Open to opportunities

Ane Martínez Larrinaga

@anemartnezlarrinaga

Message

I build scalable translational bioinformatics workflows for cancer biomarker discovery.

Spain

Message

What I'm looking for

I'm interested in translational oncology roles where I can apply machine learning, biomarker evaluation, and scalable omics analysis to advance precision medicine.

At Josep Carreras Leukaemia Research Institute, I lead bioinformatics for translational oncology projects spanning large-scale transcriptomic and single-cell datasets, including more than 5 million cells and 900 patient samples.

I've developed reproducible R and Python pipelines in SLURM-based HPC environments, coordinated Snakemake workflows that reduced processing time by approximately 30%, and established QC and data-integration practices across concurrent research programs.

My PhD work identified the 53-gene Tumor-Associated Pericyte Signature associated with adverse clinical outcome and produced the first pan-cancer single-cell atlas of the mural cell compartment, integrating approximately 4 million cells across nine cancer types.

I work closely with clinicians and experimental researchers to turn high-dimensional clinical and omics data into biomarker evidence, molecular programs, and actionable translational insights.

Experience

Work history, roles, and key accomplishments

Current

Postdoctoral Researcher & Bioinformatics Lead

Current

Josep Carreras Leukaemia Research Institute

Jan 2026 - Present (7 months)

Defined analytical strategies and decision-making frameworks across multiple translational oncology projects involving large-scale transcriptomic and single-cell datasets. Established standardized QC, reproducibility and data integration practices, and coordinated scalable computational infrastructures in HPC environments.

Bioinformatics Data Integration HPC SnakeMake Slurm

Ph.D. Candidate (Bioinformatics)

Josep Carreras Leukaemia Research Institute

Jan 2022 - Dec 2025 (3 years 11 months)

Performed large-scale transcriptomic and single-cell analyses across >5M cells and 900+ patient samples to identify disease-associated molecular programs. Developed scalable analytical pipelines in R and Python within HPC environments, and applied survival analysis and machine learning to identify biomarkers.