欧洲生物信息学研究所Isidro Cortes-Ciriano小组近日取得一项新成果。经过不懈努力，他们研制了SAVANA：使用长读测序对体细胞结构变异和拷贝数畸变进行可靠分析。该项研究成果发表在2025年5月28日出版的《自然—方法学》上。

在这里，课题组人员描述了SAVANA，这是一种算法，旨在以单倍型分辨率检测体细胞SV和SCNA，并在有或没有种系对照样本的情况下估计肿瘤纯度和倍性主题的长读测序数据。研究组还建立了以数据驱动的方式在整个基因组中对SV检测算法进行基准测试的最佳实践，以主题复制和读回分相分析。通过对99对人类肿瘤-正常配对的Illumina和纳米孔全基因组测序数据的分析，课题组研究人员发现SAVANA比第二和第三名的算法具有更高的灵敏度，特异性分别高出13倍和82倍。

此外，SAVANA报告的SV与短读测序检测到的SV高度一致。总之，SAVANA使长读测序能够可靠地检测SVs和SCNAs。

据介绍，准确检测体细胞结构变异（SVs）和体细胞拷贝数畸变（SCNAs）对于研究支撑癌症进化的突变过程至关重要。

附：英文原文

Title: SAVANA: reliable analysis of somatic structural variants and copy number aberrations using long-read sequencing

Author: Elrick, Hillary, Sauer, Carolin M., Espejo Valle-Inclan, Jose, Trevers, Katherine, Tanguy, Melanie, Zumalave, Sonia, De Noon, Solange, Muyas, Francesc, Casco, Rita, Afonso, Angela, Rust, Alistair G., Amary, Fernanda, Tirabosco, Roberto, Giess, Adam, Freeman, Timothy, Sosinsky, Alona, Piculell, Katherine, Miller, David T., Faria, Claudia C., Elgar, Greg, Flanagan, Adrienne M., Cortes-Ciriano, Isidro

Issue&Volume: 2025-05-28

Abstract: Accurate detection of somatic structural variants (SVs) and somatic copy number aberrations (SCNAs) is critical to study the mutational processes underpinning cancer evolution. Here we describe SAVANA, an algorithm designed to detect somatic SVs and SCNAs at single-haplotype resolution and estimate tumor purity and ploidy using long-read sequencing data with or without a germline control sample. We also establish best practices for benchmarking SV detection algorithms across the entire genome in a data-driven manner using replication and read-backed phasing analysis. Through the analysis of matched Illumina and nanopore whole-genome sequencing data for 99 human tumor-normal pairs, we show that SAVANA has significantly higher sensitivity and 13- and 82-times-higher specificity than the second and third-best performing algorithms. Moreover, SVs reported by SAVANA are highly consistent with those detected using short-read sequencing. In summary, SAVANA enables the application of long-read sequencing to detect SVs and SCNAs reliably.

DOI: 10.1038/s41592-025-02708-0

Source: https://www.nature.com/articles/s41592-025-02708-0