The emergence of Hi-C and related techniques have facilitated research focused on the study of genome-wide and chromosomal conformation and interactions.
In the last decade, these technologies have played an important role in increasing our understanding of the relationship between 3D genome structure and disease occurrences at a chromosomal and genome-wide scale. It has allowed the structural comparisons of chromosomes to identify and understand the structural deviations that occur between cells, e.g. a normal and a malignant B-cell.
In addition, the development and expansion of the Hi-C technique have led to an increasing amount of data—from different individuals and cell types, for example—and higher resolution data—from multiple cells, single cells, or subcellular localizations—which has consequently increased the complexity of the datasets. This increase necessitates the development of novel approaches to infer spatial chromosomal patterns using more sophisticated and computational approaches.
In this context, many computational approaches and advanced machine learning algorithms are becoming more prevalent, providing additional and alternative bioinformatics tools for the analysis of 3D genome structures.
Our goal with this Research Topic is to bring together a community of scientists and researchers working at the interface of computational biology and machine learning (or physics, statistics, computer science, and mathematics) to conduct research on this problem domain and to propose new algorithms that foster advanced discovery in order to investigate and analyze the genome organization.
Typical themes for the research scope will include:
? 3D chromatin structure visualization and analysis
? 3D genome structure reconstruction and analysis
? A/B compartment detection
? Topologically associating domain (TAD) detection
? Chromatin loop detection
? Epigenetics and 3D genome
? Transcriptional regulation and 3D genome
? Resolution enhancement of Hi-C data
? Methods for measuring the reproducibility and quality of Hi-C data
? Normalization and de-noising of single-cell Hi-C, bulk-cell Hi-C data, and promoter capture Hi-C data
? Single-cell Hi-C data analysis and imputation
? 4D genome structure analysis
The emergence of Hi-C and related techniques have facilitated research focused on the study of genome-wide and chromosomal conformation and interactions.
In the last decade, these technologies have played an important role in increasing our understanding of the relationship between 3D genome structure and disease occurrences at a chromosomal and genome-wide scale. It has allowed the structural comparisons of chromosomes to identify and understand the structural deviations that occur between cells, e.g. a normal and a malignant B-cell.
In addition, the development and expansion of the Hi-C technique have led to an increasing amount of data—from different individuals and cell types, for example—and higher resolution data—from multiple cells, single cells, or subcellular localizations—which has consequently increased the complexity of the datasets. This increase necessitates the development of novel approaches to infer spatial chromosomal patterns using more sophisticated and computational approaches.
In this context, many computational approaches and advanced machine learning algorithms are becoming more prevalent, providing additional and alternative bioinformatics tools for the analysis of 3D genome structures.
Our goal with this Research Topic is to bring together a community of scientists and researchers working at the interface of computational biology and machine learning (or physics, statistics, computer science, and mathematics) to conduct research on this problem domain and to propose new algorithms that foster advanced discovery in order to investigate and analyze the genome organization.
Typical themes for the research scope will include:
? 3D chromatin structure visualization and analysis
? 3D genome structure reconstruction and analysis
? A/B compartment detection
? Topologically associating domain (TAD) detection
? Chromatin loop detection
? Epigenetics and 3D genome
? Transcriptional regulation and 3D genome
? Resolution enhancement of Hi-C data
? Methods for measuring the reproducibility and quality of Hi-C data
? Normalization and de-noising of single-cell Hi-C, bulk-cell Hi-C data, and promoter capture Hi-C data
? Single-cell Hi-C data analysis and imputation
? 4D genome structure analysis
