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This project is based on the premises that:
- cytogenetic aberration data (e.g. chromosomal translocations) represent an important set of molecular markers, esoecially in hematologic malignancies but also in developmental diseases
- a large body of such data exists, but is mostly difficult to access and or search, systematically
- cytogenetic annotations can be converted into modern variant annotation forrmats, such as being developed by the Genomic Knowledge Standards workstream of the Global Alliance for Genomics and Health (GA4GH)
- an implementation to access genome variant data is the Beacon API
With the advent of new generation sequencing (NGS) bioinformatic methods must keep pace to provide robust scalable solutions to analyse large sets of molecular sequences. The evolutionary history of molecules is described by a tree structure called phylogeny, which is inferred from genomic sequences. Phylogenies are used for testing biological hypotheses with applications ranging from medicine to ecology. Phylogeny inference usually relies on an inferred alignment of homologous sequences, which – in turn – relies on a guide-tree reflecting their ancestral relationships. The goal is to address this apparent circularity so to improve the reliability of phylogenetic analyses. Ideally alignment and tree should be inferred jointly.Contact Details
The URPP Evolution in Action: From Genomes to Ecosystems is seeking Masters students for projects in bioinformatics. The URPP Evolution in Action involves multiple research groups in biology, and it plays an important integrative role for the diverse biological disciplines at UZH.
We have several projects available at the interface between computation and biology, which aim to gain insight into biological mechanisms by mining data from labs and literature. These projects involve the analysis of multi-omics data, including DNA methylation, transcriptome, chromatin modifications, and chromatin accessibility in plants. Some projects also involve software development.
We are looking for candidates with programming experience in
R and knowledge of
UNIX-like platforms. Familiarity with git and experience in analyzing high-throughput sequencing data is advantageous, but not necessary. Applicants with
research experience are particularly encouraged to apply. We welcome applicants from a variety of backgrounds, including biology, bioinformatics, mathematics, computer science, physics, or any relevant interdisciplinary field.
The student will be directly supervised by Dr. Deepak Tanwar and will join a team of bioinformaticians and biologists who work together on the generation and analysis of experimental data. Through this position, students will have the opportunity to learn or improve their skills in version-controlled genomic/epigenomic data analysis, writing efficient code, making publication-ready figures, cluster computing, scientific writing, and presentation skills.
To apply, please send a CV and cover letter to Deepak Tanwar at firstname.lastname@example.orgContact Details