We are continually inviting applications for postdoc, PhD and internship positions. You can apply for one of two tracks:
- Method development in statistical computing and bioinformatics,
- Biological discovery through integrative data analysis (“dry biology”)
For track 1, you will have strong quantitative and analytical skills, such as acquired through a degree in mathematics, statistics, physics, computer science or a related field. You have curiosity and motivation to work in interdisciplinary projects, which include generation of new data and their analysis, and are eager to get to grips with relevant areas of biology and the technologies used in biology research. You will have experience in scientific computing and be familiar with one or several computer languages. Familiarity with R is definitively a plus.
For track 2, you will have a training in life sciences and strong coding skills that enable you to undertake complex data transformations, integrative operations, applications of mathematical models and visualizations. You are driven by making fundamental discoveries by mining cutting-edge, large data sets.
To apply, please contact Wolfgang with your CV, a brief statement of research interests, and examples of your work: besides your publications, this can include theses, research reports, talk slides, software projects (e.g. R packages, github projects) or data analysis reports (e.g. markdown reports or Jupyter notebooks).
Here are some keywords and a non-exhaustive list of collaboration partners with whom we work frequently on new, exciting data types:
- Latent spaces and manifolds estimation from multi-modal single cell data
- Genotype-drug interactions, precision oncology, multivariate biomarker discovery
- Imaging-based phenotyping
- Thorsten Zenz – pharmacogenomics of drug response in blood cancer
- Sascha Dietrich – systems medicine of cancer drugs
- Lars Steinmetz – systems genetics & ‘omics technology development
- Michael Boutros – high-throughput genetics, genetic interactions & synthetic lethality in cancer
- Henrik Kaessmann – evolution of cell types
Abstract: Expression of tissue-restricted self antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential for the induction of self-tolerance and prevents autoimmunity, with each TRA being expressed in only a few mTECs. How this process is regulated in single mTECs and is coordinated at the population level, such that the varied single-cell patterns add up to faithfully represent TRAs, is poorly understood. Here we used single-cell RNA sequencing and obtained evidence of numerous recurring TRA–co- expression patterns, each present in only a subset of mTECs. Co-expressed genes clustered in the genome and showed enhanced chromatin accessibility. Our findings characterize TRA expression in mTECs as a coordinated process that might involve local remodeling of chromatin and thus ensures a comprehensive representation of the immunological self.
Read more: Brennecke et al.ni2015
Registration is now open for the 2015 Stanford | EMBL Conference on Personalized Health, which will take place in Heidelberg 16-19 Nov 2015.
Abstract: Gene-gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene-gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.
The Perspective paper Orchestrating high-throughput genomic analysis with Bioconductor is addressed at users and prospective developers. It gives an overview over the collaborative software development and delivery model of the Bioconductor project. At Readcube: http://rdcu.be/b2VE.
Abstract: Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.