I was born and raised in Paarl, South Africa, and developed an interest in both mathematics and biology early on. I was intrigued by the intersection between these two fields. This led me to my undergraduate studies towards a Bachelor of Science in Mathematical Sciences, with a focus in Biomathematics, at Stellenbosch University, South Africa in 2009. This was a new program at Stellenbosch University, and provided in-depth courses in both mathematics (including calculus, real analysis, linear algebra, discrete mathematics and combinatorics, foundations in abstract mathematics, and biomathematics) as well as biochemistry, genetics, biotechnology and scientific computing/programming. This unique program at the university provided me with a solid foundation of knowledge in molecular biology as well as technical mathematical and computational skills. During my 4th year (Bachelor of Science Honors) I joined a computational biology lab in the Institute for Wine Biotechnology, an industry-funded institute at the University of Stellenbosch where multi-omic research was focused on investigating, improving and optimizing the grapevine plant, metabolite content of grapes, and microbes involved in the fermentation process. As part of the computational biology lab, I gained experience working with multiple large -omics data types and high performance computing, and had the opportunity to begin working on self-led and collaborative research projects.

I obtained my MSc in Biotechnology at Stellenbosch University in the computational biology lab at the Institute for Wine Biotechnology. This was a 2-year full time research masters in which my work focused on the development and application of network approaches for the construction, analysis and visualization of phylogenomic and transcriptomic networks, as well as several approaches for network topology characterization and comparison. The work from my MSc was published in the following:

Weighill et al. (2015) 3-way Networks: Application of Hypergraphs for Modelling Increased Complexity in Comparative Genomics. PLOS Computational Biology 11, e1004079. doi.org/10.1371/journal.pcbi.1004079 

Weighill et al. (2017) Network Metamodeling: Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology, in: Nookaew, I. (Ed.), Network Biology, Advances in Biochemical Engineering/Biotechnology. Springer International Publishing, Cham, pp. 143–183. doi.org/10.1007/10_2016_46 

I obtained my PhD from the Bredesen Center for Interdisciplinary Research and Graduate Education, a joint program between the Department of Energy's Oak Ridge National Laboratory (ORNL) and the University of Tennessee, Knoxville. Bredesen Center students undertake collaborative, interdisciplinary research in all areas on Energy Science. I joined the Computational Biology group in the Biosciences at ORNL, where my research focused on multi-omic data integration with applications in bioenergy, in particular, the bioenergy feedstock Populus trichicarpa.

I developed the Lines Of Evidence (LOE) method (Weighill et al., 2018) to integrate genome variant, gene expression, DNA methylation, and metabolite phenotype data using association networks. LOE scores quantify the lines of evidence linking genes to target functions across network layers. I applied LOE to identify new candidate genes involved in lignin and cell wall functions in the bioenergy crop, Populus trichocarpa (Weighill et al. 2018; Furches, Kainer and Weighill et al. 2019). As another example of my approach to network-based analyses, I developed Multi-Phenotype Association (MPA) Decomposition (Weighill et al. 2019a). The method uses a network decomposition approach that "unravels'' the pleiotropic signatures of genes in large-scale genome-wide Quantitative Trait Loci data, allowing identification of pleiotropic signatures and clustering of genes based these signatures. 

My work has also involved the development/application of similarity metrics for network construction (Joubert, Weighill, et al. 2018). This included the COMET suite of similarity metrics for genomic network construction, which received the Association for Computing Machines (ACM) Gordon Bell Prize for supercomputing. The ACM is one of the premier professional societies in computer science and the Gordon Bell Prize recognizes outstanding achievement in high-performance computing, rewarding innovation in applying high-performance computing in science, engineering, and large-scale data analytics.

Furches, A., Kainer, D., Weighill, D et al. 2019. Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence. Front. Plant Sci. 10. https://doi.org/10.3389/fpls.2019.01249

Weighill, D 2019a. et al. Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships. Front. Genet. 10. https://doi.org/10.3389/fgene.2019.00417

Weighill, D et al. 2019b. Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation. Front. Genet. 10. doi.org/10.3389/fgene.2019.00487

Joubert, W., Nance, J., Weighill, D., Jacobson, D., 2018. Parallel accelerated vector similarity calculations for genomics applications. Parallel Computing 75, 130–145. doi.org/10.1016/j.parco.2018.03.009

Joubert, W., Weighill, D et al. 2018. Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction, in: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. Presented at the SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 717–730. doi.org/10.1109/SC.2018.00060