Using Big Data Approaches in Microbial Genomics
Lead Research Organisation:
University of Oxford
Department Name: Interdisciplinary Bioscience DTP
Abstract
Studying the rate at which genetic sequences change through evolution reveals an inverse correlation between the measured rate of change and time spanned through the measurement. This could potentially be an artefact of insufficient
sampling, but could also be a result of genuine evolutionary processes over large time-frames. The deep evolution of bacteria has remained a black box due to the lack of a fossil record. Using a comprehensive data-set of ancient microbial DNA we aim to contribute to the discussion whether the time-dependence of evolutionary rates is a sampling artefact and, if not, what other factors can explain it. Thus we hope to finally open up the black box that is bacterial evolution.
This project contributes towards the area of data driven biology which is of prime interest to the BBSRC.
sampling, but could also be a result of genuine evolutionary processes over large time-frames. The deep evolution of bacteria has remained a black box due to the lack of a fossil record. Using a comprehensive data-set of ancient microbial DNA we aim to contribute to the discussion whether the time-dependence of evolutionary rates is a sampling artefact and, if not, what other factors can explain it. Thus we hope to finally open up the black box that is bacterial evolution.
This project contributes towards the area of data driven biology which is of prime interest to the BBSRC.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/M011224/1 | 01/10/2015 | 31/03/2024 | |||
| 1944976 | Studentship | BB/M011224/1 | 01/10/2017 | 31/12/2021 |
| Description | Campylobacteriosis is the most common foodborne disease in the European Union. As such it is important to attribute sources precisely to condemn the mostly sporadic outbreaks using targeted health regulations. We have developed a new machine learning based source attribution using genomic information from the disease causing bacterium. The new method is about 20% more accurate than the previously most frequently used source attribution method and we hope to thus help contain campylobacteriosis. |
| Exploitation Route | The machine learning source attribution methods can be used by other researchers who isolate the underlying bacterium C. jejuni from human samples to know where the infection came from. Additionally, databases like PubMLST.org could adapt our method for automatic source attribution upon upload. |
| Sectors | Agriculture, Food and Drink,Healthcare,Pharmaceuticals and Medical Biotechnology |