EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics
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...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2024
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| In: |
Acta Neuropathologica Communications
Year: 2024, Jahrgang: 12, Pages: [1]-16 |
| ISSN: | 2051-5960 |
| DOI: | 10.1186/s40478-024-01759-2 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s40478-024-01759-2
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| Verfasserangaben: | 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 |
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| 245 | 1 | 0 | |a EpiDiP/NanoDiP |b a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics |c 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 |
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| 520 | |a 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. | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Copy number profiling | |
| 650 | 4 | |a Cryptocurrency miner | |
| 650 | 4 | |a Digital pathology | |
| 650 | 4 | |a Dimension reduction | |
| 650 | 4 | |a Edge computing | |
| 650 | 4 | |a Epigenetics | |
| 650 | 4 | |a gpGPU | |
| 650 | 4 | |a Intraoperative | |
| 650 | 4 | |a Methylation | |
| 650 | 4 | |a Methylation sequencing | |
| 650 | 4 | |a Methylome | |
| 650 | 4 | |a Microarray | |
| 650 | 4 | |a Nanopore sequencing | |
| 650 | 4 | |a Oncology | |
| 650 | 4 | |a Same-day classification | |
| 650 | 4 | |a SoC | |
| 650 | 4 | |a Tumour | |
| 650 | 4 | |a UMAP | |
| 650 | 4 | |a Unsupervised machine learning | |
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| 700 | 1 | |a Benton, Miles |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Albers, Anne |e VerfasserIn |4 aut | |
| 700 | 1 | |a Thomas, Christian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Hasselblatt, Martin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Schüller, Ulrich |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Kölsche, Christian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Tolnay, Markus |e VerfasserIn |4 aut | |
| 700 | 1 | |a Frank, Stephan |e VerfasserIn |4 aut | |
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