Genomic and Transcriptomic Studies on Non-Model Organisms

As the advance in high-throughput sequencing enables the generation of large volumes of genomic information, it provides researchers the opportunity to study non-model organisms even in the absence of a fully sequenced genome. The hugely advantageous progress calls for powerful sequencing assembly algorithms as these technologies also raise challenging assembly problems: (1) Some RNA products are highly expressed but others may have much lower expression level. (2) Data cannot easily be represented as linear structure, due to post-transcriptional modification like alternative splicing. (3) Conserved sequences in domains in gene families can result in assembly errors, (4) Sequencing errors due to technique limitations. Useful assembly algorithms are required to overcome the difficulties above. In these studies, there is often a need to identify similar transcripts in non-model organisms to transcripts found in related organisms. The traditional approach to address this problem is to perform de novo transcriptome assemblies to obtain predicted transcripts for these organisms and then employ similarity comparison algorithms to identify them. I observe it is possible to obtain a more complete set of similar transcripts from transcriptome assembly by making use of evolutionary information. I apply new algorithms to study non-model organisms which play an important role in applied biology. Moreover, improvement of sequencing technologies and application of current algorithms also help to study interkingdom signals between blow flies and bacteria community. With current computational tools, I annotate genomes of Proteus mirabilis and Providencia stuartii, which play an important role in bacteria-insect interaction. The study shows significant features of these strains isolated, which provides useful information to develop and test hypothesis in related interactions in insects and bacteria

A forward genetic screen for genes involved in the arbuscular mycorrhizal symbiosis in the model legume Medicago truncatula

The arbuscular mycorrhizal symbiosis (AMS) provides a significant part of nutrient uptake for the majority of plant species. Engineering increased symbiotic potential in crops offers great benefits for agriculture, reducing the demand for fertilisers and increasing resiliency to disease and abiotic stress. We attempted to increase understanding of the AMS, by identifying genes involved in the symbiosis, focusing on the poorly understood parts of the symbiotic process outside of the legume common symbiosis pathway. Prior work had carried out an initial screen of the model legume Medicago truncatula, mutagenised with the retrotransposon tnt1, to obtain lines showing a phenotype of impaired arbuscular mycorrhizal colonisation while retaining normal rhizobial colonisation. This project took candidate lines from that screen, and used morphological and genetic phenotyping to confirm four Medicago lines with defects in different parts of the AMS. We developed a computational pipeline to quickly locate the 30-60 tnt1 insertions in each mutant line with Illumina whole genome sequencing (WGS). We backcrossed the mutants to produce populations segregating for the different insertions. This population was genotyped for the insertions located by WGS. Co-segregation analysis was used to show correlation between tnt1 insertions and the impaired arbuscular mycorrhizal colonisation phenotype in these lines. Finally, we attempted to replicate reports that plant mycorrhizal colonisation phenotypes are dependent on fungal genotype, and question the assumed universality of signalling across this highly generalist symbiosis