Senior Bioinformatician | Multi-Omics Analysis & Workflow Engineering

Daniel Mouzo

I work across cancer genomics, gene therapy, proteomics, and single-cell biology, building reproducible workflows that turn complex omics data into clear, useful answers.

Clear analysis. Reproducible workflows. Results teams can use.

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10+ Years Experience
10+ Key Projects
4 Countries
13+ Publications
30+ People Coordinated
3 Courses Taught

Experience

2025 — Present Barts Cancer Institute · Queen Mary University

Postdoctoral Bioinformatician

Engineered production-grade Nextflow workflows for 20+ TB of Genomics England WGS data within a secure clinical research environment.

Automated clinical reporting workflows integrating variant calls and participant metadata, reducing turnaround to under 3 hours.

2021 — 2025 Navarrabiomed Biomedical Research Center

Postdoctoral Bioinformatician

Led bioinformatics delivery for a €4M European work package, coordinating 30+ researchers and partner sites across gene therapy and oncology projects.

Built single-cell and NGS workflows, standardizing QC and cross-site harmonization for translational and clinical-stage studies.

Administered and optimized a 7-node HPC cluster, supporting reproducible multi-user analysis with 99% compute availability.

2025 Ospedale San Raffaele · SR-Tiget

Visiting Scientist · Gene Therapy

Harmonized scMultiome datasets across XPAND clinical sites to support cross-centre comparison and downstream translational analysis.

Validated LT-HSC signatures in AML-derived cells, linking multi-omic profiles to clinically relevant cell-state interpretation.

2023 KAUST University

Visiting Scientist · Workflow & Data Systems

Designed Snakemake preprocessing workflows for large-scale NGS datasets, reducing turnaround time and standardizing execution.

Built relational databases to centralize experimental metadata and strengthen data integrity across collaborative projects.

2015 — 2020 University of Santiago de Compostela

Research Assistant & PhD Candidate

Optimized LC-MS and quantitative proteomics workflows, reducing turnaround time by 1.5 days and increasing sample throughput.

Developed traceability analytics for industrial quality control, translating experimental data into cost-saving decisions.

Projects

RR771 project illustration

RR771 - Genomics England

Cancer genomics WGS Workflow

Secure WGS workflow for germline predisposition analysis in AML, built to support fast and interpretable reporting in the Genomics England environment.

GitHub
scAnnex project illustration

scAnnex

Single-cell Workflow Dashboard

Automated single-cell workflow that takes raw counts to interpretable results through reproducible analysis and a simple interactive dashboard.

GitHub
bulkAnnex project illustration

bulkAnnex

Bulk RNA-seq Workflow Dashboard

Reproducible bulk RNA-seq workflow for differential expression, pathway analysis, and self-contained reporting.

GitHub
XPAND project illustration

XPAND

SC multiomics Gene therapy GRN

Integrated single-cell multiomics analysis for HSC gene therapy, focused on marker discovery and translational interpretation.

GitHub Web
GeneSetCluster project illustration

GeneSetCluster 2.0

R/Shiny GSEA Open source

Open-source toolkit for clustering and comparing enrichment results across datasets, tools, and databases.

GitHub Shiny Paper
Open source packages illustration

scNextPlot - R package

R package Single-cell Visualization

Open-source R package for clean single-cell visualizations and faster, more consistent figure generation.

GitHub
Endocarditis project illustration

Endocarditis

Dual RNA Host-pathogen Cardiology

Dual RNA-seq study of infective endocarditis, profiling host and pathogen responses to understand disease biology from both sides.

GitHub Paper
BioHackathon 2026 project illustration

ELN Intelligence Layer

Product Traceability React

Hackathon prototype for adding traceability and context to electronic lab notebooks without changing existing workflows.

GitHub
B-ALL regulatory landscape project illustration

B-ALL Regulatory Landscape

scRNA/scATAC GRN Leukemia

Multiomic analysis of regulatory programs across early B-cell development and leukemia-associated states in B-ALL.

GitHub Shiny Paper
Toolbox
R R
Python Python
Bash Bash
Markdown Markdown
Nextflow Nextflow
Snakemake Snakemake
Docker Docker
Singularity Singularity
Conda Conda
Linux Linux
Windows Windows
AWS AWS
Git Git
GitHub GitHub
Shiny Shiny
Notion Notion
VS Code VS Code
RStudio RStudio
R R
Python Python
Bash Bash
Markdown Markdown
Nextflow Nextflow
Snakemake Snakemake
Docker Docker
Singularity Singularity
Conda Conda
Linux Linux
Windows Windows
AWS AWS
Git Git
GitHub GitHub
Shiny Shiny
Notion Notion
VS Code VS Code
RStudio RStudio

About

I'm Daniel

I started in the wet lab, working in proteomics and seeing first-hand how much biological value can stay hidden inside complex datasets. That was the turning point that pulled me into bioinformatics. Since then, I have spent more than a decade across genomics and computational biology, building a profile that combines scientific intuition with the discipline to make analysis reproducible, scalable, and genuinely useful.

Today, I develop bioinformatic solutions for translational and biomedical research across multi-omics, gene therapy, cancer genomics, and workflow engineering. I build in Nextflow and Snakemake, support HPC environments, and work with researchers, clinicians, and technical teams to turn raw data into decisions and usable workflows.

In industry, good bioinformatics is not just about getting analyses to run. It is about building work that is reliable, maintainable, and scalable when quality and clinical impact matter. That is why traceability is important to me, and why ISO 13485 internal auditor certification and IEC 62304-informed practices already shape how I approach development.

Daniel Mouzo

Certificates

  • ISO 13485 Internal Auditor
  • Nextflow Advanced (Seqera)
  • AWS Cloud Practitioner Certified (AWS)
  • Project Management (Google)
  • Machine Learning & Big Data for Bioinformatics (UGR)
  • Cybersecurity (Cámara de Comercio)
  • Data Science Specialization (IBM)

Education

  • Ph.D. Molecular Biology (USC)
  • MD in Innov. in Food Safety and Tech. (USC)
  • Degree in Biology (USC)

Contact

Good bioinformatics should be clear,
reproducible, and useful.

If that is what your team needs, I would be glad to talk.

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