ArticleNature biotechnology2014
Comprehensive characterization of complex structural variations in cancer by directly comparing genome sequence reads.
Article in Nature biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
53 citing papers in PubMed.
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Integrative radiomics and habitat imaging models for predicting PD-L1 expression in non-small cell lung cancer.Frontiers in oncology · 2026Article
- Eliciting antitumor immunity via therapeutic cancer vaccines.Cellular & molecular immunology · 2025Review
- Whole-genome and whole-exome sequencing of Mayer-Rokitansky-Küster-Hauser syndrome-discordant monozygotic twins.Journal of assisted reproduction and genetics · 2025Article
- Chromoplexy: A Pathway to Genomic Complexity and Cancer Development.International journal of molecular sciences · 2025Review
- Taming large-scale genomic analyses via sparsified genomics.Nature communications · 2025Article
- Chromoplexy.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Chromothripsis.Methods in molecular biology (Clifton, N.J.) · 2025Review
- ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers.Cell genomics · 2024Article
- The Digital World of Cytogenetic and Cytogenomic Web Resources.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Ultrafast prediction of somatic structural variations by filtering out reads matched to pan-genome k-mer sets.Nature biomedical engineering · 2023Article
- RFcaller: a machine learning approach combined with read-level features to detect somatic mutations.NAR genomics and bioinformatics · 2023Article
- Detection of early seeding of Richter transformation in chronic lymphocytic leukemia.Nature medicine · 2022Article
- Identification of neoantigens for individualized therapeutic cancer vaccines.Nature reviews. Drug discovery · 2022Review
- Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma.Frontiers in genetics · 2022Article
- Current status of structural variation studies in plants.Plant biotechnology journal · 2021Review
- Neuroblastoma Risk Assessment and Treatment Stratification with Hybrid Capture-Based Panel Sequencing.Journal of personalized medicine · 2021Article
- GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing.Genome biology · 2021Article
- Article
- Editorial: Scalable Bioinformatics: Methods, Software Tools, and Hardware Architectures.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of high-throughput sequencing technologies has advanced our understanding of cancer. However, characterizing somatic structural variants in tumor genomes is still challenging because current strategies depend on the initial alignment of reads to a reference genome. Here, we describe SMUFIN (somatic mutation finder), a single program that directly compares sequence reads from normal and tumor genomes to accurately identify and characterize a range of somatic sequence variation, from single-nucleotide variants (SNV) to large structural variants at base pair resolution. Performance tests on modeled tumor genomes showed average sensitivity of 92% and 74% for SNVs and structural variants, with specificities of 95% and 91%, respectively. Analyses of aggressive forms of solid and hematological tumors revealed that SMUFIN identifies breakpoints associated with chromothripsis and chromoplexy with high specificity. SMUFIN provides an integrated solution for the accurate, fast and comprehensive characterization of somatic sequence variation in cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.