| Summary: | My PhD is affiliated with the dCod 1.0 project (https://www.uib.no/en/dcod): decoding the systems toxicology of Atlantic cod (Gadus morhua), which aims to better understand how cods adapt and react to the stressors in the environment. One of the research topics is to discover the biomarkers which discriminate the fish under normal biological status and the ones that are exposed to toxicants. A biomarker, or biological marker, is an indicator of a biological state in response to an intervention, which can be for example toxic exposure (in toxicology), disease (for example cancer), or drug response (in precision medicine). Biomarker discovery is a very important research topic in toxicology, cancer research, and so on. A good set of biomarkers can give insight into the disease / toxicant response mechanisms and be useful to find if the person has the disease / the fish has been exposed to the toxicant. On the molecular level, a biomarker could be "genotype" - for instance a single nucleotide variant linked with a particular disease or susceptibility; another biomarker could be the level of expression of a gene or a set of genes. In this thesis we focus on the latter one, aiming to find out the informative genes that can help to distinguish samples from different groups from the gene expression profiling. Several transcriptomics technologies can be used to generate the necessary data, and among them, DNA microarray and RNA sequencing (RNA-Seq) have become the most useful methods for whole transcriptome gene expression profiling. Especially RNA-Seq has become an attractive alternative to microarrays since it was introduced. Prior to analysis of gene expression, the RNA-Seq data needs to go through a series of processing steps, so a workflow which can automate the process is highly required. Even though many workflows have been proposed to facilitate this process, their application is usually limited to such as model organisms, high-performance computers, computer fluent users, and so on. To fill these gaps, we ...
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