Salvia hispanica L. (commonly known as chia) is gaining popularity worldwide and specially in US as a healthy oil and food supplement for human and animal consumption due to its favorable oil composition, and high protein, fiber, and antioxidant contents. Despite these benefits and its growing public demand, very limited gene sequence information is currently available in public databases. In this project, we generated 90 million high quality 150 bp paired-end sequences from the chia leaf and root tissues. The sequences were de novo assembled into 103,367 contigs with average length of 1,445 bp. The resulted assembly represented 92.2% transcriptome completeness. Around 69% of the assembled contigs were annotated against the uniprot database and represented a diverse array of functional and biological categories. A total of 14,267 contigs showed significant expression difference between the leaf and root tissues, with 6,151 and 8,116 contigs upregulated in the leaf and root, respectively. The sequence data generated in this project will provide valuable resources for future functional genomic research in chia. With the availability of transcriptome sequences, it would be possible to identify genes involved in the important metabolic pathways that give chia its unique nutritional and medicinal properties. Finally, the generated data will contribute to the genetic improvement efforts of chia to better serve the public demand.

>Magister Scientiae - MSc / Lung cancer remains the leading cause of cancer deaths worldwide, with the majority of cases attributed to non-small cell lung carcinomas. At the time of diagnosis, a large percentage of patients present with advanced stage of disease, ultimately resulting in a poor prognosis. The identification circulatory markers, overexpressed by the tumour tissue, could facilitate the discovery of an early, specific, non-invasive diagnostic tool as well as improving prognosis and treatment protocols. The aim was to analyse gene expression data from both microarray and RNA sequencing platforms, using bioinformatics and statistical analysis tools. Enrichment analysis sought to identify genes, which were differentially expressed (p < 0.05, FC > 2) and had the potential to be secreted into the extracellular circulation, by using Gene Ontology terms of the Cellular Component. Results identified 1 657 statically significant genes between normal and early lung cancer tissue, with only 1 gene differentially expressed (DE) between the early and late stage disease. Following statistical analysis, 171 DE genes selected as potential early stage biomarkers. The overall sensitivity of RNAseq, in comparison to arrays enabled the identification of 57 potential serum markers. These genes of interest were all downregulated in the tumour tissue, and while they did not facilitate the discovery of an ideal diagnostic marker based on the set criteria in this study, their roles in disease initiation and progression require further analysis.