Home > Bioinformatics

Bioinformatics

Bioinformatics is an interdisciplinary research field, which aims to develop novel methods and software tools to further the understanding of complex biological data.  At Moredun, a range of bioinformatics tools and methodologies are applied to improve our understanding of livestock disease research across a number of projects, for example:

  • Performing transcriptomic analysis of host and pathogens
  • Whole genome sequencing, assembly and annotation of complex pathogens
  • Pathway and network analysis to enable elucidation of signalling pathways and gene networks
  • Using high parallel DNA sequencing to determine the entire DNA content of a viral or bacterial genome. From information encoded in this sequence it is possible to deduce what proteins this organism expresses and how this expression is regulated by interactions with their environment
  • Comparing DNA and protein sequences between organisms with different growth characteristics in order to identify candidate proteins for making vaccines or for differential diagnostic tests
  • Carrying out a detailed comparison of DNA and protein sequence (in conjunction with colleagues at BioSS) across a range of similar organisms allows us to infer how they are related and for example, how important traits such as pathogenicity have arisen and spread within populations of bacteria
  • In conjunction with colleagues at BIOSS developing novel analytical pipelines to analyse complex mass spectral profiles of closely related bacterial isolates with a view to building rapid isolate discrimination tools.
  • Identifying and quantifying information about an organism's molecular makeup
  • Interpreting information generated by transcriptional analysis of an organism or from Moredun’s quantitative mass spectrometry facilities
  • Characterising an animal’s ‘normal ’ microbial flora of gut, lung etc. and then comparing this to situations where it is changed by disease, antimicrobials, immune compromise, stress and a host of external factors
  • Taking large, complex and cryptic genomic and proteomic datasets and presenting them in a comprehensible format, filtering and removing redundant data
  • Using relational databases as a means of storing and interrogating genomic and transcriptomic datasets
  • Developing databases for the management and analysis of disease trends