Developing Compressive Algorithms for Large-Scale Metagenomic Data Analysis
Metagenomic datasets are exponentially increasing in size at a rate that outpaces computing power. This project focuses on using compression to mitigate the computational burden of metagenomic datasets. Compressive genomics is based on the redundancy of the genome and the project aims to exploit this redundancy on a metagenomic level. These compressive methods will dramatically accelerate the meta-genomic pipeline and allow researchers to deriving tangible results from increasingly large datasets efficiently.

My name is Divya Shanmugam and I’m a senior majoring in Course 6-3. I’ve spent the past couple of years loosely interested in the intersection of algorithms with the life sciences. Much of my past work has been geared towards this interest and the same can be said about this SuperUROP. I hope to develop a strong framework for research this semester and sharpen my ability to approach open problems this semester!