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LINUX Cluster Project

Experimentelle Bioinformatik


  • Name: Lehrstuhl für Experimentelle Bioinformatik
  • Address: Maximus-von-Imhof-Forum 3, 85354 Freising
  • Project Proposal Date: 2018-11-13 10:44:17


The competing endogenous RNA (ceRNA) hypothesis motivates the existence of so-called sponges, i.e., genes that exert strong regulatory control via miRNA binding in a ceRNA interaction network. This poses a powerful mechanism for cancer to dysregulate parts of the cellular transcriptional program through one or few key sponge genes. In particular non-coding RNAs may act as sponges in cancer to facilitate changes in transcriptional programs without the risk of lethal side effects caused by expressing a protein at abnormally high or low levels. In spite of the importance of this phenomenon, we currently lack an efficient method for inferring sponge interactions on a genome-wide scale. Moreover, confounding factors such as large differences in sample numbers prevent comparisons across different cancer cohorts. We have thus developed sparse partial correlation on gene expression (SPONGE), a method that is orders of magnitude faster than previous approaches and allows for the construction of genome-wide ceRNA interaction networks. SPONGE is the first method to compute empirical p-values efficiently based on a series of null models and can thus control for confounding factors that were underestimated in previous studies. Additionally: Immunotherapy is currently the most rapidly advancing area of clinical oncology and provides the unprecedented opportunity to effectively treat, and even cure, several previously untreatable malignancies. A growing awareness exists of the fact that the success of therapy depends on the individual immune contexture, which is determined by the density, composition, functional state and organization of the leukocyte infiltrate of the tumour (Wolf Herman Fridman et al. 2012; Wolf H. Fridman et al. 2017). As a result, knowledge about the immune contexture can yield information that is relevant to prognosis, prediction of treatment response and various other pharmacodynamic parameters. In this project, we aim at identifying and characterizing functional states of immune cells in cancer. To this end, we will integrate several recently-published, large-scale single cell datasets of immune cells across different cancer types aiming at a total of more than 300,000 individual single cells. Differences in data preprocessing, including the use of incompatible gene identifiers hampers data integration on processed (i.e. counts) data. To maximise utility of the dataset we will reprocess all datasets from raw sequencing data using a unified pipeline specifically developed to deal with single cell data from various experimental platforms.