Molecular Technology and Informatics for Personalised Medicine and Health

The research within our LUMC departments is conducted within departmental research programmes. The research programme below is embedded within the department of Human Genetics.

Aim and focus

Research program 50106 aims to better understand genotype - phenotype relationships in humans and in disease models, and to predict predisposition to and progression of disease by means of bio-M.I.S. (molecular, informatic, and semantic) approaches. We see increasing opportunities for data-driven research. These extend beyond conventional research, diagnosis, screening and therapy methods, and include innovative -omics technologies (genomics, epigenomics, transcriptomics, proteomics and metabolomics), and bioinformatic and biosemantic analyses. We focus on the use of next-generation sequencing (NGS) technology followed by integrative semantic data analysis to unravel unsolved genetic diseases; to study the regulation of the genomic organisation and gene expression; to identify the genetic basis of genome instability, to understand genome evolution and population differentiation; and on the use of ‘In silico Knowledge Discovery’ tools to support the above strategies.

Position in international context

The department of Human Genetics (HG) has a strong International track record in the development and implementation of innovative technology. Examples include the next generation sequencing technology, single molecule sequencing, single cell analysis, epigenetic profiling, single-chain high-affinity antibody proteomics technology, exon skipping, biosemantic analysis, and FAIR (Findable, Accessible, Interoperable and Reusable) data principles, often developed in collaboration with other LUMC departments or outside partners, public and private. The department is seen as one of the leaders in Europe in innovative methods for life science data management and discovery of new biomedical knowledge through linked data approaches. Within the European life science data infrastructure ELIXIR, it provided the national ‘head-of-node’ and the co-lead for the ELIXIR interoperability platform, and is co-leading the rare disease work package. It is the initiator of the, now globally endorsed (including the G20 and G7), FAIR data principles. The department is a founding partner in many nationally funded research and translational activities such as the Center for Medical Systems Biology (CMSB), the Forensic Genomics consortium Netherlands (FGCN), Center for Genome Diagnostics (CGD), and the Biobanking and Biomolecular Research Infrastructure (BBMRI-NL), the Medical Genetics Center Southwest Netherlands (MGC), Open PHACTS, ELIXIR, the European Open Science Cloud, GO FAIR, and is a main participant in the Dutch Techcentre for Lifesciences (DTL) and ELIXIR-NL. The department is a key participant in several national and international projects (infrastructures) in the field of gene variant databases (LOVD platform), biobanking (BBMRI-NL, BBMRI-ADOPT), rare diseases (RD-Connect, RD Action, European Reference Networks for rare diseases), drug discovery (Open PHACTS), standards for digital research practice (Wf4ever, ELIXIR, NWO-ODEX4All, Personal Health Train, rare disease linked data and ontology task force), and has public private partnerships with several companies (e.g. Biomarin, Ionis, Facio Therapies, GenomeScan, EURETOS).

Content / highlights / achievements

  • Use of NGS for the discovery of causative disease mutations in novel genes (KFSD, TOD, ICF2-4, FSHD2). Development of clinical diagnostic applications of NGS to unravel the cause of unknown genetic diseases.
  • Translation of innovative tools and analysis infrastructure from the research to the diagnostic setting
  • Implementation of NGS (incl. single molecule and single cell sequencing) for gene expression analysis, for the study of transcriptional and translational regulation, and for unrevealing and studying the mechanisms underlying genome instability and evolution.
  • Collection of clinically relevant gene variants through web-based gene variant databases, incl. the development and implementation of the LOVD and Mutalyzer software, currently setting the international standard.
  • Validation of biosemantics approaches for prediction of biological relationships such as protein-protein interactions and disease-gene associations, from the literature and biological databases.
  • Establishment of standards for FAIR data stewardship for enabling distributed access to interoperable, human & machine-readable data for analysis of health and life science data  (with ELIXIR and European Science Cloud in Europe and Force11 and NIH in the USA, and the World Economic Forum), including a leading international role in the rare disease community (EU networks of health care expert centres, patient registries, biobanks, omics data) exemplified by the recent recognition of FAIR guiding principles by the International Rare Disease Research Consortium (IRDiRC).
  • Development of new highly sensitive NGS-based research protocols for analysing complex mixed DNA samples (e.g. forensic samples and bone-marrow-transplantation remission studies).
  • Leading role in the education of medical and biomedical students in (modern) genetics, including the establishment of the minor Genome-based Medicine.
  • The organisation of a highly successful series of expert courses for basic and clinical researchers on next generation sequencing data analysis and programming.
  • Disseminations activities towards general physicians, high school students, and the general public, to increase awareness of the relevance of genome knowledge for disease prevention and treatment (

Future themes

  • Our long term aim, in concert with and supporting the other research programmes within HG, is to contribute to the implementation of "genome-based medicine" in the hospital, facilitating both cost-effective high quality data generation (sequencing, FAIR standards), as well as - more importantly - integrative analysis of FAIR data, ultimately resulting in a report describing a 'SWOT' analysis of the patient's genome with diagnostic, preventive measures and implications for treatment.
  • The advance in genetics is reflected by increased technological automation, throughput increase, workload reduction and upscaling of molecular technologies (such as NGS), thus allowing greater emphasis on development and readout of cell-based and animal in vivo technologies and on bioinformatics, biostatistical and population-based approaches. We will actively pursue this route, with the further implementation of novel, high-throughput technologies.
  • We focus on increased reproducibility, automation and standardisation (good research practice – GRP - in the wetlab but also in the data analysis routines), workload reduction, integration and upscaling of molecular technologies for early detection and monitoring treatment success (biomarkers).
  • This coincides with the development of cell-based and animal in vivo technologies and bioinformatics, biostatistical and population-based approaches to test and predict the effect of gene variants detected.
  • In order to better understand current disease risks we reconstruct the Dutch-genetic (risk) landscape of the past 7000 years using ancient DNA and NGS approaches. 
  • Development of advanced FAIR data analytics for elucidation of the mechanisms of human genetic diseases and supporting translational research, realized by large-scale semantic analysis of distributed FAIR data encompassing all types of health data including all omics, clinical, preclinical and life science reference data, with a particular focus on methods for sparse rare disease data.
  • Development of FAIR data services to establish the concept of the personal health train (, where analysis is brought to the data and where individuals can give access to their personal data to researchers and health care providers.
  • Fostering adoption and exploitation of FAIR data principles and services in the LUMC to support LUMC (pre)clinical and translational research, and in international communities to source FAIR data for research on human genetic diseases.

Cohesion within LUMC

For the development and implementation of novel genomics and transcriptomics technologies, single molecule and single cell sequencing, the LGTC, in collaboration with GenomeScan, will further strengthen their position by providing service to other LUMC departments, groups within Leiden University and external parties. For the routine application of NGS, the close collaboration with the department of Clinical Genetics will be continued. Furthermore, we will continue our close collaboration with the Transgenic Facility - TFL, for the analysis of disease genes in animal models, again thereby providing service to LUMC and external parties. For the growing demand of developing and implementing biosemantic and bioinformatic approaches (analysis pipelines, workflows) for high-throughput data analysis this research line has a key role, in close collaboration with LUMC’s Sequence Analysis Support Core (SASC), and LUMC’s Bioinformatics Center or Expertise (BCE), BBMRI-NL, and DTL. We are also founder (together with Medical Statistics & Bioinformatics) of the Technology Focus Area (TFA) ‘Data Analytics’. Members of this research line actively contribute to (1) the professionalization of LUMC’s research infrastructure and support through the ResearchICT program, (2) the proteomics and metabolomics in collaboration with Center for Biomolecular Mass Spectrometry and NMC, (3) the Leiden Network for Personalised Therapeutics (LNPT), and (4) the European Reference Networks for rare diseases, including the LUMC-led ENDO-ERN (Endocrine rare diseases). Finally, we contribute to Generade, the new center of expertise for applied genomics between Leiden University, Hogeschool Leiden, Naturalis, Baseclear, and LUMC on the study of applied genomics in the fields of biodiversity and health, and to teaching of Bioinformatics program at the Hogeschool Leiden.