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This document describes a Python-based workflow designed to detect copy number variations (CNVs) in Oxford Nanopore Technologies (ONT) amplicon sequencing data. The method is optimized for small targeted regions, such as a single gene amplicon split into multiple sub-targets, where standard CNV callers are often unsuitable due to the limited number of genomic intervals and the strong influence of PCR/amplicon coverage bias.

The workflow compares each sample against a negative-control and applies an additional intra-sample normalization step across all BED-defined targets.

The final output is an Excel report containing both high-confidence CNV calls and target-level coverage metrics.