bachelor and master thesis projects

Bachelor and Master theses

Our research projects provide permanently new topics for bachelor and master theses. We are happy to adapt the topic of a thesis work to your experience and interests.

We currently propose new topics bachelor and master theses within these ongoing research projects:

1. Rapid calculation of protein family assignments by machine learning (deep learning): assessment and improvement of deepfam
2. Prediction of metabolic pathways and molecular subsystems from genome contents: development of new trait prediction models based on genome-scale metabolic models and gap filling.

If you are interested and have a good background in bioinformatics, we are very happy to present you these topics in detail during a personal meeting. Please contact Thomas Rattei for more information.

Project example 1: The evolution and sequence-based prediction of the Type III secretion signal

Many bacteria can manipulate their environment by the secretion of proteins, which act outside the bacterial cell. An important example is the interaction of symbiotic and pathogenic bacteria with their respective host cells. Several functions of the secreted proteins ("effectors") have been so far described, as e.g. influencing the immune defense or preventing cell death of the host. Up to date, seven different secretion systems have been described. Three of them (Type III, IV and VI) allow penetrating host cell membranes and injecting proteins directly into the cytosol of host cells. Among them, the Type III secretion apparatus (TTSS) is encoded in the genomes of many, mainly pathogenic or symbiotic, Gramnegative bacteria and is a key factor for the virulence of pathogens. It spans both bacterial membranes and enters the host cytosol to transport a variety of effectors directly into the host cell in a specific and energy dependent manner. The TTSS is evolutionary related to the flagellar system and well conserved across a wide range of taxa. Several effector proteins have been described so far.

Despite the acquisition by descent or horizontal gene transfer, the de-novo invention of new effectors is a necessary prerequisite for the adaptation of a pathogen or symbiont to changing environments and new hosts. The theory of terminal re-assortment can explain the evolution of many effectors, but nevertheless, an initial set of effectors (in particular, effector Ntermini with a TTSS specific secretion signal) must exist before this process can occur. Initial signals might be acquired by point mutations towards an efficient signal, which might be facilitated by acquisition of an initial pre-signal from the 50 intergenic space. These two principles seem to be likely considering the computational models of the N-terminal secretion signal, which predict an unusual amino acid composition as the signals core.

In this project, we will therefore investigate the evolution of effector sequences from different pathogenic and non-pathogenic bacteria by using comparative genomics methods. This will allow us to redefine the length of the N-terminal signal and to develop an improved prediction method.

Prerequisites:

• Enrolment in biology with strong interest in computational and statistical methods OR enrolment in bioinformatics OR enrolment in computational science
• Basic background in sequence analysis
• Basic background in machine learning and statistics

Contact: Thomas Rattei (thomas.rattei@univie.ac.at)