Bio

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

The Sheffield Lab uses computation to ask and answer biological questions. We study the non-coding DNA that encodes gene regulatory networks and enables cellular differentiation, and how these networks break down in disease like cancer. To address these biological questions, we develop scientific computing infrastructure that enables large-scale computational biology research using cloud computing, software engineering, and maching learning.

Dr. Sheffield holds B.S. and Ph.D degrees in Bioinformatics and Computational Biology. He completed his PhD in 2013, working on large-scale analysis of chromatin accessibility in primates and among human cell-types. 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. Outside work, Dr. Sheffield enjoys spending time with his family, teaching his children, reading good books, writing, taking photos, and having fun. He is also the author of this popular Scientific Writing web resource.

Research Interests

  • Computational Biology and Bioinformatics
  • Biomedical Data Sciences
  • Machine Learning
  • Epigenomics
  • Gene Regulation and Chromatin
  • High-performance Computing

Selected Publications

  • Embeddings of genomic region sets capture rich biological associations in low dimensions. Bioinformatics (2021) ABS Gharavi E, Gu A, Zheng G, Smith JP, Zhang A, Brown DE, and Sheffield NC
  • COCOA: coordinate covariation analysis of epigenetic heterogeneity. Genome Biology (2020) ABS Lawson JT, Smith JP, Bekiranov S, Garrett-Bakelman FE, and Sheffield NC
  • Refgenie: a reference genome resource manager. GigaScience (2020) ABS Stolarczyk M, Reuter VP, Smith JP, Magee NE, and Sheffield NC
  • The chromatin accessibility landscape of primary human cancers. Science (2018) ABS Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, Silva TC, Groeneveld C, Wong CK, Cho SW, Satpathy AT, Mumbach MR, Hoadley KA, Robertson AG, Sheffield NC, Felau I, Castro MAA, Berman BP, Staudt LM, Zenklusen JC, Laird PW, Curtis C, Greenlea
  • DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma. Nature Medicine (2017) ABS Sheffield NC, Pierron G, Klughammer J, Datlinger P, Schönegger A, Schuster M, Hadler J, Surdez D et al.
  • LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics (2016) ABS Sheffield NC, and Bock C.
  • Multi-Omics of Single Cells: Strategies and Applications. Trends in Biotechnology (2016) ABS Bock C, Farlik M, and Sheffield NC.
  • Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Reports (2015) ABS Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schönegger A, Klughammer J, and Bock C.
Google Scholar

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.