Di Salle, Pasquale (2014) Analisi di co-espressione in A.thaliana. [Tesi di dottorato]

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Testo Di SALLE PASQUALE DOTTORATO IN BIOINFORMATICA 26 ciclo.pdf Download (4MB) | Anteprima

Abstract

Abstract The plant cell is a very coordinated system, with a central nucleus, organelles and structures, interacting continuously to perform the tasks needed to the cell growth, survival and replication. Genomes in fact store the instructions to define the cells, according to their genetic program. Most of cell activities are in fact the result of a work performed by multiple partners, collaborating in a serial or parallel way. All these efforts require hence a strong coordination among the gene functionalities too. This work aims to investigate the gene expression, in particular, the genes which are co-expressed, their roles and the main bioinformatics approaches exploited to analyze them. In order to perform this study, we have exploited the genome of Arabidopsis thaliana, a plant species very well studied and with a genome sequence made available in 2000 [The Arabidopsis Genome Initiative, 2000]. Because of this and thanks to the huge amount of data derived for this plant, Arabidopsis thaliana is considered the reference organism in plant genomic and constantly exploited for comparative analyses with other species. A. thaliana was established as a model plant genome because of its easy management properties, such as a small diploid genome, the small size and the high seeds production through self-pollination. However this plant has shown a very complex genome structure, with a hard to count number of gene duplications and chromosomes shuffles. The gathering of information about the co-expressed genes has inevitably led us to challenge some important questions in bioinformatics, as the research of 4 a network identification method able to group genes with a biological meaning, and the evaluation of the statistical components exploited to define genes as co-expressed. This effort has hence required the comparison of the public available platforms concerning the co-expression issue. As result of these activities, we have also tried to produce a new network profiling approach, inspired by social networks and able to underline quickly the genes in relation inside the genome. The comparison of the platforms concerning the co-expression has instead revealed a not uniform behavior of the genes according to the co-expression identification methods, underlining the need of data mining analyses not only through a single and specific dataset, but trying to compare the highest number of resources available.

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