(Internal) Environmental effects on transposable element and small non-coding RNA expression as a common theme in germline and neural development
Advisor: Jonathan LaMarre, Biomedical Sciences
Proposed co-advisor: Stefan Kremer, Nicole Ricker, Karl Cottenie
Approximately half of most mammalian genomes are composed of transposable elements (TEs, jumping genes) that are generally silenced by epigenetic features such as DNA and histone methylation. During the formation of gametes (gametogenesis) and neurons (neurogenesis), epigenetic marks are removed and many TEs become transcribed as RNA. We postulate that the environment influences the pattern and level of expression of TE families and the small RNA pathways that "defend" against their negative effects on the genome. This project will develop, modify and apply bioinformatic pipelines to RNAseq datasets that we generate to characterize changes in TE expression in response to environmental changes such as heat and hypoxia.
In brief, RNAseq data will be evaluated for TE expression using specialized bioinformatic tools that have been designed to overcome several challenges associated with analyzing TEs that are present in high copy numbers in the genome. These approaches incorporate the use of Telescope for TE expression and the LIONs analysis suite to identify sites of TE insertions that have been exapted as regulatory elements (TEeRS) in the genome. This will allow the differential pattern of TE expression (DTE pattern) in control and heat or hypoxia-treated cells to be determined and quantified. Modifications to the pipelines are anticipated to account for species and tissue dependent variations in TE expression.
The M.Binf. student on this project will develop and extend experience in pipeline development using BASH, data visualization using the R programming language and the use of the RepeatMasker toolkit.
This project is suitable for one or two semesters. The student is required to occasionally be on-site.
Knowledge/Skills
Basic familiarity with BASH and R programming. Some experience with analysis of RNAseq data would be helpful.