Construction and refinement of Reference Transcript Dataset annotations for fast and accurate transcript quantification in barley and potato

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In the model plant, Arabidopsis, we have taken a new and successful approach to analyse gene expression from high-throughput sequencing of RNA (RNA-seq). The high incidence of alternative splicing (AS) in plants (found in >600% of intron-containing genes) requires methods to distinguish and quantify AS variants or isoforms. We have taken an approach used by scientists analyzing gene expression and AS in human cancers. The approach uses programmes like SALMON, which does not require mapping of reads to genomes but instead use a Reference Transcript Dataset to quantify transcripts. We have generated an RTD for Arabidopsis (Zhang et al., 2015, 2016), which involved the development of in-house pipelines for removing redundancy in the RTD. Using this system with RNA-seq data from a time-course of plants transferred from normal to cold temperatures has already demonstrated transcript-specific expression including AS responses to cold, and identified new genes involved in the cold response. The PhD project will develop computational pipelines for construction of RTDs in crop plants (potato and barley) which will be tested with RNA-seq datasets. This will require development of algorithms for the pipelines and for various downstream analyses. The ability to generate transcript-specific and allele-specific expression data will greatly enhance our ability to analyse gene expression and identify key genes in plant/crop processes such as abiotic and biotic stress responses.