Research infrastructure & Expertise
Expert Service Centre Next-Generation Sequencing (ESC-NGS)
Dept. Translational Genomics (TGX) – Institute MHeNS – FHML
The Expert Service Centre Next-Generation-Sequencing (ESC-NGS) integrates genomics and bioinformatics expertise with a fully equipped state-of-the-art infrastructure for NGS driven research at Maastricht University.
NGS has been the core-activity for the Dept. Translational Genomics (TGX) since 2009. It was and is a key driving force in genomics innovations and research across the UM and MUMC. This activity has evolved into a central service facility, called the (ESC-NGS), supported by TGX, MHeNs, and FHML. The infrastructure is Illumina-based and matched with a brand-new high performance computer cluster. Extensive, up-to-date expertise is available for RNA- and DNA-NGS applications, including both laboratory procedures and bioinformatics pipelines.
The ESC-NGS has a proven track record of the main transcriptomics methodologies (PolyA-enriched, Ribo-depleted and microRNAs RNA-sequencing) from a diversity of sources, like tissues, cell-lines, serum/plasma, single cells and exosomes. DNA-Seq has mainly focused on Whole-Exome Sequencing (WES), Whole-Genome Sequencing (WGS), and Mitochondrial DNA Sequencing, covering humans, animal models and the microbiome. Standardized bioinformatics pipelines have been developed for these applications or can be developed custom-made.
The service level of the ESC-NGS varies from Plug and Play (“the complete project perfomed by ESC-NGS)”) to joint projects (“part of a project perfomed by the ESC-NGS”) or use of the NGS-equipment only. For every project a tailor-made quotation is provided. This covers the net costs of the kits, a contribution to the man-hours and a small service charge, which is an insurance to be able to repeat the experiment, in case something unexpected goes wrong. Projects can be discussed with the ESC-NGS team, prior to the start of the experiments, to safeguard the best experimental design and technical choices within the available budget and make sure that the data will contribute to solving the underlying research questions.
