Open Positions

People

Group picture as of November, 2019

Ana Luísa Costa

PhD student

Daria Doncevic

PhD student

Carl Herrmann

Group leader

Carlos Ramirez

Postdoc

Andres Quintero

PhD student

Ashwini Sharma

Postdoc

Youcheng Zhang

PhD student


Alumni

  • Calvin Chan
  • Qi Wang
  • Nils Kurzawa
  • Sebastian Steinhauser
  • Paul Saary
  • Ron Schwessinger
  • Christian Heyer
  • Asma Hamid
  • Clothilde Chenal
  • Jérémie Perrin

We are happy to welcome motivated students for lab rotation, bachelor and master thesis.

Projects

Applications of non-negative matrix factorization to single-cell and bulk genomic datasets

We use epigenomics datasets to characterize neuroblastoma subtypes

Cohort stratification and chromatin feature extraction using neural networks.

OpenLab Epigenomics

The Epigenomics OpenLab is a joined effort of the DKFZ and Medical Faculty to support groups in the processing of their epigenomics datasets. We offer assistance and expertise, as well as access to our processing pipelines, and are happy to host external members to guide them through the analysis.

Please contact us if you are looking for assistance.

Teaching

Members of the lab are involved in teaching in the Molecular Biotechnology Bachelor and Master Program at the university Heidelberg.


Courses

Winter semester 2020 / 2021

Summer semester 2020

Winter semester 2019 / 2020


Bachelor thesis projects 2021

Here are some possible topics/projects for students wanting do to their bachelor thesis in our group during the summer semester 2021

Topic 1 : Building gene-regulatory networks

Gene regulatory networks (GRN) are networks between transcription factors (TF) and target genes, indicating which genes are regulated by which transcription factor. Currently, several methods enable to build (or “infer”) gene regulatory networks from gene expression data: if a gene is regulated by a TF, then the expression profiles of the two should be related. However, such methods require a large number of samples in order to work. Often however, we have a small collection of samples (e.g. patients) for which we have expression data, but insufficient to reliably infer GRNs. One idea is to use transfer learning, which means using a large, related dataset to predict a GRN, and fine-tune the GRN using the specific small dataset of interest. I doing so, we can build specific gene regulatory networks for small sets of patients.

The goal of the bachelor thesis will be to test several approaches towards transfer-learning on GRNs, for example Random Forest approaches, and to use different test datasets (e.g. neuroblastoma, glioblastoma,…) to validate the approach, using pre-existing knowledge on the gene regulatory mechanisms

Main aspects:

  • data analysis of expression data
  • statistical concepts of machine-learning

References

Topic 2 : comparison of Type 1 vs Type 2 diabetes with respect to COVID19 comorbidities

Diabetes has been recognized as a major comorbidity of COVID19, and it is established that Sars-Cov-2 can infect pancreatic cells. However, little is known about the difference between Type 1 and Type 2 diabetes. Given the very different ethiology between these 2 diseases, we expect different molecular mechanisms which could play a role towards comorbidity with COVID19. Here, we want to use recently obtained single-cell datasets from Type 2 patients, as well as further datasets of Type 1 patients to compare the perturbed pathways in pancreatic cells of both diseases and identify shared or specific mechanisms which could explain the higher susceptibility. The goal of the bachelor thesis will be to use COVID19 gene signatures that we have previously identified, and compare the expression of these signatures across multiple diabetes datasets.

Main aspects

  • data processing of single-cell data; deconvolution of bulk RNA-seq datasets
  • representation of the results
  • biological interpretation of identified activated pathways

References

Topic 3 : differential “in-silico phenotyping” of tumor and normal tissues

The existence of large RNA-seq datasets of tumor tissue and matching normal tissue allows to conduct comparative studies. In particular, recent approaches allow to determine the activity of pathways and transcription factors from the transcriptomic data, which can be used to understand how pathways and master regulators are jointly activated or seem to have mutually exclusive patterns. In recent projects, we have for example described how mesenchymal phenotypes appear to be tightly related to pathway activation, for example the RAS pathway. The goal of the project is to conduct a large scale analysis of the activity patterns of pathways and master regulators, and to understand how these patterns are perturbed in tumor tissues compared with normal counterpart. We will in particular focus on processes related to ferroptosis across various tumor types to describe how this process is related to other pathways.

Main aspects

  • large scale processing of transcriptomic data from TCGA
  • implementation of statistical methods to study differential correlation
  • visual representation of the data and interactive data mining.

Contact