Background
Inside the nucleus of a cell, DNA is arranged in a complex three-dimensional (3D) structure, which is crucial for gene function. In cancer cells, however, this 3D arrangement can become disorganized, leading to improper gene expression. This disorganization and the resulting changes in gene expression are believed to play a significant role in cancer progression and metastasis. However, this process is not well understood due to a lack of data.
Our group, the Paulsen group, has access to pioneering 3D genome data from breast cancer cells at various stages - from normal to pre-cancerous and finally metastatic cells. Our ongoing research has revealed that the orderly 3D arrangement of the genome, which we call "radial organization," gets disrupted during cancer progression (see figure below). We aim to delve deeper into this process in hopes of paving the way for new breast cancer treatments in the future.
Fig. 1: A: Left: A normal (non-cancer) 3D genome organization. Each chromosome is shown in a separate color. Right: The same model, showing a normal radial organization with gene-rich DNA in the center (A3-A0; red), and gene poor (B0-B3; blue) in the periphery of the structure B: Plot showing how the radial organization is lost during breast cancer progression. A normal radial organization is shown with the blue line, which gradually increases. The yellow (pre-cancer) and green (cancer) data, shows that this trend is lost, indicating that the normal order of the genome is lost in cancer. Read more: https://www.biorxiv.org/content/10.1101/2023.11.26.568711v2
Aim of this Master project
In this exciting bioinformatics and computational project, the student (together with the supervisor) will analyze existing data to uncover how the disorganization of the 3D genome affects gene expression. Supported by the supervisor and other researchers in the Paulsen group, the student will develop and use machine learning tools and statistical analyses methods to dig deeper into the process that turns normal cells into cancer cells.
Prerequisites
- The student will be trained in understanding the data, and in the tools we use to analyze the data.
- The student needs to know some programming (for example Python and/or C++) and must have an interest in computational analyses
Contact and questions:
Jonas Paulsen (group leader and supervisor): jonas.paulsen@ibv.uio.no, tlf: 41147241