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

  • Expression Atlas update: from tissues to single cells.

    Papatheodorou I, Moreno P, Manning J, Fuentes AM, George N et al.

    Nucleic acids research 2019

  • Resolving the fibrotic niche of human liver cirrhosis at single-cell level.

    Ramachandran P, Dobie R, Wilson-Kanamori JR, Dora EF, Henderson BEP et al.

    Nature 2019

  • The human body at cellular resolution: the NIH Human Biomolecular Atlas Program.

    HuBMAP Consortium

    Nature 2019;574;7777;187-192

  • IL-7-dependent compositional changes within the γδ T cell pool in lymph nodes during ageing lead to an unbalanced anti-tumour response.

    Chen HC, Eling N, Martinez-Jimenez CP, O'Brien LM, Carbonaro V et al.

    EMBO reports 2019;e47379

  • A transcriptomic atlas of mammalian olfactory mucosae reveals an evolutionary influence on food odor detection in humans.

    Saraiva LR, Riveros-McKay F, Mezzavilla M, Abou-Moussa EH, Arayata CJ et al.

    Science advances 2019;5;7;eaax0396

  • Challenges in measuring and understanding biological noise.

    Eling N, Morgan MD and Marioni JC

    Nature reviews. Genetics 2019

  • Identification of a regeneration-organizing cell in the Xenopus tail.

    Aztekin C, Hiscock TW, Marioni JC, Gurdon JB, Simons BD and Jullien J

    Science (New York, N.Y.) 2019;364;6441;653-658

  • EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.

    Lun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T et al.

    Genome biology 2019;20;1;63

  • Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis.

    Ernst C, Eling N, Martinez-Jimenez CP, Marioni JC and Odom DT

    Nature communications 2019;10;1;1251

  • Support for a clade of Placozoa and Cnidaria in genes with minimal compositional bias.

    Laumer CE, Gruber-Vodicka H, Hadfield MG, Pearse VB, Riesgo A et al.

    eLife 2018;7

  • COMRADES determines in vivo RNA structures and interactions.

    Ziv O, Gabryelska MM, Lun ATL, Gebert LFR, Sheu-Gruttadauria J et al.

    Nature methods 2018

  • CTCF maintains regulatory homeostasis of cancer pathways.

    Aitken SJ, Ibarra-Soria X, Kentepozidou E, Flicek P, Feig C et al.

    Genome biology 2018;19;1;106

  • T cell cytolytic capacity is independent of initial stimulation strength.

    Richard AC, Lun ATL, Lau WWY, Göttgens B, Marioni JC and Griffiths GM

    Nature immunology 2018

  • Detection and removal of barcode swapping in single-cell RNA-seq data.

    Griffiths JA, Richard AC, Bach K, Lun ATL and Marioni JC

    Nature communications 2018;9;1;2667

  • CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness.

    Morgan MD and Marioni JC

    Genome biology 2018;19;1;81

  • Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

    Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T et al.

    Molecular systems biology 2018;14;6;e8124

  • Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis.

    Stojic L, Lun ATL, Mangei J, Mascalchi P, Quarantotti V et al.

    Nucleic acids research 2018

  • Using single-cell genomics to understand developmental processes and cell fate decisions.

    Griffiths JA, Scialdone A and Marioni JC

    Molecular systems biology 2018;14;4;e8046

  • Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

    Haghverdi L, Lun ATL, Morgan MD and Marioni JC

    Nature biotechnology 2018

  • Maturing Human CD127+ CCR7+ PDL1+ Dendritic Cells Express AIRE in the Absence of Tissue Restricted Antigens.

    Fergusson JR, Morgan MD, Bruchard M, Huitema L, Heesters BA et al.

    Frontiers in immunology 2018;9;2902

  • SRSF3 maintains transcriptome integrity in oocytes by regulation of alternative splicing and transposable elements.

    Do DV, Strauss B, Cukuroglu E, Macaulay I, Wee KB et al.

    Cell discovery 2018;4;33

  • Single-Cell Landscape of Transcriptional Heterogeneity and Cell Fate Decisions during Mouse Early Gastrulation.

    Mohammed H, Hernando-Herraez I, Savino A, Scialdone A, Macaulay I et al.

    Cell reports 2017;20;5;1215-1228

  • Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos.

    Goolam M, Scialdone A, Graham SJL, Macaulay IC, Jedrusik A et al.

    Cell 2016;165;1;61-74

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