Using open data and machine learning approaches to decode the regulatory regions of wheat

HALLA_E21CASE

The life sciences are becoming increasingly data rich, yet understanding how to best exploit this information remains a major challenge and a bottleneck in advancing our understanding. Machine learning (ML) and artificial intelligence (AI) offer exciting new approaches for exploring these huge datasets and generating testable hypotheses.

This PhD project is a partnership between the Earlham Institute and the RnD Innovation Centre of a major agri-tech company, BASF. The project will train you to apply ML approaches to explore and mine large genomic datasets, with the aim of identifying and decoding the regulatory regions of wheat. Understanding the impact of sequence variation within these regulatory regions is key in allowing us to identify base changes that are altering expression of genes influencing important traits such as yield, disease resistance and heat and drought tolerance.

With this PhD you have the opportunity to make a difference! The methods developed will have direct applications in crop breeding and trait engineering within the timeframe of this PhD. The successful candidate will receive extensive training and unique experience, placing them at the forefront of data driven science in the agri-tech sector.

References:

Consortium (IWGSC), The International Wheat Genome Sequencing, IWGSC RefSeq principal Investigators, Rudi Appels, Kellye Eversole, Catherine Feuillet, Beat Keller, Jane Rogers, et al. 2018. ‘Shifting the Limits in Wheat Research and Breeding Using a Fully Annotated Reference Genome’. Science 361 (6403): eaar7191. https://doi.org/10.1126/science.aar7191.

Ramírez-González, R. H., P. Borrill, D. Lang, S. A. Harrington, J. Brinton, L. Venturini, M. Davey, et al. 2018. ‘The Transcriptional Landscape of Polyploid Wheat’. Science 361 (6403): eaar6089.
https://doi.org/10.1126/science.aar6089.

L.-J. Gardiner, R. Joynson, J. Omony, R. Rusholme-Pilcher, L. Olohan, D. Lang, C. Bai, M. Hawkesford, D. Salt, M. Spannagl, K. F. X. Mayer, J. Kenny, M. Bevan, N. Hall, and A. Hall, “Hidden variation in polyploid wheat drives local adaptation.,” Genome Res, http://doi.org/10.1101/gr.233551.117