Bio

B.S. Bioinformatics, Brigham Young University, 2008Ph.D. Computational Biology and Bioinformatics, Duke University, 2013Post-Doc Computational Epigenetics, CeMM, Vienna, 2015, Stanford University, 2016

"Using computation to ask and answer biological questions, specifically how DNA encodes regulatory networks that enable cellular differentiation?"

Nathan Sheffield, Assistant Professor of Public Health Sciences and Biomedical Engineering

Dr. Sheffield grew up in California, but lived in many places in the US and abroad. He appreciates cultural diversity and spent much of his adult life living in Europe. As an undergraduate, he studied Bioinformatics and conducted research in phylogenetic systematics and population genetics with Michael Whiting and Keith Crandall. He completed his PhD at Duke in 2013, working on pattern recognition in gene regulation in primates and among human cell-types with Terry Furey (now at UNC), Greg Crawford, and Boris Lenhard (CSC and Imperial). After his PhD, Dr. Sheffield was an HFSP Postdoctoral Fellow in Christoph Bock’s Lab at the Center for Molecular Medicine, Vienna, with the return phase of his fellowship in Howard Chang’s Lab at Stanford. In free time, he enjoys spending time with his family, teaching his children, reading good books, writing, taking photos, and having fun.

Dr. Sheffield also has a passion for scientific writing, and is the author of this Scientific Writing web resource.

Research Interests

  • Biomedical Data Sciences
  • Biotechnology and Biomolecular Engineering (Biomolecular Design, Cellular and Molecular Bioengineering)
  • Biomedical Data Sciences
  • Machine Learning

Selected Publications

  • DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma. Nature Medicine (2017) Sheffield NC, Pierron G, Klughammer J, Datlinger P, Schönegger A, Schuster M, Hadler J, Surdez D et al.
  • Single-cell epigenomic variability reveals functional cancer heterogeneity. Genome Biology (2017) Litzenburger UM, Buenrostro JD, Wu B, Shen Y, Sheffield NC, Kathiria A, Greenleaf WJ, and Chang HY.
  • LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics (2016) Sheffield NC, and Bock C.
  • Multi-Omics of Single Cells: Strategies and Applications. rends in Biotechnology (2016) Bock C, Farlik M, and Sheffield NC.
  • The second European interdisciplinary Ewing sarcoma research summit - A joint effort to deconstructing the multiple layers of a complex disease. Oncotarget (2016) Kovar H, Amatruda J, Brunet E, Burdach S, Cidre-Aranaz F, de Alva E, Dirksen U, van der Ent W et al.
  • ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nature Methods (2015) Schmidl C, Rendeiro AF, Sheffield NC, and Bock C.
  • Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep (2015) Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schönegger A, Klughammer J, and Bock C.
  • Epigenome mapping reveals distinct modes of gene regulation and widespread enhancer reprogramming by the oncogenic fusion protein EWS-FLI1. Cell Rep (2015) Tomazou EM, Sheffield NC, Schmidl C, Schuster M, Schönegger A, Datlinger P, Kubicek S, Bock C, and Kovar H.

Featured Grants & Projects

  • Computational cancer epigenomics


    I am interested in understanding how cancers commandeer the normal regulatory machinery to create disease. As a model system, I use Ewing sarcoma, a pediatric tumor, which is a good model because it is almost always driven by a single, well-characterized mutagenic event: a chromosomal translocation leading to the fusion protein EWS-FLI1. To explore how this fusion protein re-wires the cells to proliferate uncontrollably, I am examining genome-wide epigenetic profiles of Ewing sarcoma. These types of questions lead to computational problems inherent in dealing with lots of data from different individuals, cancers, and data types.

  • Single-cell sequencing analysis


    In the past, we have only been able to sequence populations of cells, leaving important cell-to-cell differences unexplored. New microfluidics and sequencing technology is making it possible to ask questions about single cells. Using this technology, I am interested in fundamental questions about how cells differentiate and respond to their environments at the single cell level.

  • Gene regulation and chromatin structure


    I am interested in how cells fold their DNA to enable complex regulatory patterns. Humans are made up of many different cell-types. Though these cell-types share a single genome, they have very different phenotypes and functions, working together to enable multicellular life. The basis for these dynamics is regulatory DNA, which governs when and where different genes are expressed. I analyze data from high-throughput ChIP-seq, DNase-seq, and ATAC-seq experiments to understand how cells do this during development.