Résumé for Research

Note

This format focuses on contributions and impact, rather than job titles or publication counts. Wherever possible, link to evidence that supports your claims.

This Résumé for Research follows a narrative CV format commonly requested by research funders and institutions.

Contributions are grouped into four modules describing how I contribute to research, people, the research community, and broader society.

1. Contributions to the generation of knowledge

Genetic analysis of Alzheimer’s disease risk

  • Contribution: Developing computational approaches to investigate how genetic variation contributes to Alzheimer’s disease risk, with particular focus on structural variation and complement system genes.
  • Significance: These analyses aim to improve understanding of genetic mechanisms underlying neurodegeneration, particularly in complex genomic regions that are difficult to resolve with traditional sequencing methods.
  • Evidence: Long-read sequencing and statistical genetics analyses conducted at the UK Dementia Research Institute.

Multi-omics analysis of immune cell metabolism

  • Contribution: Led integrated analysis of transcriptomics and lipidomics datasets to investigate how the transcription factor GATA6 regulates macrophage lipid metabolism.
  • Significance: This work improved understanding of how immune cell metabolism influences inflammatory responses.
  • Evidence: First-author and collaborative publications including Cellular and Molecular Life Sciences and Bioinformatics.

Development of bioinformatics tools and analytical workflows

  • Contribution: Contributed to the development of LipidFinder 2.0, a bioinformatics pipeline for lipidomics data processing and analysis.
  • Significance: The tool supports reproducible and scalable lipidomics analysis for researchers working with mass spectrometry data.
  • Evidence: Alvarez-Jarreta et al., Bioinformatics.

2. Contributions to the development of individuals

Mentoring and supervision in computational biology

  • Contribution: Supervised and mentored undergraduate and postgraduate students in bioinformatics and omics data analysis.
  • Who benefited: Students developing computational skills in genomics and multi-omics research.
  • Evidence: Supervision of BSc, MSc and PTY research projects.

Training researchers in data analysis and reproducible workflows

  • Contribution: Provided guidance to colleagues on analysis workflows, data management, and interpretation of omics datasets.
  • Who benefited: Researchers working across genomics, immunology, and clinical research projects.
  • Evidence: Collaborative projects and internal training activities.

Supporting onboarding and training in bioinformatics

  • Contribution: Contributing to the development of onboarding resources and documentation for bioinformatics researchers within the UK Dementia Research Institute.
  • Who benefited: New researchers joining computational biology projects across the institute.
  • Evidence: Development of training materials and documentation frameworks.

3. Contributions to the wider research community

Promoting open and reproducible research practices

  • Contribution: Developing workflows and documentation that support transparent and reproducible computational research using GitHub and modern documentation tools.
  • Reach: These practices facilitate collaboration and reuse of analytical methods across research groups.
  • Evidence: Public repositories and shared analysis resources.

Community building in computational biology

  • Contribution: Co-founded the Cardiff University Bioinformatics Club, creating a forum for researchers to share methods and collaborate across disciplines.
  • Reach: Researchers across multiple departments and career stages.
  • Evidence: Regular meetings and collaborative exchanges within the community.

Research community service

  • Contribution: Participated in organising scientific meetings and contributing to peer review activities.
  • Reach: Supports collaboration and knowledge exchange within the biomedical research community.
  • Evidence: Infection and Immunity Annual Meeting organising committee (2021).

4. Contributions to broader society

Translational research in complex diseases

  • Contribution: Research investigating genetic and molecular mechanisms underlying diseases including sepsis and Alzheimer’s disease.
  • Impact: Improved understanding of disease biology contributes to the long-term development of better diagnostics and therapeutic strategies.
  • Evidence: Collaborative research projects and publications.

Public engagement and community involvement

  • Contribution: Participation in public engagement activities related to biomedical research and health awareness.
  • Impact: Supports communication of scientific research to wider audiences and promotes awareness of health challenges.
  • Evidence: Engagement in outreach and community initiatives.

Supporting open science and data sharing

  • Contribution: Promoting FAIR data principles and reproducible analysis practices.
  • Impact: Enables broader access to research outputs and improves transparency in biomedical research.
  • Evidence: Use of open repositories and shared research resources.