{"id":373,"date":"2025-11-12T13:33:27","date_gmt":"2025-11-12T18:33:27","guid":{"rendered":"https:\/\/web.uri.edu\/iacr\/?p=373"},"modified":"2025-11-12T14:28:43","modified_gmt":"2025-11-12T19:28:43","slug":"faculty-spotlight-rachel-schwartz-biology","status":"publish","type":"post","link":"https:\/\/web.uri.edu\/iacr\/faculty-spotlight-rachel-schwartz-biology\/","title":{"rendered":"Faculty Spotlight: Rachel Schwartz (Biology)"},"content":{"rendered":"\n

Decoding the Tree of Life: How Research Computing Enables Large-Scale Genomics at URI<\/strong><\/p>\n\n\n\n

At the University of Rhode Island, the push to answer fundamental questions about evolutionary history increasingly relies on massive computational power. For Dr. Rachel Schwartz<\/a>, an Associate Professor who runs a “dry lab” focused on computational evolutionary genetics, access to URI\u2019s research computing resources is not just helpful\u2014it\u2019s critical to her work analyzing some of the largest genomics datasets in science today.<\/p>\n\n\n\n

The Computational Core of Evolution<\/strong>
Dr. Schwartz\u2019s research centers on developing methods and software to interpret the vast amounts of data produced by next-generation sequencing. Her lab, which draws students from fields spanning computer science, math, and evolutionary biology, seeks to extract meaningful, phylogenetically informative data to map the relationships among organisms, from strains of pathogens to the entire tree of life.<\/p>\n\n\n\n

One key challenge is sifting through raw genomic data to find the relevant evolutionary signals. Dr. Schwartz developed software called SISRS (Site Identification from Short Read Sequences), which has revealed that non-coding loci often contain more robust phylogenetic signal than traditionally studied coding loci.<\/p>\n\n\n\n

\u201cBeing able to analyze large-scale genomic data across many species to determine phylogenetic relationships requires substantial compute power,\u201d Dr. Schwartz explains. \u201cData are getting larger, more complex, and analyses are getting larger and more complex. Research computing is becoming even more critical.\u201d<\/p>\n\n\n\n

HPC: The Essential Engine for Big Data Genomics<\/strong>
For Dr. Schwartz, the ability to pursue cutting-edge computational work hinges entirely on URI\u2019s high-performance computing (HPC) resources<\/a>, particularly the Unity cluster<\/a>.<\/p>\n\n\n\n

The scale of modern genomics data simply exceeds the capacity of local machines. The Unity cluster provides the storage and computational muscle necessary for:<\/p>\n\n\n\n