DIANA: An integrated pipeline for analysis of long-read whole-genome sequencing data for molecular neuropathology

2026 · doi:10.64898/2026.03.25.714119
preprint OA: closed CC-BY-4.0
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The paper presents DIANA, an open-source, fully automated Nextflow pipeline for integrated analysis of long-read whole-genome sequencing data (Oxford Nanopore and PacBio) for molecular profiling in CNS tumors. DIANA combines outputs from multiple analytic components—including DNA methylation classification, copy-number variant detection, gene fusion analysis, small variant calling, and MGMT promoter methylation status—into a single human-readable report. The primary caveat stated is that a supporting document was missing in the submitted version. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

ABSTRACT Summary Central nervous system (CNS) tumor diagnosis requires comprehensive genomic profiling including DNA-methylation classification, copy-number variants (CNV), gene fusion analysis, small variant detection and MGMT promoter methylation status. Long-read sequencing platforms such as nanopore sequencing by Oxford Nanopore Technologies and SMRTseq by PacBio can capture all these in a single assay, but integrating diverse analytical tools to leverage the advantages of long-read sequencing remains complex. We present DIANA ( D iagnostic I ntegrated A nalytics of N eoplastic A lterations ) , a pipeline providing fully automated end-to-end processing of long-read whole-genome sequencing data from aligned sequence reads. DIANA produces a human readable report that combines methylation classification with prioritized genetic variants to support CNS tumor diagnostics and clinical decision-making. Availability and implementation DIANA is an open-source Nextflow pipeline, freely available through Docker or Apptainer/Singularity technologies. The source code, comprehensive documentation, and installation protocols are available on GitHub: https://github.com/VilhelmMagnusLab/DIANA.git . Supplementary information Supplementary data are available at Bioinformatics online.
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ABSTRACT Summary Central nervous system (CNS) tumor diagnosis requires comprehensive genomic profiling including DNA-methylation classification, copy-number variants (CNV), gene fusion analysis, small variant detection and MGMT promoter methylation status. Long-read sequencing platforms such as nanopore sequencing by Oxford Nanopore Technologies and SMRTseq by PacBio can capture all these in a single assay, but integrating diverse analytical tools to leverage the advantages of long-read sequencing remains complex. We present DIANA (Diagnostic Integrated Analytics of Neoplastic Alterations), a pipeline providing fully automated end-to-end processing of long-read whole-genome sequencing data from aligned sequence reads. DIANA produces a human readable report that combines methylation classification with prioritized genetic variants to support CNS tumor diagnostics and clinical decision-making. Availability and implementation DIANA is an open-source Nextflow pipeline, freely available through Docker or Apptainer/Singularity technologies. The source code, comprehensive documentation, and installation protocols are available on GitHub: https://github.com/VilhelmMagnusLab/DIANA.git. Supplementary information Supplementary data are available at Bioinformatics online. Competing Interest Statement HL, EOVM and SH received travel reimbursements from Oxford Nanopore Technologies to present at conferences in 2023-2025. Footnotes In the submitted version a supporting document 1 file was missing.

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License: CC-BY-4.0