Dr. Laura-Jayne Gardiner is a computational biologist with a background in molecular biology. Laura's work mainly focuses on the development of methods and pipelines to analyse large scale next-generation sequencing (NGS) datasets to gain biological insight for life science research. She completed her PhD at the University of Liverpool where she focused on the genomic analysis of crops with the goal of understanding key adaptive traits and their development. Laura was awarded the Monogram early career excellence award for her PhD work that was published in the academic journal Genome Biology surveying DNA methylation in bread wheat.
After her PhD, Laura moved to the Earlham Institute (Norwich, UK) to take the role of Senior Postdoctoral Researcher in Prof Anthony Hall's group. Here she was primarily focused on the genetic improvement of bread wheat and studied the role of epigenetics in plant development and adaptation. Wheat's genome is 5X the size of the human genome, and with poorer genome resource availability, its analysis is challenging. Her post-doctoral work analysing a diverse set of wheat varieties, which enabled her to associate DNA methylaton with adaptation of wheat to local environment was reported as "Epic" and as uncovering "the hidden genetic secrets that give wheat its remarkable ability for local adaptation" by the British Biological Sciences Research Council (BBSRC). This work was later published in academic journals including Genome Research and presented at international conferences.
Currently, Laura is a Research Staff Member at IBM Research UK within the Application of AI team. Laura works at the interface of computational science and biology to integrate HPC and AI into the analysis of large scale sequencing datasets. She is applying Machine Learning to improve both the processing and integration of multi-omic datasets and to predict key phenotypes from multi-omic data ranging from drug toxicity in humans to complex gene expression patterns in plants. Laura frequently works with a range of data sources including genomic, transcriptomic, epigenomic and metagenomic datasets.