Väitös (fysiologia): MSc Arfa Mehmood
Aika
25.8.2023 klo 12.00 - 16.00
MSc Arfa Mehmood esittää väitöskirjansa ”Tools and strategies for RNA-Sequencing analysis” julkisesti tarkastettavaksi Turun yliopistossa perjantaina 25.8.2023 klo 12.00 (Turun yliopisto, Medisiina C, Osmo Järvi -sali, Kiinamyllynkatu 10, Turku).
Vastaväittäjänä toimii professori Inge Jonassen (University of Bergen, Norja) ja kustoksena tutkimusjohtaja Laura Elo (Turun biotiedekeskus). Tilaisuus on englanninkielinen. Väitöksen alana on fysiologia.
Väitöskirja yliopiston julkaisuarkistossa: https://www.utupub.fi/handle/10024/175552
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Tiivistelmä väitöstutkimuksesta:
Transcriptomic studies are carried out to identify genes and pathways involved in the development and progression of diseases. In many transcriptomic studies, the main aim is to identify systematic differences in the expression levels of genes between biological sample groups. With modern high-throughput biotechnology approaches such as RNA-sequencing (RNA-seq), the expression levels of all genes of an organism can be measured simultaneously. The resulting RNA-seq data can then be analyzed using computational approaches to determine the differentially expressed genes between biological conditions or disease states. Further, RNA-seq data can also be used to study the splicing patterns of genes and to detect genes that are differentially spliced between conditions. Over the years, many strategies and tools have been developed for detecting both differentially expressed and differentially spliced genes.
In this thesis, it was illustrated how the accuracy and power of detecting the differentially expressed genes can be improved by performing the statistical testing on the exon-level expression signals rather than the conventionally used gene-level expression signals. In addition, we performed a comprehensive comparison study of the popular and recent differential splicing tools and found that the robustness and reproducibility of the generated results greatly varied across the tools. The studies included in this thesis provide a valuable resource for researchers working on transcriptomic data, helping them to optimize their analysis workflows for differential gene expression and splicing analysis.
Vastaväittäjänä toimii professori Inge Jonassen (University of Bergen, Norja) ja kustoksena tutkimusjohtaja Laura Elo (Turun biotiedekeskus). Tilaisuus on englanninkielinen. Väitöksen alana on fysiologia.
Väitöskirja yliopiston julkaisuarkistossa: https://www.utupub.fi/handle/10024/175552
***
Tiivistelmä väitöstutkimuksesta:
Transcriptomic studies are carried out to identify genes and pathways involved in the development and progression of diseases. In many transcriptomic studies, the main aim is to identify systematic differences in the expression levels of genes between biological sample groups. With modern high-throughput biotechnology approaches such as RNA-sequencing (RNA-seq), the expression levels of all genes of an organism can be measured simultaneously. The resulting RNA-seq data can then be analyzed using computational approaches to determine the differentially expressed genes between biological conditions or disease states. Further, RNA-seq data can also be used to study the splicing patterns of genes and to detect genes that are differentially spliced between conditions. Over the years, many strategies and tools have been developed for detecting both differentially expressed and differentially spliced genes.
In this thesis, it was illustrated how the accuracy and power of detecting the differentially expressed genes can be improved by performing the statistical testing on the exon-level expression signals rather than the conventionally used gene-level expression signals. In addition, we performed a comprehensive comparison study of the popular and recent differential splicing tools and found that the robustness and reproducibility of the generated results greatly varied across the tools. The studies included in this thesis provide a valuable resource for researchers working on transcriptomic data, helping them to optimize their analysis workflows for differential gene expression and splicing analysis.
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