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.
Nature biotechnology 2015;33;5;503-9
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data.
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.
Nature biotechnology 2015;33;2;155-60
Computational and analytical challenges in single-cell transcriptomics.
Nature reviews. Genetics 2015;16;3;133-45
High-resolution mapping of transcriptional dynamics across tissue development reveals a stable mRNA-tRNA interface.
Genome research 2014;24;11;1797-807
Accounting for technical noise in single-cell RNA-seq experiments.
Nature methods 2013;10;11;1093-5
Extensive compensatory cis-trans regulation in the evolution of mouse gene expression.
Genome research 2012;22;12;2376-84
COMRADES determines in vivo RNA structures and interactions.
Nature methods 2018
CTCF maintains regulatory homeostasis of cancer pathways.
Genome biology 2018;19;1;106
T cell cytolytic capacity is independent of initial stimulation strength.
Nature immunology 2018
Detection and removal of barcode swapping in single-cell RNA-seq data.
Nature communications 2018;9;1;2667
CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness.
Genome biology 2018;19;1;81
Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.
Molecular systems biology 2018;14;6;e8124
Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis.
Nucleic acids research 2018
Using single-cell genomics to understand developmental processes and cell fate decisions.
Molecular systems biology 2018;14;4;e8046
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.
Nature biotechnology 2018
SRSF3 maintains transcriptome integrity in oocytes by regulation of alternative splicing and transposable elements.
Cell discovery 2018;4;33
Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos.