Dr Tim Beck


School/Department: Genetics and Genome Biology, Department of



Dr Beck is a bioinformatician interested in phenotype semantics and how biological, biomedical and clinical observation data can be understood and compared by computers using ontologies.  Dr Beck has applied this interest to different species during his career to date.

Dr Beck completed his bioinformatics PhD developing a phenotype ontology for fission yeast at the University of Sussex in 2007.  This was followed by a Career Development Fellowship with the Mammalian Genetics Unit at MRC Harwell working on projects to enable the annotation of mouse phenotypes with ontologies in online databases.  Dr Beck then took up a postdoctoral position at the University of Leicester focussing on the integration and comparison of human genotype and phenotype data.

After a spell gaining data science experience as a Research Associate, Dr Beck attained a UKRI Innovation Fellowship.  Hosted by the NIHR Leicester Biomedical Research Centre, the fellowship involved studying the role of ontologies in accelerating the harmonisation and integration of clinical and health data.  Following this, Dr Beck was appointed as a lecturer in 2021.  Dr Beck works at both the Cardiovascular Research Centre at the Glenfield Hospital and the Adrian Building on the University campus.



Dr Beck’s main focus is the use of semantics to connect health-related big data to enable them to be aligned and compared, and for larger participant/sample sets to be discovered for analysis.  This involves developing methods and tools to harmonise both structured and unstructured data.

To be able to automatically compare quantitative and qualitative values across big data, computers need to know if the values have the same meaning between data sets, and the semantic rigour required to do this at scale is enabled by the application of ontologies.

Dr Beck leads the development of the world’s largest open-access genome-wide association study database, GWAS Central (  The resource provides advanced tools to allow visualisation, interrogation and comparison of summary-level genetic association data sets from the perspective of genes, genome regions and phenotypes.

Dr Beck and his team develop natural language processing tools to extract biomedical information from unstructured data.  Through an ELIXIR funded project, text mining algorithms are being applied to the scientific literature to support GWAS Central biocuration activities.



CS Yeung, T Beck and JM Posma. MetaboListem and TABoLiSTM: two deep learning algorithms for metabolite named entity recognition. Metabolites. 2022;12(4):276. 

J Rambla, M Baudis, R Ariosa, T Beck, LA Fromont, A Navarro, R Paloots, M Rueda, G Saunders, B Singh, JD Spalding, J Törnroos, C Vasallo, CD Veal and AJ Brookes. Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond. Human Mutation 2022;43(6):791-799. 

T Beck, T Shorter, Y Hu, Z Li, S Sun, CM Popovici, NAR McQuibban, F Makraduli, CS Yeung, T Rowlands and JM Posma. Auto-CORPus: a natural language processing tool for standardising and reusing biomedical literature. Frontiers in Digital Health. 2022;4:788124. 

HL Rehm, AJH Page, L Smith, JB Adams, G Alterovitz, LJ Babb, MP Barkley, M Baudis, MJS Beauvais, T Beck, JS Beckmann, S Beltran, D Bernick, A Bernier, JK Bonfield, TF Boughtwood, G Bourque, SR Bowers, AJ Brookes, M Brudno, MH Brush, D Bujold, T Burdett, OJ Buske, MN Cabili, DL Cameron et al. [202 authors]. GA4GH: International policies and standards for data sharing across genomic research and healthcare. Cell Genomics 2021;1(2):100029. 

T Beck, T Shorter and AJ Brookes. GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies. Nucleic Acids Research 2020;48(D1):D933-D940. 

MP Keller, ME Rabaglia, KL Schueler, DS Stapleton, DM Gatti, M Vincent, KA Mitok, Z Wang, T Ishimura, SP Simonett, CH Emfinger, R Das, T Beck, C Kendziorski, KW Broman, BS Yandell, GA Churchill and AD Attie. Gene loci associated with insulin secretion in islets from non-diabetic mice. Journal of Clinical Investigation 2019;130:4419-4432. 



Dr Beck is the primary supervisor of a current PhD project: Unlocking the potential of biomedical data to understand the causes of disease, and has supervised or co-supervised over 15 Masters projects.

Dr Beck is keen to supervise future students (PhD and Masters) in the areas detailed on the Research tab.



Dr Beck's teaching includes bioinformatics lectures and practical workshops for Midlands Integrative Biosciences Training Partnership (MIBTP) masterclasses, and the Natural Sciences NS3023 module.

Press and media

  • Mammalian genetic association data
  • Phenotype semantics and ontologies
  • Biomedical text analytics


  • Member of the ELIXIR-UK Management and Steering Committees
  • Co-founder and co-lead of the ELIXIR Health Data Focus Group
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