(BBSRC DTP Studentship) Exploring fungal disease associations using genomic data and network models

Principal Supervisor: Professor James McInerney


Funding available for eligible UK/EU applicants.


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.

Professor McInerney laboratory website:  http://mcinerneylab.com/

Prof Delneri website: http://www.ls.manchester.ac.uk/people/profile/?personid=504

Dr. Knight website: tinyurl.com/knightFLS

Related Publications

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Subject Areas

  • Bioinformatics
  • Genetics
  • Microbiology
  • Molecular Biology
  • Structural Biology

How to Apply

This project is to be funded under the BBSRC Doctoral Training Programme.   Projects under this scheme are competitively funded; ie there are more projects advertised than are available.  If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible.  You MUST also submit an online application form, full details on how to apply can be found on the BBSRC DTP website http://www.dtpstudentships.manchester.ac.uk/