Here is a list of possible topics for Bachelor thesis in 2024 in our group. These are examples, and topics might slightly evolve depending on your precise interests, and the status of these projects at the beginning of the thesis!
Mental disorders such as schizophrenia, depression and bipolar disorders have partly overlapping symptoms which renders the diagnosis challenging and points at partly similar molecular origins. In addition, many of these disorders are associated to a higher susceptibility for somatic disorders, such as cardiovascular disorders or diabetes. Again, underlying perturbed pathways are likely the underlying cause. We have started building a comprehensive network of transcriptional signatures using large cohorts of disease patients to understand the shared dimensions between these disorders. We would like to refine this work and include (1) additional disease cohorts, for example including autoimmune disorders, and (2) include epigenomic data such as DNA methylation data to build a complete network connecting disease signatures and highlighting shared perturbed molecular pathways. These signatures will be identified using statistical factorization approaches potentially combined with deep-learning approaches.
Zhang, Y., Bharadhwaj, V. S., Kodamullil, A. T. & Herrmann, C. A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders. bioRxiv 2023.10.02.560428 (2023) doi:10.1101/2023.10.02.560428.
Romero, C. et al. Exploring the genetic overlap between twelve psychiatric disorders. Nat Genet 54, 1795–1802 (2022).
Cancer is known to highjack normal developmental processes to ensure growth and development of the tumor. Hence, tumor signatures obtained from transcriptomics data often include normal developmental genes, that are deregulated during tumor growth. In recent studies, it has been shown that neuronal genes are activated or repressed in certain tumor types. While this is expected in brain tumor, neuronal signatures have also been identified in less expected tumors, like colon cancer or breast cancer. In this project, we would like to mine systematically tumor transcriptomic datasets from TCGA and verify the presence of specific neuronal signatures.
Single-cell genomics has uncovered a new level of insights regarding tissue heterogeneity. While scRNA-seq is now widely used, other single-cell omics data is increasingly used, to shed light on new aspects of gene regulation, such as scATAC-seq. Finally, multiomics assay allow to capture multiple signals from the very same cell (for example scRNA-seq and scATAC-seq). Using these multiomics datasets, we have built a deep-learning model, PELICAN, that allows to translate one modality into another. Hence, given scRNA-seq, the model can predict the possible scATAC-seq profiles. We would like to systematically compare this method against previously published methods using various multiomics datasets and perform a benchmark of various approches. Using in-silico knock-outs, we want to verify the predictions of our model when genes are knocked-out, in terms of remodlling of the epigenomic landscape.
Zhang, R., Meng-Papaxanthos, L., Vert, J.-P. & Noble, W. S. Semi-supervised single-cell cross-modality translation using Polarbear. 2021.11.18.467517 https://www.biorxiv.org/content/10.1101/2021.11.18.467517v1 (2021) doi:10.1101/2021.11.18.467517.
Wu, K. E., Yost, K. E., Chang, H. Y. & Zou, J. BABEL enables cross-modality translation between multiomic profiles at single-cell resolution. PNAS 118, (2021).