In this work we present a set of studies that explore genomic sequencing data and offer computational methods to process these data at scale. Broadly the topics of this theses can be grouped into two categories: those that bridge the gap of efficient large scale data analysis with applications in public health and those that explore algorithmic solutions to analyses of clinically relevant metagenomic data. Across this set of topics we make several contributions that include scientific data analysis, and algorithm and software development.

In the realm of public health, we contribute an exploratory study of the genomic variation within SARS-CoV-2 and its impacts on our ability to track the virus and its spread. We also propose an efficient pipeline for characterization of wastewater derived SARS-CoV-2 samples which is employed for routine monitoring in Houston, USA. On the clinical metagenomics side we explore a scalable database-free approach for characterization of longitudinal changes in human gut microbiota. We also propose a laptop-friendly software for taxonomic profiling of long-read metagenomic samples.

Together the contributions of this thesis span two major application areas and cover topics of data-driven algorithm and software design.