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Introducing an RNA seq pipeline in GenEx software
University of Skövde, School of Bioscience.
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

RNA sequencing has become a widely utilized tool in life sciences as a quantitative means of detecting RNA abundance in tissues and cells. The increased usage of RNA-seq technology has resulted in the continual development of new tools for every level of analysis, from alignment through downstream pathway analysis. Although RNA-seq has a wide range of applications, no single analysis workflow can be applied in all cases. This study presents an R-based pipeline for RNA-seq that was generated for GenEx software. To generate the pipeline the dataset was used from the previous study and then the results were compared with the same published study. The RNA-seq pipeline includes the mapping and alignment of reads by using Rsubread, identification of differentially expressed genes by using Deseq2, and enrichment analysis PathfindR tool was used. This study reviews all major steps including mapping, read alignment, differential gene expression, and enrichment analysis. The results showed Rsubread 90-92% reads were mapped in two groups and 46-74% reads were mapped in the other two groups. The alignment was 50-60% reads and 30-40% reads. On comparison of Deseq2 results with the previous study by using the Venn diagram, it shows that most of the DEGs were like the previous study. Also, enrichment analysis shows quite similar results with the previous study. The KEGG database predominantly identified upregulated inflammation-related pathways, such as the “TNF signaling pathway,” “IL17 signaling pathway,” and “NF-kappa B signaling pathway in groups which is quite similar to the previous study. Overall, the result was similar to the previous study so implementation of this pipeline in GenEx software will be beneficial for the wet-lab biologist and it will reduce human involvement that will reduce the chances of error. The study's drawback is that the parameter was not briefly specified, and different tools were used for pathway analysis and differential expression analysis in the previous study, making it impossible to compare the results of differential expression analysis and pathway analysis.

Place, publisher, year, edition, pages
2021. , p. 37
National Category
Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:his:diva-20694OAI: oai:DiVA.org:his-20694DiVA, id: diva2:1610825
External cooperation
Multid Analysis AB
Subject / course
Systems Biology
Educational program
Infection Biology - Master’s Programme 120 ECTS
Supervisors
Examiners
Available from: 2021-11-11 Created: 2021-11-11 Last updated: 2025-02-07Bibliographically approved

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