David originally trained in chemical engineering, pursuing a BSc(Hons) in the subject at the University of Birmingham, followed by six years as a Technologist at the Shell Pulau Bukum refinery in Singapore. In 1986, he decided to retrain as a molecular biologist, taking an MSc in molecular biology at the University of Sussex, then a PhD in cancer studies at the University of Birmingham on Epstein-Barr virus-mediated growth transformation. He continued in EBV research on a Beit ZZM fellowship before further postdoctoral research at Cambridge where he diversified into Drosophila genetics and bioinformatics.
He joined the University of Wolverhampton in 2012 and is now Senior Lecturer in Bioinformatics. He currently provides teaching in molecular biology and bioinformatics.
He is currently module leader for:-
6AB015 (Advanced biology practicals)
5BC001 (Molecular biosciences)
6BC002 (Gene manipulation and bioinformatics)
6BC004 (Genetics and genomics in pharmacy)
7BC006 (Contemporary applied bioinformatics)
and also teaches on:-
5BC003 (Molecular biosciences practical techniques)
6BC001(Advanced topics in molecular biosciences)
6PY002 (Pharmaceutical biotechnology and molecular biology)
6FS006 (Honours projects)
7AB002 (Masters lab techniques)
7BC002 (Molecular genetics and genomics)
7BC003 (DNA data mining)
Bioinformatics; molecular biology; gene drive systems; machine learning; metagenomics
Metagenomics concerns itself with the genomics populations containing significant numbers of unculturable microbes. Instead of analysing the sequences from axenic cultures, metagenomic analysis infers the constituent genomes by analysing sequences directly obtained from mixed populations. David is interested in the development and application of algorithms for metagenomic analysis. He has latterly performed an analysis of the antibiotic resistance genes present in the human microbiome and 16S analysis of stool microbiota from Canada geese (Branta canadensis).
He is also interested more generally in the application of computational techniques to the analysis of biological phenomena. Currently, he has been investigating the use of machine learning to large-scale protein classification. He also has collaborations where he has supplied bioinformatics support to projects involving somatic mutation detection within tumor-normal pairs and identification of genes involved in chemotherapeutic drug resistance using the near-haploid KBM7 cell line.