Browsing by Author "Fernandes, Isabel Cristina Moniz"
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- RNA-Seq for the detection of differential expressed genes under several experimental conditionsPublication . Fernandes, Isabel Cristina Moniz; Paulo, Octávio, 1963-Bioinformatics aims to analyze and store complex biological datasets, which, due to the multidisciplinarity of the field, can be essential for finding meaning in biological systems, contributing to the modern life sciences knowledge. Transcriptomics is currently one of the areas if bioinformatics in greater expansion, namely through RNA sequencing (RNA-seq), which is an efficient transcriptome profiling approach. Its main application is the analysis of differentially expressed genes (DEGs), to assign biological meaning to specific tissues, environmental conditions, and other aspects. Reproductive strategies, resistance and stress responses can be evaluated through this technique, leading to a better understanding of the species fitness and survival. This thesis intended to detect and functionally annotate DEGs through the application of RNA seq pipelines. Moreover, since there’s still no gold standard for its best practices, this work mostly aimed to find the best suited tools and methods for each data type, such as length, depth and replicates, according to the research goals. Furthermore, it established a better understanding of the different expression profiles of species from three different genera, namely Casuarina, Coffea and Limonium. In general, the RNA-seq workflow was performed as follows: quality analysis, assembly (for non-model species), alignment, quantification, differential expression, and functional annotation. Since this project was developed as four separated analyses, each step and respective tools were evaluated according to each dataset features. The results of these analyses break the path for further studies and integration with other omics, which can help unravel relevant mechanism and pathways of the studied species. During the work of this thesis, a large set of scripts were developed to speed up and automatize the analysis, using Python and R languages, which have been made publicly available and can be applied by other users that work on similar studies.