Dr Martin Trick
Computational and Systems Biology
My research is centred on the use of computational genomics for crop improvement by enabling predictive breeding for key desirable agronomic traits. Advances in sequencing technology have revolutionised the way in which this can be achieved. It is now feasible even in crop plants with very large and complex genomes such as wheat.
Much of the work I’m engaged in uses Illumina transcriptome sequencing in order to gather data on variation both in sequence and in expression of genes sampled across populations of genotypes or across diversity panels. Associative transcriptomics can then be used to seek significant correlations between SNP or expression markers and measured phenotypic traits. This methodology can be applied even to organisms lacking established genomics resources, as de novo transcriptome assembly can always be used to create a bespoke reference sequence. The strategy has been used successfully in oilseed rape, vegetable brassicas and bread wheat and is now being applied to identify markers in ash trees for tolerance of the Ash dieback disease.
ContactTel: 01603 450557
Spatio-temporal expression dynamics differ between homologues of flowering time genes in the allopolyploid Brassica napus.
Publisher’s version: 10.1111/tpj.14020
Phytochemistry Reviews 17 p291-326
Publisher’s version: 10.1007/s11101-017-9532-2
Applied and Environmental Microbiology
Publisher’s version: 10.1128/AEM.02828-17
Molecular Breeding 38 p30
Publisher’s version: 10.1007/s11032-018-0781-6
Plant Cell 29 p1864-1882
Publisher’s version: 10.1105/tpc.17.00389
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