ArticleNucleic acids research2017
Statistical algorithms improve accuracy of gene fusion detection.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Characteristics of fusion genes in breast cancer.Cancer cell international · 2026Review
- Benchmarking genomic foundation models for binary classification of gene fusion breakpoints from DNA sequences.BioData mining · 2026Article
- A de novo assembly based fusion gene detection concept based on DNA-seq data of 100 Ewing sarcoma cases.Briefings in bioinformatics · 2026Article
- A convergent malignant phenotype in B-cell acute lymphoblastic leukemia involving the splicing factor SRRM1.NAR cancer · 2022Article
- Specific splice junction detection in single cells with SICILIAN.Genome biology · 2021Article
- Improved detection of gene fusions by applying statistical methods reveals oncogenic RNA cancer drivers.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine.Frontiers in cell and developmental biology · 2017Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors.
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Registered trials
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