Jürgen Hench, Claus Hultschig, Jon Brugger, Luigi Mariani, Raphael Guzman, Jehuda Soleman, Severina Leu, Miles Benton, Irenäus Maria Stec, Ivana Bratic Hench, Per Hoffmann, Patrick Harter, Katharina J Weber, Anne Albers, Christian Thomas, Martin Hasselblatt, Ulrich Schüller, Lisa Restelli, David Capper, Ekkehard Hewer, Joachim Diebold, Danijela Kolenc, Ulf C Schneider, Elisabeth Rushing, Rosa Della Monica, Lorenzo Chiariotti, Martin Sill, Daniel Schrimpf, Andreas von Deimling, Felix Sahm, Christian Kölsche, Markus Tolnay, Stephan Frank
DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data...
April 4, 2024: Acta Neuropathologica Communications