Minerva：利用长读长测序实现高分辨率等位基因特异性拷贝数与复杂结构变异的协调整合

Copy-number alterations (CNAs) and structural variations (SVs) are major drivers of cancer evolution, yet accurately resolving allele-specific copy number (ASCN) in highly rearranged tumor genomes remains challenging. Conventional approaches often struggle with limited phasing information and inaccurate segmentation. Long-read sequencing offers unique advantages for genome reconstruction, but current CN tools do not fully exploit its long-range phasing or breakpoint-level resolution.We present Minerva, a long-read framework for ASCN calling and complex SV clustering. Minerva integrates high-fidelity structural variation calls from Severus into a breakpoint graph that defines segmentation directly on rearrangement breakpoints. This approach enables chromosome-arm-scale phasing, allowing haplotypes to be followed through long and structurally complex regions. It further provides haplotype-specific coverage estimates and accurate CN assignment even across SV clusters. Minerva supports both tumor-normal and tumor-only workflows, delivering stable ploidy estimation and haplotype-aware CN inference even in the absence of matched normals. Using the CASTLE long-read somatic cancer cell line panel, we benchmarked Minerva against established short-read and long-read CN methods. Across nearly all cell lines, Minerva achieved higher chromosome-scale CN accuracy, more consistent purity/ploidy estimates, and markedly improved detection of focal CN changes, including deletions, duplications, or more complex events like translocations with deletions. Leveraging the precision of long-read SV breakpoints, Minerva resolves sub-100 bp CN segments, representing orders-of-magnitude finer resolution than short-read approaches. Performance gains were strongest in genomes with dense rearrangements, including fold-back inversions, sysmic amplifications, templated insertions, and ecDNA. We further applied Minerva to (i) a breast cancer long-read cell-line panel and (ii) a long-read breast tumor cohort. Across both datasets, Minerva identified distinct and recurrent amplification patterns in key oncogenic regions, including MYC, ERBB2, and CCND1, and revealed allele-specific focal events and complex rearrangement architectures missed by existing CN/SV methods. Minerva provides a unified, phasing-aware solution for high-resolution CN and SV interpretation in tumor-only long-read cancer genomics, enabling more accurate reconstruction of cancer genome structure, selective pressures, and oncogenic amplification landscapes.