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Biostatistics for Omics Data

Prof. Dr. Simon Anders

Insights in the haystack of high-throughput assay data.

We extract insights from high-throughput assay data, analyzing sequencing data, creating dataset visualizations, and applying omics data for functional genomics and systems medicine. Thanks to modern assay techniques, biology is transforming into a “data-rich” science. With high-throughput sequencing, mass spectrometry, automated perturbation screens and other “big data” assay technologies, we can now get at the same time a “bird's eye” overview as well as plenty of detail on a set of biological samples. The vast amount of raw data produced this way is, however, of little use without powerful bioinformatics and biostatistics methods to process, analyze, and interpret them. The new bioinformatics group focuses on developing the computational tools that biologists need to find the needles of biological insights in the haystacks of high-throughput assay data. We are working on methods to analyze high-throughput sequencing data, to visually and interactively explore big interlinked datasets, e.g., from omics studies, and on the use of transcriptomics and proteomics data in functional genomics and systems medicine.

Research Strategy

Computational scientists provide crucial expertise to research in molecular biologist that is complementary to the skill set that a typical biologist or clinical researcher has learned in her or his studies: We know how how to process, organise and explore big data sets, and transform data such that it can be viewed from "just the right angle" to gain new insights into biological systems (exploratory data analysis, EDA) and we know how to ascertain that findings are true and reproducible rather than the result of random chance or wishful thinking (inferential statistics).

Exploring big data requires tight collaboration, and especially effective communication, between computational scientists and biologists. While only the latter have the in-depth knowledge and intuition of the system under study to know which specific questions should be asked, we know what questions can be asked from a big data set, and, importantly, we can invent new investigative strategies to ask entirely new kinds of questions that were not accessible before.

For my group, I aim to assemble an interdisciplinary team, drawing not only from bioinformatics and statistics, but also from computer science, physics, engineering and other quantitative subjects, in order to cover a broad field of expertise on methods that can be translated, adapted and expanded for use in molecular medicine.

We will strive to strike a balance between two modes of research: On the one hand, we will craft tailored analysis methods for specific advanced experiments carried out by our wet-lab and clinical colleagues in Heidelberg and elsewhere. On the other hand, we will package such approaches into generally usable software tools that are modular, documented well and easy to use, to ensure that even research group with little or no biostatistics expertise can download and use them, thus making cutting-edge biostatistical methodology available to the research community at large.

Topics and Projects

  • Exploratory analysis of multi-omics data in molecular medicine
  • Interactive visualization of high-dimensional data
  • Data analysis for high-throughput sequencing
  • Transcriptomics and proteomics for large-scale studies
A screenshot of our Sleepwalk tool for interactive exploration of single-cell data. The colours indicate the dissimilarity scores (distances) of each cell to the cell under the mouse cursor, thus allowing the user to explore the fidelity of a t-SNE or UMA

 

 

Anders B&W
Prof. Dr. Simon Anders
  • +49 (0)6221 54-51380

Selected Publications

Exploring dimension-reduced embeddings with Sleepwalk

Svetlana Ovchinnikova, Simon Anders

Genome Res. 2020 May;30(5):749-756

A colorimetric RT-LAMP assay and LAMP-sequencing for detecting SARS-CoV-2 RNA in clinical samples.

Viet Loan Dao Thi, Konrad Herbst, Kathleen Boerner, Matthias Meurer, Lukas Pm Kremer, Daniel Kirrmaier, Andrew Freistaedter, Dimitrios Papagiannidis, Carla Galmozzi, Megan L Stanifer, Steeve Boulant, Steffen Klein, Petr Chlanda, Dina Khalid, Isabel Barreto Miranda, Paul Schnitzler, Hans-Georg Kräusslich, Michael Knop, Simon Anders

Sci Transl Med. 2020 Aug 12;12(556):eabc7075.

Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels.

Tobias Roider, Julian Seufert, Alexey Uvarovskii, Felix Frauhammer, Marie Bordas, Nima Abedpour, Marta Stolarczyk, Jan-Philipp Mallm, Sophie A Herbst, Peter-Martin Bruce, Hyatt Balke-Want, Michael Hundemer, Karsten Rippe, Benjamin Goeppert, Martina Seiffert, Benedikt Brors, Gunhild Mechtersheimer, Thorsten Zenz, Martin Peifer, Björn Chapuy, Matthias Schlesner, Carsten Müller-Tidow, Stefan Fröhling, Wolfgang Huber, Simon Anders, Sascha Dietrich

Nat Cell Biol.2020 Jul;22(7):896-906.