RESEARCH CORE: GENOMIC DATA SCIENCE CORE SPECIFIC AIMS. The next-generation sequencing (NGS) revolution has generated massive amounts of new data that have transformed the field of genomics. Furthermore, development of new and existing genomics technologies continues to increase throughput of existing platforms while also giving rise to novel data types that measure an ever-growing list of genomic modalities1. Bioinformatic, computational, and statistical analysis approaches are critical for extraction of meaningful biological insights from the highly dimensional datasets generated by NGS technologies. Application of complex bioinformatics approaches has played a central role in recent scientific milestones, such as the completion and closure of the entire human genome sequence2, and rapid assembly of the Sars-CoV-2 genome during the COVID-19 pandemic3. Interdisciplinary frameworks that forge collaboration between quantitative and experimental researchers are required to maintain continued discovery in the genomic era. The field of single-cell genomics has recently seen intense and rapid development of novel technologies that have provided deeper insights into a vast array of biological processes4. These technologies have continued to increase not only the number of cells examined in a single experiment but also the number of genomic modalities that can be measured simultaneously. For example, integration of genomic and microscopic technologies has spawned the field of spatial transcriptomics, which enables spatial analysis of genome-wide gene expression at single cell-resolution5. However, the promise of these technologies requires the concurrent development of computational methodologies that can draw robust and efficient insights from these unique data. Development of novel approaches for specific single-cell applications is an active area of research, with a constant stream of new methods becoming available to the research community. However, leveraging these methods to make relevant insights requires teams of bioinformaticians, computational biologists, and quantitative methodologists who have diverse interdisciplinary backgrounds in genomics, statistics, data science, and computing. In Phase 1, the Data Analytics Core (renamed herein for Phase 2 as the Genomics Data Sciences Core, GDSC) developed a dynamic and interactive core facility that met the unique analytical needs of its wide user base. In Phase 2, we will build on the established services from Phase 1 to serve the new research project leads (RPLs) as well as those of the wider Dartmouth research community. Specifically, we will develop and incorporate analysis pipelines for spatial transcriptomics into our analysis portfolio to support the investment in cutting-edge instrumentation made by the Single-Cell Genomics Core (SCGC). In addition, we will continue to innovate and incorporate data analysis solutions for other emerging genomics technologies such as long-read sequencing appli...