Description

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.

• Data Compression
• 3D Reconstruction
• Resolution Enhancement

• Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
• A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
• Genome-wide mapping and analysis of chromosome architecture
• (Data Storage) Scool: a new data storage format for single-cell Hi-C data
• (Data Storage) Contact Matrix Compressor
• (3D Reconstruction) Reconstruct high-resolution 3D genome structures for diverse cell-types using FLAMINGO
• (Resolution Enhancement) VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data

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:

Requirements

• Motivated
• Can work independently
• Things/knowledges you will use:
• Statistic
• (depends on topic) Source Coding
• (depends on topic) Machine Learning
• (depends on topic) Computer Vision
• (depends on topic) Deep Learning
• Good programming skill:
• Python and scientific computing libraries
• (depends on topic) C++

Contact person: Yeremia G. Adhisantoso