ArticleNucleic acids research2023
Precise characterization of somatic complex structural variations from tumor/control paired long-read sequencing data with nanomonsv.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 51 citations in OpenAlex.
- Structural variant calling using Sniffles2.Nature protocols · 2026Review
- SVPG: a pangenome-based structural variant detection approach and rapid augmentation of pangenome graphs with new samples.Nature methods · 2026Article
- 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
- 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
- Large-scale genome structure interrogation via recombinase-mediated rearrangements of multiplexed prime edits in repetitive elements.Nature protocols · 2026Review
- Personalized reference genome-based pipeline reveals comprehensive haplotype-resolved views of cancer genomes.bioRxiv : the preprint server for biology · 2026Article
- An active Helitron transposon family in wheat.Nature plants · 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
- 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
- Jumping in the human brain: A review on somatic transposition.Fundamental research · 2026Review
- Benchmarking major somatic structural variant callers on the HG008 genome.Frontiers in genetics · 2026Article
- Review
- Long read sequencing reveals novel genomic and epigenomic alterations in repetitive regions of high grade serous ovarian cancer.Scientific reports · 2025Article
- Diploid donor-specific assembly enhances somatic structural variant detection in cancer genomes.bioRxiv : the preprint server for biology · 2025Article
- Improving long-read somatic structural variant calling with pangenome and de novo personal genome assembly.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present our novel software, nanomonsv, for detecting somatic structural variations (SVs) using tumor and matched control long-read sequencing data with a single-base resolution. The current version of nanomonsv includes two detection modules, Canonical SV module, and Single breakend SV module. Using tumor/control paired long-read sequencing data from three cancer and their matched lymphoblastoid lines, we demonstrate that Canonical SV module can identify somatic SVs that can be captured by short-read technologies with higher precision and recall than existing methods. In addition, we have developed a workflow to classify mobile element insertions while elucidating their in-depth properties, such as 5' truncations, internal inversions, as well as source sites for 3' transductions. Furthermore, Single breakend SV module enables the detection of complex SVs that can only be identified by long-reads, such as SVs involving highly-repetitive centromeric sequences, and LINE1- and virus-mediated rearrangements. In summary, our approaches applied to cancer long-read sequencing data can reveal various features of somatic SVs and will lead to a better understanding of mutational processes and functional consequences of somatic SVs.
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