About LUMC— Human Genetics— Computational and Functional Genomics
Human Genetics

Computational and Functional Genomics

This LUMC lab develops machine learning and statistical methods for high-throughput genomic data to understand gene expression regulation. Read more.

Our research focuses on the following themes:

I.            Single-Cell Genomics
II.           Imaging-Genetics
III.          Brain Transcriptomics

You can find more information here: https://www.mahfouzlab.org/

Research Themes

Single-Cell Genomics
Imaging-Genetics
Brain Transcriptomics

Single-Cell Genomics

We develop scalable methods to identify cells in single-cell data through interactive visualization, scalable clustering, classification, and interpretable trajectory modelling. We also work on methods to integrate single-cell data across studies and technologies. 

Imaging-Genetics

We develop methods to integrate brain imaging with gene expression data to unravel the molecular mechanisms underlying neuropathologies. We applied our methods to study a wide-range of diseases, such as: Parkinson’s, Alzheimer’s and Cushing. 

Brain Transcriptomics

We extract biological knowledge from spatial and temporal transcriptomic atlases of the brain. We use co-expression analysis to infer functional association between disease-related genes and to decode brain region- and age-specific gene regulatory networks.

Key publications

SCHNEL: Scalable clustering of high dimensional single-cell data
SpaGE: Spatial Gene Enhancement using scRNA-seq
Untangling biological factors influencing trajectory inference from single cell data
Unravelling the complexity of the cancer microenvironment with multidimensional genomic and cytometric technologies
Transcriptomic signatures of brain regional vulnerability to Parkinson’s disease
A comparison of automatic cell identification methods for single-cell RNA-sequencing data
Single-cell RNA sequencing in facioscapulohumeral muscular dystrophy disease etiology and development
How Metabolic State May Regulate Fear: Presence of Metabolic Receptors in the Fear Circuitry
Timing and localization of human dystrophin isoform expression provide insights into the cognitive phenotype of Duchenne muscular dystrophy
Genome-wide coexpression of steroid receptors in the mouse brain: Identifying signaling pathways and functionally coordinated regions

Our Team

Prof.dr.ir. Marcel Reinders
Principal Investigator / Professor Biomedical Data Sciences

Dr. Ahmed M.E.T.A. Mahfouz         
Principal Investigator / Assistant Professor

Mikhael D. Manurung          
Researcher

Lieke C.M. Michielsen          
PhD student

Qirong Mao
PhD student