New molecular technologies have revolutionised the fields of genomics and genetics, offering great potential to deepen our understanding of human diseases. We in the Genomics and Genetics Group are working with these powerful technologies with the ultimate aim of discovery and design of new approaches for effective diagnosis, prognosis and eventual treatment. Our group comprises both computational and experimental researchers with expertise in many areas and is part of a wide network of national and international collaborations.
We are part of the Biomedical Research Group within the University of Plymouth's Institute of Translational and Stratified Medicine (ITSMED), working closely with the Plymouth University Systems Biology Centre and are a partner in the delivery of the NHS/MSc genomics medicine of the 100 000 Genomes Project.
- Biomarker discovery (Xinzhong Li): Molecular biomarker discovery and molecular diagnosis of brain disorders.
- Gene regulatory networks (Matthias Futschik): Reconstruction of gene regulatory networks based on promotor analyses, co-expression, miRNA profiling and chromatin immunoprecipitation combined with microarray and next generation sequencing technology (ChIP-chip/ChIP-sep).
- Genome-wide variation detection (Elaine Green): Next generation sequencing detection of LINEs-1 and their role in neurodegenerative diseases. Identification of SNP and structural variation using genome-wide microarrays.
- Microbial genomics (Mat Upton): Genome sequencing and sequence typing of pathogenic and drug resistant bacteria using Nanopore platform.
- Molecular diagnostics (Tracey Madgett): Genomic sequencing to define novel blood group genotypes and support non-invasive prenatal diagnosis.
- Genome sequence analysis (Robert Belshaw): Detecting repeat sequences within the human and animal model genome sequences to investigate the role of endogenous retroviruses.
- Transcriptomics (Neil Avent, Robert Belshaw, Matthias Futschik, Xinzhong Li, Tracey Madgett, Vasilis Lenis): Analysis of microarray and RNA-seq data and their integration with clinical data. Meta-analysis of gene expression data of diseases. Transcriptome profiling in erythroid lineage cells to define pathways of red cell senescence.