Statistical Methods for the Analysis of Genomic Data

Dear Colleagues,

In recent years, technology breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, ChIP-Seq, single-cell assays, Hi-C) have been developed in order to help explore the complex biological systems. As more genomic datasets become available, both in volume and variety, the analysis of such data has become a critical challenge as well as a topic of interest. Consequently, statistical methods dealing with the problems associated with these newly developed techniques are in high demand. This Special Issue will highlight the state-of-the-art statistical methods for the analysis of genomic data, and explore potential future directions for improvement.

Dr. Hui Jiang
Dr. Zhi He
Guest Editors

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