Bioinformatician / Statistical genetics / Biostatistics / Transcriptomics / NGS
Salvatore Barbagallo
Prioritising drug targets, from GWAS to single cells.
Bioinformatician working across statistical genetics, biostatistics, single-cell transcriptomics and NGS pipelines, in R, Python and Nextflow. MSc in Bioinformatics; seven years in regulated clinical laboratories, two of them in high-throughput clinical NGS.
- Years in regulated clinical laboratories
- 7
- Clinical NGS samples a week, prepared and QC’d at CooperGenomics
- 500+
- Nuclei re-analysed with donor-aware models
- 83k
- Bioinformatics benchmark tasks authored and accepted
- 106
Featured projects
One Parkinson’s disease gene, three angles
Genetic evidence linking GPNMB to Parkinson’s disease, a pipeline built to run the same analysis across ~2,900 plasma proteins, and the cell types that express the gene.
MSc dissertation · Statistical genetics
GPNMB and Parkinson’s disease: Mendelian randomisation and colocalisation
Two-sample MR, LD-aware sensitivity analysis and Bayesian colocalisation to test whether genetically predicted GPNMB protein levels are linked to Parkinson’s disease risk. The evidence supports prioritisation, not causality.
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Human genetics · Target discovery · Nextflow
Proteome-wide cis-MR and colocalisation scan for Parkinson’s disease
Scaled the dissertation from one protein to a Nextflow pipeline built to test each of the ~2,900 UKB-PPP plasma proteins with cis-pQTL MR and colocalisation, then apply FDR control, evidence tiers and an HTML report.
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Single-nucleus transcriptomics · Parkinson’s disease
GPNMB in Parkinson’s disease: snRNA-seq re-analysis
Re-analysed 83,484 substantia nigra nuclei from 29 donors to localise GPNMB and test PD–control differences with donor-level pseudobulk models. GPNMB was highest in microglia and about two-fold higher in PD microglia (P = 0.009), a nominal effect that did not survive transcriptome-wide FDR.
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Skills
Methods and tools I use most
- Python
- R
- Nextflow
- Docker
- Bash
- SQL
- Mendelian randomisation
- Colocalisation
- Biostatistics
- SuSiE
- GWAS summary statistics
- pQTL / eQTL
- Scanpy
- Pseudobulk DE
- DESeq2
- RNA-seq
- WGS
- Variant calling
- STAR
- Salmon
- BWA-MEM2
- bcftools
- Galaxy
- scikit-learn
- pytest
- Reproducible pipelines
- Audit-ready documentation
Get in touch
I’m looking for bioinformatics roles in statistical genetics, transcriptomics and NGS analysis, especially where clinical or cell and gene therapy experience helps. Based in London, open to hybrid and remote.