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Bioinformatics approaches for functional predictions in diverse informatics environments

Moolhuijzen, Paula (2011) Bioinformatics approaches for functional predictions in diverse informatics environments. PhD thesis, Murdoch University.

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      Abstract

      Bioinformatics is the scientific discipline that collates, integrates and analyses data and information sets for the life sciences. Critically important in agricultural and biomedical fields, there is a pressing need to integrate large and diverse data sets into biologically significant information. This places major challenges on research strategies and resources (data repositories, computer infrastructure and software) required to integrate relevant data and analysis workflows. These challenges include:

       The construction of processes to integrate data from disparate and diverse resources and legacy systems that have variable data formats, qualities, availability and accessibility constraints.
       Substantially contributing to hypothesis driven research for biologically significant information.

      The hypothesis proposed in this thesis is that in organisms from divergent origins, with differing data availability and analysis resources, in silico approaches can identify genomic targets in a range of disease systems. The particular aims were to:

      1. Overcome data constraints that impact analysis of different organisms.
      2. Make functional genomic predictions in diverse biological systems.
      3. Identify specific genomic targets for diagnostics and therapeutics in diverse disease mechanisms.

      In order to test the hypothesis three case studies in human cancer, pathogenic bacteria, and parasitic arthropod were selected, the results are as follows.

      In case study 1 sequence information was integrated to make novel predictions, and generate novel findings for the role of the Alu repeat element in cancer. An under representation of Alu was found in cancerous transcript and most noncancerous Alu transcript found were of an unknown function. These findings led to an Alu-mediated siRNA model for the down regulation of Alu containing mRNA in cancer.

      Case study 2, comparative genomic analyses identified venereal diagnostic targets that discriminated Campylobacter fetus subspecies venerealis from other Campylobacter species and subspecies. Plasmid borne virulence Type IV secretory pathway genes specificity however varied for biovars, compromising their use for diagnostics. These findings resulted in the targeted sequencing of Campylobacter fetus subspecies venerealis biovar genomes.

      Case study 3, in cattle tick ectoparasite (Rhipicephalus microplus), a large highly complex and under researched genome, transcript sequence was analysed and tick vaccination targets identified. These vaccine candidates successfully imparted immunity in the bovine host. The developed high throughput vaccine target identification system is now being applied to other disease systems.
      Through the shared bioinformatics approaches, novel functional targets and models in disease were determined.
      This thesis has developed and demonstrated in silico approaches for:

      1. The collation, annotation and integration of data from divergent organisms with variable data constraints.
      2. Novel functional predictions in diverse biological systems.
      3. Novel vaccine and diagnostic candidate identification, in diverse disease mechanisms, substantially contributing to hypothesis driven research.

      Publication Type: Thesis (PhD)
      Murdoch Affiliation: School of Information Technology
      Supervisor: Appels, Rudi and Bellgard, Matthew
      URI: http://researchrepository.murdoch.edu.au/id/eprint/5804
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