Robust methylation based classification of brain tumors using nanopore sequencing

Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined. 382 brain tumor tissue samples were submitted to DNA extraction, quality control and library preparation. Subsequently, nanopore whole genome sequencing was performed. Samples were randomly divided into a discovery and validation cohort in order to establish and validate an implemented classification score and the resulting cut-off values. ***The raw data will be uploaded to EGA due to data privacy and local legislation***