Author Description

The rapid development of high throughput sequencing (HTS) technology has reshaped almost every field of life sciences and biomedical sciences. Large scale genome sequencing projects are now producing petabyte-level genomic data from thousands of patients. Rapid generation of genomic data offer great promise in advancing precision medicine and providing personalized treatments. However, the sheer size of genomic data necessitates the development of novel technologies and computational methods for their communication and storage. In addition, since genomic data carries sensitive information of individuals and their blood relatives, sharing such data introduces potential privacy and security risks. All of the above considerations necessitate the development of novel algorithms to address the key challenges in storing, exchanging and analyzing genomic data efficiently, in a privacy-preserving fashion. In this thesis I introduce three unique approaches to address these challenges in genomic data management: compression, indexing, and privacy-preserving analysis/querying.