Jude Popham

Jude Popham

Computational Biologist, ex-Nucleome Therapeutics

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

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portfolio

Integrative Multi-omics Analysis of Human Carotid Plaque Reveals Protective Smooth Muscle Cell Phenotypes

Duration: January 01, 2023 - October 01, 2023

Aims

  • Integrate three public carotid plaque scRNA-seq datasets into a single atlas, benchmarking batch-correction methods rather than assuming one.
  • Sub-cluster the smooth muscle compartment and characterise its phenotypes from marker genes and differential expression.
  • Associate each phenotype with the lab's plaque proteomics across calcification, symptom status, sex and plaque region.

Figure 3: General Workflow of the project. Please click the title for more detailed information on results and outcomes.

Skills and Tools

TaskPackages/Tools
Single-cell Integration and Benchmarking
  • Seurat v4.3.0 (R)
  • Scanpy (Python)
  • Harmony, Scanorama, Seurat rPCA
  • SeuratDisk
Benchmark Metrics
  • NMI, ARI, average silhouette width
  • kBET, LISI
Annotation and Clustering
  • Tabula Sapiens label transfer
  • Louvain and Leiden clustering
  • Differential expression, PANTHER protein classes
Proteomics and Visualisation
  • TMT 10-plex mass spectrometry data
  • ggplot2

Elucidating Spatial Cell Composition of Neuroblastoma (Group Project)

Duration: January 01, 2024 - March 01, 2024

Aims

  • Meta-analyse various single-cell studies of neuroblastoma, building a single-cell atlas that spans tumour cell diversity (Figure 2.1)
  • Identify cell populations and tissue heterogeneity in single cell spatial transcriptomics (SCST) data using transfer learning from the single-cell reference.
  • Validate, test and scrutinise available tools for exploratory analysis of novel SCST data, including fine tuning and adpatation of a novel, multi-modal deep learning clustering approach (SiGra) (Figure 2.2)

Figure 2.1: single-cell RNA-seq workflow. Figure 2.2: single-cell spatial RNA-seq workflow. Please click the title for more detailed information on results and outcomes.

Skills and Tools

TaskPackages/Tools
Single-Cell RNA-Seq Data Integration + Analysis
  • Python: scanpy, rpy2, anndata2ri, integration(scanorama, scvi-tools), monocle, palantir
  • R: seurat, integration (harmony, rPCA CCA, BBKNN), CONICSmat, CellChat
Single-Cell Spatial RNA-Seq Data Analysis
  • squidpy, transfer learning (cell2location, singleR, Seurat, RCTD, scarches), nichedb,
Deep Learning (SiGRA Modification)
  • torchvision, matplotlib(v2.1.1), torch, seaborn, tqdm, scikit_learn, torch_geometric, keras, optuna, weights and biases (wandb), xgboost
Shell Computing
  • Unix:, Git (init, clone, add, commit, status), pull, push, branch, merge), ssh, High Performance Computing (HPC), nohup, rsync, Slurm (sbatch), module, chmod (permissions)

Regulation and Biological Functions of Alternative Splicing in Neurons of the Adult Mouse Visual Cortex

Duration: June 01, 2022 - September 01, 2022

Aims

  • Process 4 single cell/single nuclear datasets from the Allen Brain Institute in R
  • Generate high throughput splicing data using specialised tools in Unix (e.g Whippet).
  • Identify evidence to support the mechanism and presence of NMD containing transcripts in the nuclei of PV interneurons
  • Map different isoform profiles of GABAergic and glutamatergic layers
  • Validate the utility of Single-Cell data for splicing analysis compared to Bulk RNA-seq

Figure 4: Poster displaying the outcome of this project investigating alternative splicing during Neurogenesis. Please click the title for more detailed information on results and outcomes.

Skills and Tools

TaskPackages/Tools
Single-cell and Single-nucleus RNA-Seq
  • Seurat v3 (R)
  • QC, normalisation, PCA, clustering, UMAP, marker genes
Splicing Quantification
  • Whippet
  • Unix shell pipelines
Functional Interpretation
  • Gene ontology enrichment

Multi-Omics Pathway-based Data Integration Using Machine Learning and Large Language Models (LLMs)

Duration: March 01, 2024 - October 01, 2024

Aims

  • Use LLM-based semantic searching to map more metabolite names to IDs, widening the scope of pathway analysis (Figure 1A)
  • Apply novel pathway databases augmented with metabolites using deep learning, facilitating more comprehensive pathway mapping and attaining more robust biological predictions (Figure 1B)
  • Develop an extension to PathIntegrate which will provide an unsupervised utility for pathway-based multivariate analysis that can be benchmarked with synthetic data simulations (Figure 1C)

Figure 1: General Workflow of the project. Please click the title for more detailed information on results and outcomes.

Skills and Tools

TaskPackages/Tools
Data Processing, Analysis and Databases
  • pandas, duckdb SQL, fancyimpute, networkx, missforest, statsmodels, plotly, gseapy, matplotlib, seaborn, missforest, scipy
Machine Learning + Web App
  • leidenalg, streamlit (HTML + CSS), elasticsearch, sentence_transformers, HuggingFace, base64, mbpls
  • sklearn: metrics (f1_score, precision_score, recall_score, roc_auc_score, roc_curve, confusion_matrix), model_selection (train_test_split, cross_val_score, GridSearchCV), pipeline (Pipeline), preprocessing (StandardScalar), linear_model (LogisticRegression), decomposition (PCA), manifold (TSNE)
Miscellaneous
  • response, requests, OpenAI, urllib, igraph, json, tdqm, warnings, SLURM
Bioinformatics Tools
  • KEGG API, Reactome API, CytoScape, ssPA, GSEA, MOFA2, pathintegrate, iPATH3

Computational Biologist, then Junior ML Engineer, Nucleome Therapeutics

Duration: September 01, 2024 - October 01, 2025

Aims

  • Replicate five external genomics data sources inside the in-house target-information portal, exactly as presented on their own web pages, in a format that loads and plots efficiently.
  • Fine-tune genomic foundation models, including Enformer and Evo2, on ATAC-seq and DNase-seq data to try to improve on the in-house DeepHaem architecture.
  • Establish allelic skewing as a functional validation method for chromatin-accessibility predictions, and use it to compare model versions and direct training protocols.

Nucleome Therapeutics. Joined as a Computational Biologist building the data pipelines behind an in-house target-information portal, then moved into the ML team as a Junior ML Engineer, fine-tuning genomic foundation models on chromatin accessibility data and establishing allelic skewing as a functional validation method. Please click the title for more detailed information on results and outcomes.

Skills and Tools

TaskPackages/Tools
Columnar Data Engineering
  • Parquet (row-group optimisation)
  • pandas
Cloud Data Engineering
  • AWS S3
  • AWS SageMaker
Web Scraping
  • BeautifulSoup (static)
  • Selenium (dynamic)
Relational Database Querying
  • PostgreSQL
  • ChEMBL schema
Single-Cell Analysis
  • Scanpy
  • AnnData / .h5ad
  • Seurat
Genomic Foundation Models
  • Enformer
  • Evo2
  • DeepHaem
  • PyTorch
Functional Validation
  • ATAC-seq allelic skewing
  • Odds-ratio enrichment analysis

DPhil in Medical Science, University of Oxford

Duration: October 01, 2025 - May 01, 2029

University of Oxford. Doctoral research in the lab of Prof. James Davies, academic co-founder of Nucleome Therapeutics, co-supervised by Prof. Christopher Yau, who directs the Ellison Institute of Technology CDT in Fundamentals of AI. Scaling base-pair-resolution 3D genomics (Micro-Capture-C) across the CD4+ T cell genome with machine learning, to read gene regulation out of chromatin structure.

Computer Vision Prototyping for Intra-Operative AI

Duration: August 01, 2019 - September 01, 2019

Aims

  • Build computer vision prototypes for intra-operative AI, working on video from live surgical procedures.
  • Pre-train and test models supporting the company's remote and tele-mentored surgery ambitions.
  • Contribute to data infrastructure improvements and surgical AI validation protocols.

Touch Surgery, acquired by Medtronic for $300M five months later. Pre-training and testing computer vision models for intra-operative video, in support of the company’s remote and tele-mentored surgery work.

Skills and Tools

TaskPackages/Tools
Computer Vision
  • OpenCV
  • NVIDIA CUDA
Model Training and Evaluation
  • Model pre-training
  • Validation protocol design
Data Infrastructure
  • Surgical video pipelines

projects

publications

Alternative Splicing and Neurogenesis

Published in KCL Biochemist Print, 2021

This article was inspired by the work of Professor Eugene Makeyev who studied intricate processes of gene expression regulation, such as NMD, during neurogenesis. My interest in this biological problem inspired my first bioinformatics internship which kickstarted my journey in the field today

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Novel Plastic Degrading Enzymes

Published in KCL Biochemist Print, 2022

This article was inspired by the work of Professor Zelesniak who studied plastic degrading enzymes. It covers key processes of bioremediation and the potential of these enzymes for efficient recycling.

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Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes

Published in Circulation Research, 2023

I was a co-author for this publication, working on the single-cell component. This study provides a comprehensive proteomics analysis of human atherosclerotic plaques, revealing sex differences and distinct plaque phenotypes with prognostic implications.

GitHub Repository: https://github.com/Cardiovascular-Bioinformatics/Plaques_Cellular_Composition

Cited 88 times (OpenAlex, September 2026)

Citation: Theofilatos K, Stojkovic S, Hasman M, Popham J, et al. (2023). "Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes." Circulation Research. 133(7):542-558. doi:10.1161/CIRCRESAHA.123.322590.
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PathIntegrate: Multivariate Modelling Approaches for Pathway-Based Multi-Omics Data Integration

Published in PLOS Computational Biology, 2024

I contributed to the development of the PyPi PathIntegrate Python package (included in v1.0.0), a tool for pathway-based multi-omics data integration used in this publication. My contribution has a seperate doi but it is based on this paper from PLOS.

GitHub Repository: https://github.com/cwieder/PathIntegrate

Cited 37 times (OpenAlex, September 2026)

Citation: Popham, J., Wieder, C., & Ebbels, T. (2024). cwieder/PathIntegrate: PathIntegrate Unsupervised (v1.0.0). Zenodo. --> 'Wieder C, Cooke J, Frainay C, et al. (2024). PathIntegrate: Multivariate Modelling Approaches for Pathway-Based Multi-Omics Data Integration. PLOS Computational Biology. 20(3): e1011814. doi:10.1371/journal.pcbi.1011814.
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Spatial Biology Network KCL

Invited to the Kings Spatial Biology Research Conference, 2024

This conference was focused on pilot studied with revolutionary CosMX Nanostring Spatial RNA-Seq data. All research groups, including ours, were working within the KCL Spatial Biology Network. Click the title to see an image of our presentation.

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talks

venture

AI Gold Rush and Investments

Speaker event · Keble College, Oxford · 2026

Is the current wave of AI investment genuine innovation, or the next tech bubble? A discussion at Keble College.

Female Founders & Funders

Speaker panel · Oxford Venture Capital Network · 2026

A panel on the funding disparities female founders face, followed by speed networking between founders and investors.

Pint & Pitch with Oxford Seed Fund Office Hours

Networking · The Lighthouse Pub, Oxford · 2026

An informal evening combining pitch conversations with Oxford Seed Fund office hours, deliberately held in a pub rather than a lecture theatre.

Lumina House Hackathon

Hackathon · Oxford · 2026

An all-female gamified hackathon over two days in Oxford, co-hosted with the Oxford Founders Society, the OX1 Incubator and others.

Co-Founder Matching

Networking · Oxford Tech Week · 2026

Connecting potential co-founders at Oxford Tech Week, on the basis that choosing one is among the most consequential decisions a founder makes.

VC Reverse Pitch

Speaker event · Oxford Venture Capital Network · 2026

Turning the pitch around: leading VC firms presented their investment thesis, sector focus and approach to partnering with early-stage founders, to a curated room of Oxford entrepreneurs.

How are VC Decisions Made?

Speaker event · Oxford Venture Capital Network · 2026

What actually happens behind closed doors when a VC decides to back a startup or pass on it.