ArticlePLoS computational biology2011
deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 321 papers.
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Who cites it
321 citing papers in PubMed.
- Integrated analysis of relapsed B-cell precursor Acute Lymphoblastic Leukemia identifies subtype-specific cytokine and metabolic signatures.Scientific reports · 2019Trial
- Clinical and molecular characteristics ofHaematologica · 2019Trial
- An epithelioid hemangioendothelioma with a novel RREB1::TFE3 gene fusion.Virchows Archiv : an international journal of pathology · 2026Article
- A de novo assembly based fusion gene detection concept based on DNA-seq data of 100 Ewing sarcoma cases.Briefings in bioinformatics · 2026Article
- The Charcot-Marie-Tooth Neuropathy (CMTX3) Complex Structural Variation Causes Differential SOX3 Spatiotemporal Expression.Human mutation · 2026Article
- Fast and sensitive detection of targeted gene fusions using frequency minimizers and fuzzy pattern matching with Fuzzion2.Cell reports methods · 2025Article
- Clinical utility of targeted RNA sequencing in cancer molecular diagnostics.Nature medicine · 2025Article
- Whole transcriptome analysis identifiesCancer pathogenesis and therapy · 2025Article
- Radiotranscriptomics in papillary thyroid carcinoma complement current noninvasive risk stratification system.Science advances · 2025Article
- PLAG1 fusions define a third subtype of CNS embryonal tumor with PLAG family gene alteration.Acta neuropathologica · 2025Article
- Accurate fusion transcript identification from long- and short-read isoform sequencing at bulk or single-cell resolution.Genome research · 2025Article
- Understanding the Role of Rho GTPase Activating Protein and Bone Marrow Kinase X: A Novel Target in Gastric Cancer Treatment.Asian Pacific journal of cancer prevention : APJCP · 2025Review
- FuSViz-visualization and interpretation of structural variation using cancer genomics and transcriptomics data.Nucleic acids research · 2025Article
- Architects and Partners: The Dual Roles of Non-coding RNAs in Gene Fusion Events.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Identification of theFrontiers in oncology · 2025Article
- A New Approach of DetectingCancers · 2024Article
- 3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data.Briefings in bioinformatics · 2024Article
- Adenoid cystic carcinoma of the Bartholin's gland is underpinned by MYB- and MYBL1- rearrangements.Gynecologic oncology · 2024Article
- CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution.bioRxiv : the preprint server for biology · 2024Article
- Genomic DNA-based measurable residual disease monitoring in pediatric acute myeloid leukemia: unselected consecutive cohort study.Leukemia · 2024Article
261 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
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Abstract
Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes.
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