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 make the biggest impact possible.

Currently, I am working at Altos Labs in the BioML team, developing foundation models of DNA sequence.

Previously, I worked in the therapeutics arm of Genomics plc. This is where I learned about human genetics, and I use dmy interdisciplinary skills to identify novel opportunities for therapeutic intervention, and guided the design of experiments to validate these hypothesis.

During my PhD, I worked on methodology for analysing the first large-scale, droplet-based single-cell RNA-sequencing datasets. I applied these methods by leasing the analysis of a landmark scRNA-seq dataset of mouse embryonic development. My PhD was funded by Wellcome, in the Marioni lab.

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

Statistics & machine learning
Genomics & human genetics
Single-cell methods
Data sharing
R aficionado
Espresso

Career

 
 
 
 
 
Senior Scientist, Computational
May 2024 – Present Cambridge, UK
I am developing foundation models of DNA sequence, which allows me to bring together my genetics and functional genomics background with large-scale AI/machine learning tools to identify mechanisms of gene expression and epigenetic regulation.
 
 
 
 
 
Computational Biologist
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 in Altos to help qualify disease-reversing therapeutic targets.
 
 
 
 
 
Senior Scientist
May 2021 – Jun 2022 Cambridge, UK
In addition to further developing my target-focused work, I: worked closely with experimental collaborators 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
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
Sep 2015 – Sep 2019 University of Cambridge
I led analysis on a landmark atlas of mouse embryonic development and subsequently generated gene-knockout chimaeric embryos. In addition, I developed several tools for analysis of single-cell RNA-sequencing data, including estimation of ploidy and removal of barcode-swapping sequencing artefacts.

Training & consulting

APTS PhD statistics course
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
Provided analysis and interpretation of in-house RNA-sequencing data, including integration with public datasets.
Human Cell Atlas hackathon
Invited to a hackathon on how to cost-effectively generate data for the Human Cell Atlas.

Publications

(2021). Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nature Biotechnology.

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(2021). Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction. Circulation: Genomic and Precision Medicine.

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(2020). Diverse Routes toward Early Somites in the Mouse Embryo. Developmental Cell.

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(2019). A single-cell molecular map of mouse gastrulation and early organogenesis. Nature.

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(2018). Detection and removal of barcode swapping in single-cell RNA-seq data. Nature Communications.

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(2018). Using single‐cell genomics to understand developmental processes and cell fate decisions. Molecular Systems Biology.

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(2017). Mosaic autosomal aneuploidies are detectable from single-cell RNAseq data. BMC Genomics.

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