The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

To find a particular PI's email or look up other PI details, use the menu at the top of this page (PIs tab).

Size matters! - Bacterial genome size and its implications in ecological traits and niche distribution.
Summary: Lots of sampling, lots of experiments, lots of (existing and new) data and analyses and lots of fun! Detailed: Body size in animals has significant correlations with ecological traits, such as metabolism, population size, ecological range and niche breadth. However much less is known whether these observations hold true in the bacterial kingdom. Studies indicate that the interaction between an organism and its ecological niche, for example, resource availability and environmental stability, selects the genome size of the species. The exact mechanisms driving the genome sizes are still not well understood. It has, however, been speculated that species living in invariant niches tend to have small genomes, as stability acts to reduce genome size due the metabolic burden of replicating DNA with no adaptive value such as in obligatory and intracellular pathogens or mutualists. Due to their metabolic diversity, species with large genomes are potentially able to tackle a wider range of environmental conditions and tend to be more ecologically successful where resources are scarce but diverse, and where there is little penalty for slow growth. As a result of these two competing "forces", bacterial genome sizes have previously been shown to exhibit a bimodal distribution. However, a recent study by the supervisory team revealed that there is a significant bias in the sequencing efforts towards a certain group of species, and that correcting the bias using species nomenclature and clustering of the 16S rRNA gene, results in a unimodal rather than the previously published bimodal distribution (Gweon et al., 2017). We however do not reject the possibility of a bimodal distribution in bacterial genome size, and there are still a number of observations which point to the possibility of a bimodal distribution including the bimodality in flow cytometric analysis of bacterial DNA (Schattenhofer et al., 2011; Morán et al., 2015) and discovery of a whole new array of species with small genomes from environmental metagenomes (Giovannoni et al., 2014; Morán et al., 2015). In this project, we will combine both wetlab and bioinformatics to attempt to solve these unanswered questions by (i) using existing and new computational methods, including predictive modelling and machine learning approaches for assessing publicly available databases (50%); and (ii) fieldwork coupled with extensive lab techniques including flow cytometry and NGS to examine distribution of genome sizes in various environmental niches (50%). The project will be ideal for a graduate student (biological science or non-biological background) with existing computational or mathematical skills, and who has an interest in collating theories and methods. When applying for this position please provide evidence of the following: Interest in the aims of the QMEE CDT, Research experience/potential, Academic training (degree class obtained or expected in BSc/MSc; at least two academic references). Gweon HS, Bailey MJ, Read DS. Assessment of the bimodality in the distribution of bacterial genome sizes. ISME J. 2017 Schattenhofer M et al. Phylogenetic characterisation of picoplanktonic populations with high and low nucleic acid content in the North Atlantic Ocean. Syst Appl Microbiol 2011 Giovannoni SJ et al. Implications of streamlining theory for microbial ecology. ISME J. 2014 Morán XAG et al. More, smaller bacteria in response to ocean's warming? 2015 Proc R Soc B
Soon Gweon
Daniel Read
Computing, Quantitative data analysis, Ecological observations / data collection
Soon Gweon
Quantitative data collection of bacterial genomes from public repositories. Use of data for model construction and validation. Development of models for genome size distribution and responses to environmental niches. Interpretation of flow cytometry data, microbial community and structure data.
This proposal will open novel research themes by developing a new computational approach to analyse the vast amount of genomics data from public repositories. This project will unite bioinformatics, lab, and mathematical ecology to gain new insights into bacterial genome evolution and distribution.
Fundamental questions about genome size evolution and distribution in prokaryotes; the use and development of statistical and computational tools; Development of theory, and integration of theory into models; mathematical modelling of ecological systems.
Bacteria play a critical role in almost all aspects of society, from mediating biogeochemical cycles, supporting ecosystems, and influencing human and animal health. Studying the role that eco-evolutionary forces play in shaping bacterial genomes and the functions they perform will enable a better understanding of the pressures that maintain these functions and how human activity influences them.
The information and approaches developed by the project will lead to a fundamentally new understanding of the pressures that shape bacterial genome evolution, and the relevance this has for the functioning of microbial communities. Understanding communities through traits is an emergent topic in microbial ecology and this will move forward our understanding of the links between genomes and traits.
This project combines bioinformatics/computational biology combined with aspects of ecology and evolution (Gweon), and field sampling, ecology, lab and DNA sequencing (Read) to bridge between bioinformatics, ecology and evolution.
Population genetics and evolution, Environmental microbiology, Ecological/Evolutionary tools, technology & methods
You will receive training in computer programming, computational and statistical techniques, bioinformatics pipeline development and wet-lab and field sampling methods. As well as project specific training, you will have access to a wide range of generic and research skill training opportunities offered by the host instruction, project partners and the QMEE CDT.
University of Reading, Centre for Ecology & Hydrology
2019-05-31 14:34:09