The 3D genome organisation regulates gene expression by bringing distal regulatory elements, such as enhancers, to promoters in close spatial proximity. While many scientists have been working on cell-type specificity of gene regulation through transcriptomic sequencing, comprehensive investigation of cell-type specificity of 3D genome conformation patterns is still lacking. Recently, single-cell methods allow us to examine cell-type heterogeneity and profiling chromosome architecture at the single-cell level has been achieved using chromosome confirmation capture (Hi-C). However, unbiased and robust computational methods are urgently needed to study cell type-specific chromosome structural patterns and accurately identify local enhancer-promoter interactions at single-cell level.
Artificial Intelligence | Machine learning | Genetic diseases | Rare diseases | Bioinformatics
Resources: We have access to 3D genome organization data from more than 50 samples. We have also local high performance computing systems in BioMedical Machine Learning Lab.
The skills required for project(s): Scripting experience in Python, R, or MATLAB.
What skills will you gain during your degree: Machine learning and data analytics techniques, Bioinformatics, Systems Biology.