EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.

Artificial intelligence Copy number profiling Cryptocurrency miner Digital pathology Dimension reduction Edge computing Epigenetics Intraoperative Methylation Methylation sequencing Methylome Microarray Nanopore sequencing Oncology Same-day classification SoC Tumour UMAP Unsupervised machine learning gpGPU

Journal

Acta neuropathologica communications

ISSN: 2051-5960

Titre abrégé: Acta Neuropathol Commun

Pays: England

ID NLM: 101610673

Informations de publication

Date de publication:
04 Apr 2024

Historique:

received: 25 11 2023

accepted: 11 03 2024

medline: 5 4 2024

pubmed: 5 4 2024

entrez: 4 4 2024

Statut: epublish

Résumé

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.

Identifiants

DOI: 10.1186/s40478-024-01759-2 PMID: 38576030

pubmed: 38576030

doi: 10.1186/s40478-024-01759-2

pii: 10.1186/s40478-024-01759-2

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

Informations de copyright

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