الفهرس 
Title : Computational Analysis for RNA-Seq of Plant Organisms
ContantsOnly 14 pages are availabe for public view
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Abstract
Computational techniques are used widely to help in the RNA-seq analysis process for better understanding genes behavior regarding different biotic and abiotic stress conditions. Usage of computational methods has led to saving a lot of time, effort and money for biologists. -This thesis aims at improvement of general RNA-seq data analysis, via usage of computational methods which can be applied on any organism. The work presented in this thesis is divided into two parts: the first part towards enhancing the differential expression analysis using both edgeR and Fisher criterion (FC) methods to obtain more reliable expressed genes, and second part investigates the relationship between the expression level of genes and the features of their SNPs using Rough set theory. -Both parts are applied on the analysis of A. thaliana plant under heat-stress conditions. -Results were validated via DRASTIC and TAIR10 databases and, suggest that edgeR and FC methods can be combined to perform efficient differential expression analysis within RNA-Seq data, without strong assumptions. -Moreover, set of (32) generated rules via Rough set proved good results with strength, certainty and coverage evaluation terms. The result increases the amount of knowledge for SNPs discovery and analysis in functional genomics research