ArticleBMC genomics2013
Barnacle: detecting and characterizing tandem duplications and fusions in transcriptome assemblies.
Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 18 citations in OpenAlex.
- A de novo assembly based fusion gene detection concept based on DNA-seq data of 100 Ewing sarcoma cases.Briefings in bioinformatics · 2026Article
- MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data.Genome biology · 2021Article
- Computational identification of micro-structural variations and their proteogenomic consequences in cancer.Bioinformatics (Oxford, England) · 2018Article
- SQUID: transcriptomic structural variation detection from RNA-seq.Genome biology · 2018Article
- Allelic decomposition and exact genotyping of highly polymorphic and structurally variant genes.Nature communications · 2018Article
- Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer.Cell reports · 2016Article
Corrections and comments
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Authors and funding
26 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundChimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers.
resultsWe describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets.
conclusionsOur analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases.
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Registered trials
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