Inferring genomic information from bisulfite sequencing data
Background
DNA methylation variants can arise spontaneously, they can be under genetic control or they can be induced by the environment. In plants, some DNA methylation variants are stable across many generations whereas other variants are very transient. A good understanding of the transgenerational dynamics of DNA methylation variants is essential to understand their impact on heritable traits and their effect on adaptation. Current insight in the transgenerational dynamics of DNA methylation is limited to very few model plant species, but it is predicted that these dynamics are not constant among different plants. For instance, plant reproduction mode can have a large effect because asexual reproduction bypasses some of the epigenetic resetting mechanisms that normally occur during sexual reproduction. Adaptive differences in transgenerational stability may also differ between species with different life spans or from habitats of different environmental predictability.
Your PhD Project
In this project, we aim to investigate differences in DNA methylation dynamics between species with different life history traits. In close collaboration with other bioinformaticians and biologists, a best-practice pipeline for the analysis of plant bisulfite data will be developed, benchmarked, and provided to be applied to the Next-Generation Sequencing (NGS) data generated in the frame of different EpiDiverse projects. In addition, novel algorithms will be devised and implemented to better detect DNA methylation variants and search for important methylation haplotypes to better understand the epigenetic regulation. The special constellation of working at a bioinformatics company in very strong cooperation with the chair of bioinformatics at the university offers the unique possibility of gaining benefits from both: The clearly structured style of the work in a company will help implementing novel ideas from academia in the light of recent publications, resulting in a strong focus and high productivity.
Your Qualifications
We seek a bright, highly motivated, and enthusiastic bioinformatician, or computer scientist with significant interest in solving biologically motivated questions. Excellent programming skills with knowledge of at least one of the following programming languages are mandatory: Python, R, or C/C++/C#. A strong background in Next-Generation Sequencing (NGS) and/or (epi)genetics data analyses are an advantage. A high standard of spoken and written English is required, as are good quantitative and analytical capabilities, excellent interpersonal and communication skills, and the ability to work independently as well as part of a team.
Interested? Visit www.ecseq.com/career
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