New high-throughput technologies and, above all, next generation sequencing (NGS) technologies as whole-genome, whole-exome and „Mendeliome“ sequencing have made it easier to discover disease-associated genes and disease-causing mutations. Our MutationMining (MM) Team searches the data generated by NGS-based analyses to detect novel causative genes and mutations in patients with various rare genetic diseases and undiagnosed congenital syndromes.
The MutationMining strategy relies on an innovative bioinformatics pipeline and a multidisciplinary team of clinicians and researchers who collaboratively analyze the data and interpret the relevance of identified variants. This has proven to be a successful approach, leading to the identification of numerous disease-associated genes. In a first national pilot study on the use of “Mendeliome” sequencing (i.e. the NGS-based analysis of 4813 genes) in children with unclear congenital malformation syndromes, we put the MM team strategy into action and were able to establish a molecular diagnosis in 55% of cases (Moosa et al; submitted for publication).
The MM team also aims to establish sustainable clinical and bioinformatics strategies for interpretation of potentially causative variants in whole-genome/exome sequencing, to define standardized procedures and to assure quality standards. Additionally, we work on developing structured and standardized diagnostic algorithms (indication criteria) to perform NGS-based whole-genome/exome sequencing in children and adults with an unclear diagnosis.
MM Team Members
Ibrahim Adham, Janine Altmüller [CCG Köln], Loukas Argyriou, Tabea Beyer, Nina Bögershausen, Karin Boß, Peter Burfeind, Silke Kaulfuß, Alexandr Kuranov, Yun Li, Helena Lindemann, Carolina Martinez, Shahida Moosa, Christian Müller, Christiane Neuhofer, Silke Pauli, Inka Praulich, Nadine Rosin, Julia Schmidt, Franziska Schnabel, Mateja Smogavec, Lukasz Smorag, Leonie Thiele, Alicja Turotszy, Roser Mas Ufartes, Gesa Werner, Bernd Wollnik, Gökhan Yigit, Arne Zibat
Novel disease-associated genes identified (selected)
ANO6, BRD3, KNTC1, NARF, MESDC2, PPM1D, RMI1, SEC24D, SMCHD1, SUZ12, TRAIP