Senior computational biologist working at the intersection of systems biology, transcriptomics, and drug discovery.
I design and implement reproducible computational workflows for multi-omics integration, network inference, and mechanism-driven data interpretation. My work focuses on extracting robust biological signal from high-dimensional datasets and translating systems-level insights into drug prioritisation and biomarker strategies.
I am a licensed pharmacist in Finland with clinical and pharmacological training that informs my systems pharmacology and translational modelling work. I also bring a background in evolutionary population ecology, shaping my perspective on biological variation and selection.
Reproducible benchmark for single-cell multiome peak–gene link scoring (Docker + Snakemake + Python CLI wrapper):
- Paired scRNA + scATAC processing (Seurat/Signac), LinkPeaks candidate generation
- Eleven interpretable score modes over one fixed candidate universe
- SCENT as an external comparator, chromosome-sharded across 22 autosomes
- Explicit proximity controls: distance-only baseline, distance-matched stratification, proximal-removal thresholds
- Containerized image on GHCR, Zenodo-archived, verified by a clean-clone rerun
Reports a partly negative result: apparent ranking gains are substantially proximity artifacts, and the benchmark is framed as a diagnostic rather than a method.
Repository: https://github.com/Inkasimo/multiome-link-ranking DOI: https://doi.org/10.5281/zenodo.22032459
Production-style RNA-seq workflow (Docker + Snakemake + explicit Python CLI wrapper) demonstrating:
- FASTQ-level QC and reference preparation
- STARsolo alignment
- Donor-aware differential analysis (DESeq2 + TOST)
- Co-expression and network-based downstream analysis
- Explicit execution control via wrapper interface
- Fully containerized, reproducible execution
Repository:
https://github.com/inkasimo/scRNAseq-pbmc-workflow
Inkala, S., Fratello, M., del Giudice, G. et al. MUUMI: an R package for statistical and network-based meta-analysis for multi-omics data integration. BMC Bioinformatics (2026). https://doi.org/10.1186/s12859-026-06394-3
Repository: https://github.com/fhaive/muumi
- Systems biology and network-based modelling
- Multi-omics data integration and transcriptomics
- Single-cell transcriptomics and multiome (scRNA + scATAC) analysis
- Systems pharmacology and mechanism driven drug discovery
- Reproducible computational workflows (Snakemake, Docker, HPC)
- Benchmarking, confound control and validation strategy