Exploring fungal disease associations using genomic data and network models
Lead Research Organisation:
University of Manchester
Department Name: School of Biological Sciences
Abstract
High throughput genome sequencing has resulted in an abundance of data. Lagging behind by quite a long way are the tools and approaches for understanding these data. Methods of analysis that were excellent for the small-scale approaches of 20 years ago are not capable of analysing thousands of genomes quickly and efficiently. The 1,000 fungal genomes initiative (http://1000.fungalgenomes.org/home/) is providing the genome sequences of the broad diversity of fungi. We will develop approaches that can extract the most amount of scientific knowledge from this data and other datasets like it. We will use graph approaches, similar to those used by FaceBook and Twitter to analyse relationships in massive social media data. We have been using graph approaches for some time and they have helped us to understand the origins of eukaryotes (1, 2), the history of antibiotics (3), the flows of genes around the prokaryotic world (4) and the differences in how prokaryotic and eukaryotic genomes are constructed (5). We will construct k-partite graphs using as nodes the encoded protein domains, the predicted genes, the complete genomes and the ecological niche (place of isolation) of the species in question. We will apply community detection algorithms in order to find out associations between these nodes. We will identify when protein domains are significantly associated with different kinds of diseases and those that are not associated with disease. The work will involve computer programming and data analysis. Training will be provided to any candidate that doesn't program.
References:
1. Alvarez-Ponce D, Lopez P, Bapteste E, McInerney JO. Gene similarity networks provide tools for understanding eukaryote origins and evolution. Proceedings of the National Academy of Sciences USA. 2013;110(17):E1594-603.
2. McInerney JO, O'Connell MJ, Pisani D. The hybrid nature of the Eukaryota and a consilient view of life on Earth. Nature Reviews Microbiol. 2014; 12(6): 449-55.
3. Coleman O, Hogan R, McGoldrick N, Rudden N, McInerney JO. Evolution by Pervasive Gene Fusion in Antibiotic Resistance and Antibiotic Synthesizing Genes. Computation. 2015; 3(2): 114-27.
4. Nelson-Sathi S, Sousa FL, Roettger M, Lozada-Chavez N, Thiergart T, Janssen A, et al. Origins of major archaeal clades correspond to gene acquisitions from bacteria. Nature. 2015; 517(7532): 77-80.
5. Ku C, Nelson-Sathi S, Roettger M, Sousa FL, Lockhart PJ, Bryant D, et al. Endosymbiotic origin and differential loss of eukaryotic genes. Nature. 2015; 524(7566):427-32.
References:
1. Alvarez-Ponce D, Lopez P, Bapteste E, McInerney JO. Gene similarity networks provide tools for understanding eukaryote origins and evolution. Proceedings of the National Academy of Sciences USA. 2013;110(17):E1594-603.
2. McInerney JO, O'Connell MJ, Pisani D. The hybrid nature of the Eukaryota and a consilient view of life on Earth. Nature Reviews Microbiol. 2014; 12(6): 449-55.
3. Coleman O, Hogan R, McGoldrick N, Rudden N, McInerney JO. Evolution by Pervasive Gene Fusion in Antibiotic Resistance and Antibiotic Synthesizing Genes. Computation. 2015; 3(2): 114-27.
4. Nelson-Sathi S, Sousa FL, Roettger M, Lozada-Chavez N, Thiergart T, Janssen A, et al. Origins of major archaeal clades correspond to gene acquisitions from bacteria. Nature. 2015; 517(7532): 77-80.
5. Ku C, Nelson-Sathi S, Roettger M, Sousa FL, Lockhart PJ, Bryant D, et al. Endosymbiotic origin and differential loss of eukaryotic genes. Nature. 2015; 524(7566):427-32.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/M011208/1 | 01/10/2015 | 31/03/2024 | |||
| 1791548 | Studentship | BB/M011208/1 | 01/10/2016 | 30/09/2020 |
| Description | On our first project we have discovered that plasmids, small pieces of DNA that are shared between many prokariotes but also some eukaryotes, have some structure in the way they are shared. There are sequence changes, physical properties and functions that plasmid share that depends on the environment where the hosts live and some of the functions they perform. On our second project we have provided a new revised phylogeny for the fungi using data from the 1000 Fungal Genomes Project, where we can reliably place some species whose place in the phylogeny was in doubt before and, mainly, we provide evicence that the class know as Microsporida is a sister taxa to the fungi. This has been a problematic group to place on a tree since their DNA has changed rapidly compared to other organisms and their genomes are very reduced because they are speciallized intracellular parasites. However we do not have enough data to reliably place a similar class, Rozellidea, in the phylogeny. |
| Exploitation Route | The plasmid work can be used to potentially research further in a new way of classification for plasmids. The work could also be expanded with a bigger dataset where more structure about how organisms share genetic information through plasmids could be discovered. The phylogenetic tree could be used by many researchers to use it in regards to relation between different fungal species. It can also be expanded upon with more focused smaller phylogenies that are particular to some parts of the tree to place even more species. |
| Sectors | Agriculture, Food and Drink,Education,Culture, Heritage, Museums and Collections |