Research

My work sits between biology, statistics and computer science. Most of it is collaborative: as a data scientist in a national infrastructure, I work with groups across Sweden, from veterinary medicine to evolutionary biology to clinical research.

Themes

Statistical and complex-trait genomics

Mixed-model and gene-based approaches for finding the variants behind disease and quantitative traits, including rare variants and missing heritability. Examples include chronic kidney disease in boxers, atopic dermatitis in German shepherds, pyometra in golden retrievers and plasma protein levels in humans.

Conservation and ancient genomics

Population genomics of endangered and extinct mammals, such as the Sumatran and woolly rhinoceros, and the pipeline GenErode for quantifying genome erosion.

Cancer genomics

Somatic variation in canine osteosarcoma (SETD2) and structural variants in multiple myeloma resolved with linked-read sequencing.

Machine learning for biology

Ensemble learning for protein–protein interaction prediction, rough-set classifiers for HIV-1 drug resistance, and modern neural-network methods for omics data.

Metagenomics and metabarcoding

DNA metabarcoding for authenticating food products, and genome assembly from single nuclei of complex microorganisms.

Species I have worked with

From domestic animals and wildlife to humans, microbes and plants:

Dog Human Rhinoceros Chicken Bumblebee Bacteria Plants HIV-1

Collaboration network

Co-authors of two or more papers, the institutions they mostly work at, my research themes, and the people around RaukR. Choose a view to focus on one kind of connection. Hover or focus a node to see its connections, and drag to rearrange.

Tools & languages

Day-to-day, I work with:

R Python Nextflow Snakemake HPC environments Reproducible research

Open source on GitHub