ArticleNature methods2025
SAVANA: reliable analysis of somatic structural variants and copy number aberrations using long-read sequencing.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- SVPG: a pangenome-based structural variant detection approach and rapid augmentation of pangenome graphs with new samples.Nature methods · 2026Article
- Somatic mosaicism in the brain: linking development, ageing and neurodegeneration.Nature reviews. Neurology · 2026Review
- Comprehensive evaluation of structural variation detection for germline and somatic analysis with long-read sequencing data.Briefings in bioinformatics · 2026Article
- Improving Long-Read Somatic Structural Variant Calling with Pangenome and De Novo Personal Genome Assembly.Cancer research communications · 2026Article
- nf-core/pacsomatic: a scalable somatic analytic pipeline using PacBio HiFi data.Bioinformatics (Oxford, England) · 2026Article
- ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.Nature methods · 2026Article
- A Personalized Haplotype-Resolved Near-Gapless Genome Framework for Somatic Variant Discovery in Hepatocellular Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- [Applications and Challenges of Deep Learning in Human Genome Research].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- Long-read sequencing of single cell-derived melanoma sublines reveals divergent and parallel genomic and epigenomic evolutionary trajectories.Nature communications · 2026Article
- Murine osteosarcoma recapitulates the driver landscape and genomic complexity of osteosarcoma evolution in humans.bioRxiv : the preprint server for biology · 2026Article
- A complete human pancreatic cancer genome.bioRxiv : the preprint server for biology · 2026Article
- SVScope improves somatic structural variations detection via graph-genome optimization.Genome biology · 2026Article
- cuteHap: Haplotype-Aware Structural Variant Detection in Phased Long-Read Sequencing Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- NanoVar: a comprehensive workflow for structural variant detection to uncover the genome's hidden patterns.Nature protocols · 2026Review
- Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing.Nature biotechnology · 2026Article
- Article
- Detecting foldback artifacts in long-reads.BMC genomics · 2026Article
- Benchmarking major somatic structural variant callers on the HG008 genome.Frontiers in genetics · 2026Article
- Satellite DNA fragility accompanies complex genome rearrangements and ecDNA oncogene amplification in canine osteosarcomas.bioRxiv : the preprint server for biology · 2025Article
- Detecting Foldback Artifacts in Long-reads.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
22 authors.
Funding
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.
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