Jonathan Griffiths

Jonathan Griffiths

Senior Scientist, Computational

Altos Labs

Biography

I am a computational scientist motivated by solving challenging problems to positively impact human health. I have developed deep expertise in biology, functional genomics, human genetics, and machine learning, and I like to work between these disciplines to affect the greatest possible impact.

Currently, I am working at Altos Labs in its Institute of Computation, leading a machine learning project developing large language models for epigenetics data.

During my PhD, I pioneered the first analyses of large-scale single-cell RNA-sequencing datasets. This was funded by Wellcome, and I worked at the University of Cambridge with John Marioni (now SVP, Head of Computational Sciences at Genentech).

Subsequently, I worked in the therapeutics arm of Genomics plc. I developed deep expertise in the use of human genetic data – and how to best combine it with functional genomics – to identify novel opportunities for therapeutic intervention. I identified novel targets, developed complete therapeutic stories, and worked with CROs to validate them.

Interests
  • Functional genomics
  • Human genetics
  • Machine learning
  • Single-cell data & methods
Education

  • PhD (Single-cell genomics), 2019

    Marioni lab, University of Cambridge

  • BSc (Natural Sciences), 2015

    University College London

Themes

Machine learning/AI
Excellent scientific communicator
Genomics & human genetics
Data sharing
R aficionado
Espresso

Career

 
 
 
 
 
Senior Scientist, Computational
Altos Labs
May 2024 – Present Cambridge, UK
I am leading a project developing large language models for epigenetics data, bringing together a team of machine learning and subject matter experts to deliver novel insights for target identification to reverse disease.
 
 
 
 
 
Computational Biologist
Altos Labs
Jul 2022 – Apr 2024 Cambridge, UK
I worked to set up Altos’ efforts in the human genetics space, combining these data with diverse -omics, and large-scale machine learning efforts in Altos to help qualify disease-reversing therapeutic targets.
 
 
 
 
 
Senior Scientist
Genomics plc
May 2021 – Jun 2022 Cambridge, UK
In addition to further developing my target-focused work, I: worked closely with experimental CROs for target validation; benchmarked and improved internal genetic association tools; establish substantial expertise in our disease area (including individual-level analyses and evaluation of the therapeutic landscape); and identified and resolved various other research questions as necessary.
 
 
 
 
 
Scientist
Genomics plc
May 2019 – Apr 2021 Cambridge, UK
I developed a range of tools and analyses to provide insight from functional genomic data, including end-to-end handling of single-cell RNA-seq datasets for work in Therapeutics, as well as improvements to the risk prediction tools for the Precision Health side of Genomics plc. Subsequently I moved into an internal therapeutics project, where I focused on the identification of new targets for therapeutic intervention, working from target ID through analyses to story-building and presentation.
 
 
 
 
 
PhD student
Marioni lab
Sep 2015 – Sep 2019 University of Cambridge
I led analysis on a landmark atlas of mouse embryonic development (which was among the first datasets generated on such a large scale) and subsequently generated gene-knockout chimaeric embryos. In addition, I developed several tools for technical analysis of single-cell RNA-sequencing data, including estimation of ploidy and removal of barcode-swapping sequencing artefacts.

Academic software portfolio

Barcode swapping
swappedDrops is a method to correct for artefacts from barcode swapping in 10X single-cell RNA sequencing data. Written with Aaron Lun in the DropletUtils package, which I now maintain.
Mouse gastrulation website
A Shiny app designed to allow quick review of the mouse gastrulation atlas dataset without bioinformatic expertise. The codebase has been used as a framework for other visualisation tools from the Marioni Lab in subsequent years.
MouseGastrulationData
MouseGastrulationData is a Bioconductor package that dramatically simplifies access to the mouse gastrulation atlas, embryo chimaera, and other related datasets.

Selected Talks

10x Genomics User Group Meeting - Paris
How we used the 10X platform to build the mouse gastrulation atlas, and how we corrected for common artefacts (doublets, etc.) produced by the system.
Sep 13, 2018 — Sep 14, 2018 Institut Pasteur
Quantitative Genomics 2018
A description of the mouse gastrulation single-cell atlas, and how we can use them to map experiments with perturbation. I also helped to organise the 2019 edition of this conference.
Jun 14, 2018 —
Biology of Genomes 2018
A summary of how we built the mouse gastrulation atlas, and how it can be used to understand development both by itself, and by using chimaeric embryos.
May 8, 2018 — May 12, 2018 Cold Spring Harbour Laboratory
Video
MASAMB 2018
A description of the phenomenon of barcode swapping, and how it can be efficiently remedied in droplet single-cell RNA-seq data.
Mar 19, 2018 — Mar 20, 2018
10x Genomics User Group Meeting - Cambridge
How the large scale of emerging 10x Genomics single-cell RNA-sequencing data rendered contemporary analysis techniques intractable, and a summary of novel methods to overcome the problem.
Jun 19, 2017 — Jun 20, 2017

Training & consulting

APTS PhD statistics course
Academy for PhD Training in Statistics Dec 2018 – Sep 2019
Four weeks of residential training in statistics and applied probability. Training is provided at a level suitable for mathematics and statistics PhD students.
Consultancy for Elpis Biomed
Elpis (now Bit Bio) Nov 2018 – Jan 2019
Provided analysis and interpretation of in-house RNA-sequencing data, including integration with public datasets.
Human Cell Atlas hackathon
Broad Institute Apr 2017
Invited to a hackathon on how to cost-effectively generate data for the Human Cell Atlas.

Publications

T. Lohoff, S. Ghazanfar, A. Missarova, N. Koulena, N. Pierson, J. A. Griffiths, E. S. Bardot, C.-H. L. Eng, R. C. V. Tyser, R. Argelaguet, C. Guibentif, S. Srinivas, J. Briscoe, B. D. Simons, A.-K. Hadjantonakis, B. Göttgens, W. Reik, J. Nichols, L. Cai, J. C. Marioni (2021). Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nature Biotechnology.

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Fernando Riveros-Mckay, Michael E. Weale, Rachel Moore, Saskia Selzam, Eva Krapohl, R. Michael Sivley, William A. Tarran, Peter Sørensen, Alexander S. Lachapelle, Jonathan A. Griffiths, Ayden Saffari, John Deanfield, Chris C.A. Spencer, Julia Hippisley-Cox, David J. Hunter, Jack W. O’Sullivan, Euan A. Ashley, Vincent Plagnol, Peter Donnelly (2021). Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction. Circulation: Genomic and Precision Medicine.

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Carolina Guibentif, Jonathan A. Griffiths, Ivan Imaz-Rosshandler, Shila Ghazanfar, Jennifer Nichols, Valerie Wilson, Berthold Göttgens, John C. Marioni (2020). Diverse Routes toward Early Somites in the Mouse Embryo. Developmental Cell.

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Blanca Pijuan-Sala, Jonathan A. Griffiths, Carolina Guibentif, Tom W. Hiscock, Wajid Jawaid, Fernando J. Calero-Nieto, Carla Mulas, Ximena Ibarra-Soria, Richard C. V. Tyser, Debbie Lee Lian Ho, Wolf Reik, Shankar Srinivas, Benjamin D. Simons, Jennifer Nichols, John C. Marioni, Berthold Göttgens (2019). A single-cell molecular map of mouse gastrulation and early organogenesis. Nature.

Cite DOI

Jonathan A. Griffiths, Arianne C. Richard, Karsten Bach, Aaron T. L. Lun, John C. Marioni (2018). Detection and removal of barcode swapping in single-cell RNA-seq data. Nature Communications.

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Jonathan A. Griffiths, Antonio Scialdone, John C. Marioni (2018). Using single‐cell genomics to understand developmental processes and cell fate decisions. Molecular Systems Biology.

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Jonathan A. Griffiths, Antonio Scialdone, John C. Marioni (2017). Mosaic autosomal aneuploidies are detectable from single-cell RNAseq data. BMC Genomics.

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