The Bioinformatics group focuses on sequencing data quality control (QC) and the development of efficient data processing strategies. Our research includes the systematic evaluation of enrichment kits (e.g. Altmüller et al., Frommolt et al.) as well as the optimization of data analysis through scalable and automated workflows (e.g. Bamborschke et al., Kawalia et al.).In addition, we initiated a variant calling benchmarking challenge within the sequencing centers of the NGS Competence Network (NGS-CN). This initiative resulted in the development of NCbench (Hanssen et al.), a tool that enables standardized benchmarking of SNV and indel callsets from exome or genome sequencing against established reference datasets such as HG001 and HG002.

Publications

Hanssen, F., Gabernet, G., Bauerle, F., Stocker, B., Wiegand, F., Smith, N.H., Mertes, C., Neogi, A.G., Brandhoff, L., Ossowski, A., Altmueller, J., Becker, K., Petzold, A., Sturm, M., Stocker, T., Sivalingam, S., Brand, F., Schmidt, A., Buness, A., Probst, A.J., Motameny, S., and Koster, J. (2023). NCBench: providing an open, reproducible, transparent, adaptable, and continuous benchmark approach for DNA-sequencing-based variant calling. F1000Res 12, 1125. https://www.ncbi.nlm.nih.gov/pubmed/39345270. 

Bamborschke, D., Ozdemir, O., Kreutzer, M., Motameny, S., Thiele, H., Kribs, A., Dotsch, J., Altmuller, J., Nurnberg, P., and Cirak, S. (2021). Ultra-rapid emergency genomic diagnosis of Donahue syndrome in a preterm infant within 17 hours. American journal of medical genetics Part A 185, 90-96. https://www.ncbi.nlm.nih.gov/pubmed/33048476. 

Altmuller, J., Motameny, S., Becker, C., Thiele, H., Chatterjee, S., Wollnik, B., and Nurnberg, P. (2016). A systematic comparison of two new releases of exome sequencing products: the aim of use determines the choice of product. Biol Chem 397, 791-801. https://www.ncbi.nlm.nih.gov/pubmed/27021259. 

Kawalia, A., Motameny, S., Wonczak, S., Thiele, H., Nieroda, L., Jabbari, K., Borowski, S., Sinha, V., Gunia, W., Lang, U., Achter, V., and Nurnberg, P. (2015). Leveraging the power of high performance computing for next generation sequencing data analysis: tricks and twists from a high throughput exome workflow. PLoS ONE 10, e0126321. http://www.ncbi.nlm.nih.gov/pubmed/25942438. 

Frommolt, P., Abdallah, A.T., Altmuller, J., Motameny, S., Thiele, H., Becker, C., Stemshorn, K., Fischer, M., Freilinger, T., and Nurnberg, P. (2012). Assessing the enrichment performance in targeted resequencing experiments. Hum Mutat 33, 635-641. https://www.ncbi.nlm.nih.gov/pubmed/22290614. 


 

To improve structural variant (SV) calling in short read sequencing data and better distinguish between real events and false positive calls, we contributed to the development of the SV MeCa meta caller that combines several standalone SV callers (Nkouamedjo Fankep et al.). A machine learning model considers the annotations provided by the individual callers to assign a score to each consensus variant, providing a means to recognize false positive calls.
We also contributed early on to the analysis of miRNA sequencing data (Motameny et al.).
 

Publications
 
Nkouamedjo Fankep, R.C., Soylev, A., Kobiela, A.L., Blom, J., Ernst, C., and Motameny, S. (2025). SV-MeCa: an XGBoost-based meta-caller approach for structural variant calling from short-read data. BMC Bioinformatics 26, 218. https://www.ncbi.nlm.nih.gov/pubmed/40836322.
  
Motameny, S., Wolters, S., Nurnberg, P., and Schumacher, B. (2010). Next Generation Sequencing of miRNAs - Strategies, Resources and Methods. Genes (Basel) 1, 70-84. https://www.ncbi.nlm.nih.gov/pubmed/24710011.
 

The Cologne Center for Genomics (CCG) maintains a longstanding and close partnership with the IT Center of the University of Cologne (ITCC). This collaboration combines the CCG’s expertise in genomics with the ITCC’s advanced high-performance computing (HPC) infrastructure and know-how. Through this partnership, the CCG leverages powerful computational resources for the analysis and secure storage of next-generation sequencing (NGS) data, while a substantial part of its hardware is hosted and managed by the ITCC. The strength of this collaboration is reflected in several joint initiatives, including successful participation in the DFG-funded call for NGS competence centers—leading to the establishment of the West German Genome Center (WGGC)—as well as in the National Research Data Infrastructure (NFDI) consortium GHGA. A recent highlight is the acquisition of the RAMSES HPC system by the ITCC, which offers unique capabilities for the secure processing of sensitive life science data and further enhances the joint infrastructure. Together, the CCG and ITCC significantly strengthen the University of Cologne’s position as a leading hub for data-driven life science research.

The German Human Genome-Phenome Archive (GHGA) is a consortium of 21 participating institutions that has been funded as one of the first projects of the National Research Data Infrastructure (NFDI). Established in 2020, GHGA offers a FAIR data repository for human genomics data, enabling secondary research and advances in medical research. Together with the ITCC, the CCG is an active contributor to the design, development, and implementation of GHGA infrastructure and services. Furthermore, the CCG supports its users during the submission of data that was produced at the CCG to GHGA by means of two data stewards and an interface inside the NGS-Portal (soon to come).

The NGS Competence Network (NGS-CN) is a cooperation of the four DFG-funded NGS competence centers and that started in 2018. Since then, the NGS-CN promotes NGS in the scientific community by regular meetings, webinars, workshops, and summer schools. With its Special Interest Groups (SIGs), it discusses various aspects associated with NGS like technology development (SIG3), data analysis (SIG4) or data management & protection (SIG5). With the regular exchange of expertise and the capacity to handle also large-scale projects in a collaborative fashion, the NGS-CN has made a strong impact for the life-science community in Germany. 

The BioC2 project aims to interconnect infrastructures and pool competences to make genomic assessment of biodiversity feasible at the scale of entire ecosystems. Funded by the Excellent Research Support Program of the University of Cologne, this project has already collected, sequenced, and assembled the genome of several species. The CCG provides sequencing and expertise in data management to the project.

Publications
Kraege, A., Chavarro-Carrero, E., Schnell, E., Heilmann-Heimbach, S., Becker, K., Kohrer, K., Huettel, B., Sargheini, N., Schiffer, P., Waldvogel, A.M., Thomma, B., and Rovenich, H. (2025). High quality genome assembly and annotation (v1) of the eukaryotic freshwater microalga Coccomyxa elongata SAG 216-3b. G3 (Bethesda) 15. https://www.ncbi.nlm.nih.gov/pubmed/39671565.