TY  - JOUR
AU  - Hench, Jürgen
AU  - Hultschig, Claus
AU  - Brugger, Jon
AU  - Mariani, Luigi
AU  - Guzman, Raphael
AU  - Soleman, Jehuda
AU  - Leu, Severina
AU  - Benton, Miles
AU  - Stec, Irenäus Maria
AU  - Hench, Ivana Bratic
AU  - Hoffmann, Per
AU  - Harter, Patrick
AU  - Weber, Katharina
AU  - Albers, Anne
AU  - Thomas, Christian
AU  - Hasselblatt, Martin
AU  - Schüller, Ulrich
AU  - Restelli, Lisa
AU  - Capper, David
AU  - Hewer, Ekkehard
AU  - Diebold, Joachim
AU  - Kolenc, Danijela
AU  - Schneider, Ulf C
AU  - Rushing, Elisabeth
AU  - Della Monica, Rosa
AU  - Chiariotti, Lorenzo
AU  - Sill, Martin
AU  - Schrimpf, Daniel
AU  - von Deimling, Andreas
AU  - Sahm, Felix
AU  - Kölsche, Christian
AU  - Tolnay, Markus
AU  - Frank, Stephan
TI  - EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.
JO  - Acta Neuropathologica Communications
VL  - 12
IS  - 1
SN  - 2051-5960
CY  - London
PB  - Biomed Central
M1  - DKFZ-2024-00688
SP  - 51
PY  - 2024
AB  - 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. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.
KW  - Artificial intelligence (Other)
KW  - Copy number profiling (Other)
KW  - Cryptocurrency miner (Other)
KW  - Digital pathology (Other)
KW  - Dimension reduction (Other)
KW  - Edge computing (Other)
KW  - Epigenetics (Other)
KW  - Intraoperative (Other)
KW  - Methylation (Other)
KW  - Methylation sequencing (Other)
KW  - Methylome (Other)
KW  - Microarray (Other)
KW  - Nanopore sequencing (Other)
KW  - Oncology (Other)
KW  - Same-day classification (Other)
KW  - SoC (Other)
KW  - Tumour (Other)
KW  - UMAP (Other)
KW  - Unsupervised machine learning (Other)
KW  - gpGPU (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:38576030
C2  - pmc:PMC10993614
DO  - DOI:10.1186/s40478-024-01759-2
UR  - https://inrepo02.dkfz.de/record/289293
ER  -