Nathan Sheffield

About

Nathan Sheffield is an Associate Professor in the Department of Genome Sciences at the University of Virginia School of Medicine. He holds a B.S. in Bioinformatics from Brigham Young University and a Ph.D. in Computational Biology from Duke University. His research is at the interface of computation and biology, drawing on techniques in computer science, data science, bioinformatics, and statistics, and applying them to biological questions in cancer, epigenetics, development, and genomics. His particular projects include computational cancer epigenomics, where he investigates how cancers rewire normal regulatory machinery, using Ewing sarcoma as a model system to examine genome-wide epigenetic profiles. He is also engaged in genome-scale analysis of gene regulation and chromatin structure, focusing on how different cell types fold their DNA to enable complex regulatory patterns, and how regulatory DNA governs gene expression during development. His work involves the use of machine learning, supercomputing, and software engineering to analyze high-throughput genomic data, aiming to understand cellular changes and disease mechanisms.

Research topics

• Biology
• Medicine
• Genetics
• Data Mining
• Computer Security
• Bioinformatics
• Computational biology
• Computer Science
• Political Science
• Pathology

Selected publications

• GA4GH: International policies and standards for data sharing across genomic research and healthcare

  Cell Genomics · 2021 · 292 citations

  The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective,…

  PublisherDOI

• Multimodal analysis of cell-free DNA whole-genome sequencing for pediatric cancers with low mutational burden

  Nature Communications · 2021 · 193 citations

  Sequencing of cell-free DNA in the blood of cancer patients (liquid biopsy) provides attractive opportunities for early diagnosis, assessment of treatment response, and minimally invasive disease monitoring. To unlock liquid biopsy analysis for pediatric tumors with few genetic aberrations, we introduce an integrated genetic/epigenetic analysis method and demonstrate its utility on 241 deep whole-genome sequencing profiles of 95 patients with Ewing sarcoma and 31 patients with other pediatric sa…

  DOI

• Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis

  Cell Reports · 2023 · 99 citations

  Coronary artery disease (CAD) is characterized by atherosclerotic plaque formation in the arterial wall. CAD progression involves complex interactions and phenotypic plasticity among vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but human cell phenotypes remain controversial. Here, we perform an integrated meta-analysis of 22 scRNA-seq libraries to generate a comprehensive map of human atherosclerosis with…

  PublisherOA PDFDOI

• AI-readiness Criteria for Biomedical Data

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-10-25 · 23 citations

  preprintOpen access

  Abstract Biomedical research is rapidly adopting artificial intelligence (AI). Yet the inherent complexity of biomedical data preparation requires implementing actionable, robust criteria for ethical and explainable AI (XAI) at the “pre-model” stage, encompassing data acquisition, detailed transformations, and ethical governance. Simple conformance to FAIR (Findable, Accessible, Interoperable, Reusable) Principles is insufficient. Here, we define criteria and practices for reliable AI-readiness…

  PublisherOA PDFDOI

• Inhibition of Renin Expression Is Regulated by an Epigenetic Switch From an Active to a Poised State

  Hypertension · 2024-07-11 · 7 citations

  articleOpen access

  BACKGROUND: Renin-expressing cells are myoendocrine cells crucial for the maintenance of homeostasis. Renin is regulated by cAMP, p300 (histone acetyltransferase p300)/CBP (CREB-binding protein), and Brd4 (bromodomain-containing protein 4) proteins and associated pathways. However, the specific regulatory changes that occur following inhibition of these pathways are not clear. METHODS: We treated As4.1 cells (tumoral cells derived from mouse juxtaglomerular cells that constitutively express reni…

  PublisherOA PDFDOI

Recent grants

• Novel methods for large-scale genomic interval comparison

  NIH · $1.8M · 2022–2026

• A modular data analysis ecosystem using portable encapsulated projects

  NIH · $1.6M · 2018–2023

• A modular data analysis ecosystem using portable encapsulated projects

  NIH · $360k · 2018–2023

Frequent coauthors

• Christoph Bock

  70 shared

• Jason P. Smith

  United States Department of Veterans Affairs

  43 shared

• Terrence S. Furey

  University of North Carolina at Chapel Hill

  37 shared

• Gregory E. Crawford

  Durham Technical Community College

  35 shared

• Lingyun Song

  33 shared

• Yoichiro Shibata

  30 shared

• Alok K. Tewari

  Dana-Farber Cancer Institute

  29 shared

• Galip Gürkan Yardımcı

  Oregon Health & Science University

  28 shared

Education

• Postdoc

  Stanford University

  2016

• Postdoc

  CeMM Research Center for Molecular Medicine

  2015

• PhD Computational Biology and Bioinformatics, Program in Computational Biology and Bioinformatics

  Duke University

  2013

• B.S. Bioinformatics, Biology

  Brigham Young University

  2008

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