Personal Chair of Bioinformatics and Computational Biology
Willingness to take PhD students: Yes
2014 | Doctor of Philosophy (PhD), University of Edinburgh Bioinformatic analysis of genome-scale data reveals insights into host-pathogen interactions in farm animals |
1997 | Master in Science, University of York |
1996 | Bachelor of Science, University of York |
I am interested in what large datasets tell us about biological function, and how we can correlate patterns in big data with phenotypes of interest in farm animal health, disease and productivity.
Mick Watson is a bioinformatician and genome scientist, with almost 20 years’ experience in industry and academia. He was involved in the implementation and management of pipelines for functional genomics at GlaxoWellcome, SNP discovery at Incyte Genomics and Target Discovery at Paradigm Therapeutics, before joining the Institute for Animal Health as Head of Bioinformatics in 2002. He joined the Roslin Institute in 2010 as Director of ARK-Genomics and research group leader. His group's research focuses on the use of computational and mathematical techniques to understand genome function with an emphasis on systems of relevance to animal health and food security. Publications include both primary bioinformatics research papers and collaborative research in a variety of technical and scientific journals. The outputs from his research have included novel algorithm development, as well as the application of bioinformatics techniques to microbial and meta-genomics.
Research output: Contribution to journal › Article
Research output: Contribution to journal › Article
Research output: Contribution to journal › Article
Research output: Contribution to journal › Article
Research output: Contribution to journal › Article
Activity: Publication peer-review and editorial work types › Editorial activity
Activity: Publication peer-review and editorial work types › Editorial activity
Activity: Publication peer-review and editorial work types › Editorial activity
Press/Media: Research
Press/Media: Research
Press/Media: Research
ID: 169386