| Title | Comprehensive taxonomy, pangenomics and metabolic modelling of Streptomyces. |
|---|---|
| Supervisors | Leighton Pritchard Paul Hoskisson |
| Research Area | Computational Biology Bioinformatics Microbiology (genomics, classification metabolism, and epidemiology) Microbe-host interactions Modelling of biological systems Software and algorithm development |
| Description | Aim 1: Develop a robust taxonomic classification of the Streptomyces genus Generate an objective and definitive classification scheme for Streptomyces, which is essential for meaningful comparative genomic and pangenome analysis, and for comparison of whole-genome models. Aim 2: Pangenome analysis of Streptomyces Produce a definitive pangenome analysis for Streptomyces, using the classification scheme developed in Aim 1, to elucidate the evolutionary history of the genus, and to underpin genome mining of secondary metabolite synthetic clusters. This will link to a BBSRC TRDF proposal (LP, Dr Kate Duncan, Dr Simon Rogers [Glasgow]) to develop the NPLINKER resource for prediction of secondary metabolite structure from genomic sequence. The data generated in this objective can directly inform development of kernel-based methods for associating BGC sequence with the product metabolite mass spectra in NPLINKER. Aim 3: Whole-genome metabolic modelling of Streptomyces Generate whole-genome metabolic models for key Streptomyces groups identified in Aims 1 and 2, to support engineering potential and genome mining for BGCs. Extend these models to incorporate gene-protein-reaction rules to incorporate ‘omic datasets for realistic biological constraints. This work will additionally link to a BBSRC proposal (LP, PH, Dr Nick Tucker) that aims to reconstruct whole-genome regulatory networks from Streptomyces. |
| Techniques Used | (prior experience not necessary: you will be trained) Whole-genome classification (ANI, k-mer, and SNP-based methods) Phylogenetics (ML/Bayesian) Comparative and pan-genomic analysis (Gene family/orthologue identification) Gene family classification (Feature identification/kernel methods) Genome-scale modelling (COBRA, linear programming) Bioinformatics/software development (Python, R, MATLAB, continuous integration, testing) Data management Open Science |
| References | Mendez et al. (2019) Metabolomics doi:10.1007/s11306-019-1588-0 Wittouck et al. (2019) mSystems doi:10.1128/mSystems.00264-19 Kautsar et al. (2019) Nucleic Acids Research doi:10.1093/nar/gkz882 Ye et al. (2019) Cell doi:10.1016/j.cell.2019.07.010 Pritchard et al. (2016) Analytical Methods doi:10.1039/C5AY02550H Weimann et al. (2016) mSystems doi:10.1128/mSystems.00101-16 Pritchard and Birch (2014) Molecular Plant Pathology doi:10.1111/mpp.12210 Monk et al. (2013) PNAS doi:10.1073/pnas.1307797110 Weston et al. (2005) Bioinformatics doi:10.1093/bioinformatics/bti497 |
| Conditions | Inter- and trans-disciplinary candidates are welcome. We are interested in working with biologists, computer scientists, physicists, engineers, and others. Some prior computational and mathematical experience is preferred, and this does not need to be in a biological area. Please provide a sample of written work and, if appropriate, a link to a repository (e.g. GitHub, GitLab) containing an example of your work. Applicants should possess or be about to obtain a 1st class or 2:1 Honours degree or equivalent in a relevant discipline in addition to receipt of satisfactory references and an IELTS score of 6.5 where appropriate. |
| Bench Fee | Running costs of £10000 p.a. will be associated with this project in addition to University tuition fees. |
| Suitable For | Good first degree in biology, computer science, physics, engineering, or other - with or without industrial/commercial experience. |
| Apply Now |