Browsing by Author "Cervera Taboada, Alejandra"

High-throughput technologies have had a profound impact in transcriptomics. Prior to microarrays, measuring gene expression was not possible in a massively parallel way. As of late, deep RNA sequencing has been constantly gaining ground to microarrays in transcriptomics analysis. RNA-Seq promises several advantages over microarray technologies, but it also comes with its own set of challenges. Different approaches exist to tackle each of the required processing steps of the RNA-Seq data. The proposed solutions need to be carefully evaluated to find the best methods depending on the particularities of the datasets and the specific research questions that are being addressed. In this thesis I propose a computational framework that allows the efficient analysis of RNA-Seq datasets. The parallelization of tasks and organization of the data files was handled by the Anduril framework on which the workflow was implemented. Particular emphasis was bestowed on the quality control of the RNA-Seq files. Several measures were taken to prune the data of low quality bases and reads that hamper the alignment step. Furthermore, various existing processing algorithms for transcript assembly and abundance estimation were tested. The best methods have been coupled together into an automated pipeline that takes the raw reads and delivers expression matrices at isoform and gene level. Additionally, a module for obtaining sets of differentially expressed genes under different conditions or when measuring an experiment across a time course is included.