Dr John Marioni | Associate Faculty

Marioni, John

John Marioni's group develop computational and statistical tools to exploit high-throughput genomics data to understand the regulation of gene expression and to model developmental and evolutionary processes. His team has pioneered approaches for analysing single-cell transcriptomics data and, together with four colleagues, he co-ordinates the Sanger Institute-EBI Single-Cell Genomics Centre.

John graduated from the University of Edinburgh in 2003 with a BSc in Mathematics and Statistics before obtaining an MPhil in Statistical Science at the University of Cambridge in 2004. Subsequently, he read for a PhD in the University of Cambridge, where he developed statistical approaches for analysing DNA copy number data. Upon completing his PhD in 2007 he moved to the University of Chicago, where he carried out post-doctoral research in the Department of Human Genetics. In Chicago, he focused on the analysis of RNA-sequencing data, developing novel methods that have become widely used in the field.

In 2010, he established his research group at the EMBL-European Bioinformatics Institute. His independent research has focused on understanding how differential gene expression is regulated between closely related species of mammals and in modelling variability in gene expression between single cells

John's appointment in 2014 at the Sanger Institute enables his group to apply their computational approaches to novel biological questions through collaborations with faculty and research groups at the Institute.


  • High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin.

    Achim K, Pettit JB, Saraiva LR, Gavriouchkina D, Larsson T et al.

    Nature biotechnology 2015;33;5;503-9

  • BASiCS: Bayesian Analysis of Single-Cell Sequencing Data.

    Vallejos CA, Marioni JC and Richardson S

    PLoS computational biology 2015;11;6;e1004333

  • Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

    Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A et al.

    Nature biotechnology 2015;33;2;155-60

  • Computational and analytical challenges in single-cell transcriptomics.

    Stegle O, Teichmann SA and Marioni JC

    Nature reviews. Genetics 2015;16;3;133-45

  • High-resolution mapping of transcriptional dynamics across tissue development reveals a stable mRNA-tRNA interface.

    Schmitt BM, Rudolph KL, Karagianni P, Fonseca NA, White RJ et al.

    Genome research 2014;24;11;1797-807

  • Accounting for technical noise in single-cell RNA-seq experiments.

    Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X et al.

    Nature methods 2013;10;11;1093-5

  • Extensive compensatory cis-trans regulation in the evolution of mouse gene expression.

    Goncalves A, Leigh-Brown S, Thybert D, Stefflova K, Turro E et al.

    Genome research 2012;22;12;2376-84

Marioni, John
John's Timeline

Senior Group Leader, CRUK Cambridge Institute, University of Cambridge


Associate Faculty, Wellcome Trust Sanger Institute


Research Group Leader, EMBL-EBI

University of Chicago, PostDoc, Human Genetics


University of Cambridge, PhD, Computational Biology


University of Cambridge, MPhil, Statistical Science


Graduate from University of Edinburgh, BSc in Maths and Statistics