Comparative transcriptome profiling in wild species
: uncovering gene expression signatures of mating systems

  • Nina Ockendon

Student thesis: Doctoral ThesisPhD


Understanding the molecular processes underlying adaptation of complex phenotypes presents major challenges in evolutionary biology. An important question currently is how to accurately use the plethora of ‘omics data to better understand ecological variation. Using RNA-seq transcriptome data from many lineages, I demonstrate the power of this data type when studying the molecular basis of complex phenotypes. My work has produced three major results. Firstly, I have integrated bacterial RNA-seq data with high throughput phenotype microarrays, providing the first indication of functional pathways implicated at genomic and phenotypic levels in trait evolution related to host switching and proliferation in Photorhabdus species. Secondly, since genome sequence data are currently unavailable for most species, I present an optimised methodology for RNA-seq transcriptome annotation for species with no sequenced genome. This shows that direct mapping of RNA-seq short reads to a reference genome – from the same species or a closely-related species – is the most effective, accurate and least functionally biased strategy for annotating transcriptomes compared to currently popular transcriptome assembly methods. Thirdly, I have contributed genomic resources to the scientific community by obtaining brain transcriptomes from two non-sequenced songbird species that represent interesting ecological models of mating behaviour. Applying my direct genome mapping annotation strategy to the novel data, I have described the transcriptomes via gene expression profiling and functional characterisation, amongst others methods. I have provided a first indication of genes differentially regulated during the breeding seasons of typically monogamous and polygamous songbirds. Overall, I have provided insight into the performance of state-of-the-art high throughput genomic and phenotypic analyses, identifying genes and functional pathways potentially important in the evolution and development of specific complex phenotypes across a variety of taxa. Thus, my work provides an excellent basis for further studies to disentangle how these phenotypes evolved and dissect the mechanisms by which they operate.
Date of Award2 Mar 2015
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorAraxi Urrutia (Supervisor) & Tamas Szekely (Supervisor)

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