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