GEOSPATIAL AND NETWORK ANALYSIS OF SARS-COV-2 MUTATIONS AND CANCER FOR UNDERSTANDING DISEASE PATTERNS

Shruti Sunil Patil

The availability of biological sequences and disease related data has increased dramatically in the recent years due to advances in genome sequencing technologies, large scale collection efforts, and expanded public data repositories. While such data provides valuable opportunities to study molecular variation and disease mechanisms, extracting meaningful insights from large and complex datasets remains a major challenge. Computational approaches are therefore essential for identifying patterns, relationships, and trends from these datasets. Network-based methods have proven particularly effective for modeling complex biological systems and uncovering relationships among molecular entities, enabling the study of molecular evolution, pathogenic mutations, gene interactions, and disease-associated variation. At the same time, geospatial analysis provides an important complementary perspective by revealing how biological and disease patterns vary across geographic regions and populations. This dissertation integrates network and geospatial analytical approaches to investigate mutation patterns and disease distributions in two major contexts: SARS-CoV-2 and human cancers. The first study analyzes mutation patterns in the SARS-CoV-2 spike protein using sequence similarity analysis, network-based methods, and geospatial visualizations to examine the temporal and geographic distribution of viral variants across the United States. The second study applies network-based approaches to tumor genomic data to identify relationships among cancer driver genes, revealing patterns of mutation co-occurrence, exclusivity, and cancer type–specific mutation signatures. The third study introduces GMCAV, an interactive web-based tool that supports exploratory analysis of cancer incidence, associated factors, and genomic mutations using geospatial and molecular cancer data. Together, these studies demonstrate how leveraging network and geospatial analytical approaches can provide deeper insights into disease patterns and improve our ability to explore complex biological and epidemiological datasets.

GEOSPATIAL AND NETWORK ANALYSIS OF SARS-COV-2 MUTATIONS AND CANCER FOR UNDERSTANDING DISEASE PATTERNS

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