keywords: bioinformatics, 3c, hic, data compression, 3d reconstruction, resolution enhancement.
The structure of genomes in three-dimensional (3D) space is crucial for DNA replication, genome stability, and tissue differentiation. It helps us to understand the complex system of epigenetic activities. While Chromosome Conformation Capture (3C) quantifies the interaction between two specific loci (genomic regions), Hi-C quantifies all interactions between all possible pairs of loci on all chromosomes simultaneously. The scale of a Hi-C experiment allows us to identify of long-range interactions. Unfortunately, the raw 3C and Hi-C data contain not only missing data, but also noisy data. Therefore, in order to use the data for an analysis, a further processing is required. In this project, we develop machine learning and deep learning models for the preprocessing purposes to improve the quality of the data. In addition, the sheer number of interactions generates a huge amount of data. We are also developing algorithms to store the data efficiently.Focuses of this project are:
In this project I have regular opportunities to offer (thesis, hiwi, etc.). Explicit topics for a thesis that I give out is individualized and on request, if available. If you are motivated and interested in this project, please send me a short email with the important information about you (relevant lectures you have attended / programming skills) and why you are interested. The following knowledge, qualities and experiences are helpful for the thesis:
Contact person: Yeremia G. Adhisantoso