Comparison of bioinformatics tools and transcriptome sequencing methodologies for optimal annotation of fungal genomes

The functionality of a genome is determined by a set of actively transcribed genes and their post-transcriptional regulation. Advances in sequencing technologies allow for the study of genetics at unprecedented resolution: where gene expression was once described on a gene-by-gene basis, it can now be investigated on a more holistic scale. Indeed, next generation sequencing has revolutionised the field of genomics. and its extensive variety of applications permits in-depth analysis of hundreds of species. Couple that development with the evolution of computational tools and sequencing technologies, and the analysis of genomes and species has never been more precise or more powerful. This thesis describes work on the development and comparison of sequencing technologies, along with computation methods for the re-annotation of a fungal genome. The research described in this thesis focuses on Myceliopthora thermophila, with the goal of utilising transcriptome sequencing to characterise gene expression and improve genome annotation for this thermophilic fungus. Having an annotation provides a bridge between genetic sequence, physical biology, and metabolic function. The quality of the annotation gives context to the organism, the better the annotation, the more useful this fungal species is for wider applications. The annotation defined in this thesis advances understanding of genes and gene features and potentially improves the utility of M. thermophila for specific industrial applications. Additionally, this research compares gene expression between fungal strains and within various growth conditions. We have endeavoured to identify genes involved in certain pathways and characterise their regulation. Gene expression can be measured directly using RNA-sequencing (RNA-seq), and as such, can provide an overview of complete transcriptomes and their regulation. Quantification of expressed transcripts and detection of novel genes and isoforms is eminently possible. The expanding field of RNA-sequencing has led to the development of more specialised experimental and computational techniques. Results from our research indicate that the choice of alignment software has a significant impact on the accurate interpretation of RNA-seq data. RNA sequencing can generate enormous amounts of data for interpretation. The need for analysing these large and increasing quantities of sequencing data has in turn increased the demand for efficient and effective computational tools. We present new methods and a specialized analysis technique that can be applied to both novel and existing data for effective characterisation of genomes