Robust methylation based classification of brain tumors using nanopore sequencing. 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. Overall design: 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***