# Genomic Data Science Core

> **NIH NIH P20** · DARTMOUTH COLLEGE · 2024 · $349,251

## Abstract

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...

## Key facts

- **NIH application ID:** 10852729
- **Project number:** 2P20GM130454-06
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Shannon Soucy
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $349,251
- **Award type:** 2
- **Project period:** 2019-08-01 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10852729

## Citation

> US National Institutes of Health, RePORTER application 10852729, Genomic Data Science Core (2P20GM130454-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10852729. Licensed CC0.

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