Welcome to the Biomedical Informatics Lab of Prof. Dr. Gunnar Rätsch
The research in our group lies at the interface between methods research in Machine Learning, Genomics and Medical Informatics and relevant applications in biology and medicine.
We develop new analysis techniques that are capable of dealing with large amounts of medical and genomic data. These techniques aim to provide accurate predictions on the phenomenon at hand and to comprehensibly provide reasons for their prognoses, and thereby assist in gaining new biomedical insights.
Current research includes a) Machine Learning related to time-series analysis and iterative optimization algorithms, b) methods for transcriptome analyses to study transcriptome alterations in cancer, c) developing clinical decision support systems, in particular, for time series data from intensive care units, d) new graph genome algorithms to store and analyze very large sets of genomic sequences, and e) developing methods and resources for international sharing of genomic and clinical data, for instance, about variants in BRCA1/2.
Abstract Kernel methods on discrete domains have shown great promise for many challenging tasks, e.g., on biological sequence data as well as on molecular structures. Scalable kernel methods like support vector machines offer good predictive performances but they often do not provide uncertainty estimates. In contrast, probabilistic kernel methods like Gaussian Processes offer uncertainty estimates in addition to good predictive performance but fall short in terms of scalability. We present the first sparse Gaussian Process approximation framework on discrete input domains. Our framework achieves good predictive performance as well as uncertainty estimates using different discrete optimization techniques. We present competitive results comparing our framework to support vector machine and full Gaussian Process baselines on synthetic data as well as on challenging real-world DNA sequence data.
Authors Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
Submitted arXiv Preprints
Abstract Translation initiation is orchestrated by the cap binding and 43S pre-initiation complexes (PIC). Eukaryotic initiation factor 1A (EIF1A) is essential for recruitment of the ternary complex and for assembling the 43S PIC. Recurrent EIF1AX mutations in papillary thyroid cancers are mutually exclusive with other drivers, including RAS. EIF1AX is enriched in advanced thyroid cancers, where it displays a striking co-occurrence with RAS, which cooperates to induce tumorigenesis in mice and isogenic cell lines. The C-terminal EIF1AX-A113splice mutation is the most prevalent in advanced thyroid cancer. EIF1AX-A113spl variants stabilize the PIC and induce ATF4, a sensor of cellular stress, which is co-opted to suppress EIF2α phosphorylation, enabling a general increase in protein synthesis. RAS stabilizes c-MYC, an effect augmented by EIF1AX-A113spl. ATF4 and c-MYC induce expression of aminoacid transporters and enhance sensitivity of mTOR to aminoacid supply. These mutually reinforcing events generate therapeutic vulnerabilities to MEK, BRD4 and mTOR kinase inhibitors.
Authors Gnana P. Krishnamoorthy, Natalie R Davidson, Steven D Leach, Zhen Zhao, Scott W. Lowe, Gina Lee, Iñigo Landa, James Nagarajah, Mahesh Saqcena, Kamini Singh, Hans-Guido Wendel, Snjezana Dogan, Prasanna P. Tamarapu, John Blenis, Ronald Ghossein, Jeffrey A. Knauf, Gunnar Rätsch and James A. Fagin
Submitted Cancer Discovery
Abstract Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions (“neojunctions”) in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders (“putative neoantigens”).
Authors Andre Kahles, Kjong-Van Lehmann, Nora C. Toussaint, Matthias Hüser, Stefan Stark, Timo Sachsenberg, Oliver Stegle, Oliver Kohlbacher, Chris Sander, Gunnar Rätsch, The Cancer Genome Atlas Research Network
Submitted Cancer Cell
Abstract The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.
Authors Stephanie O. M. Dyke, Mikael Linden, […], Gunnar Rätsch, […], Paul Flicek
Submitted European Journal of Human Genetics
Abstract Motivation: Technological advancements in high-throughput DNA sequencing have led to an exponential growth of sequencing data being produced and stored as a byproduct of biomedical research. Despite its public availability, a majority of this data remains hard to query for the research community due to a lack of efficient data representation and indexing solutions. One of the available techniques to represent read data is a condensed form as an assembly graph. Such a representation contains all sequence information but does not store contextual information and metadata. Results: We present two new approaches for a compressed representation of a graph coloring: a lossless compression scheme based on a novel application of wavelet tries as well as a highly accurate lossy compression based on a set of Bloom filters. Both strategies retain a coloring even when adding to the underlying graph topology. We present construction and merge procedures for both methods and evaluate their performance on a wide range of different datasets. By dropping the requirement of a fully lossless compression and using the topological information of the underlying graph, we can reduce memory requirements by up to three orders of magnitude. Representing individual colors as independently stored modules, our approaches can be efficiently parallelized and provide strategies for dynamic use. These properties allow for an easy upscaling to the problem sizes common to the biomedical domain. Availability: We provide prototype implementations in C ++, summaries of our experiments as well as links to all datasets publicly at https://github.com/ratschlab/graph_annotation.
Authors Harun Mustafa, ingo Schilken, Mikhail Karasikov, Carsten Eickhoff, Gunnar Rätsch, Andre Kahles
Date 08 Jun 2018