ArticleBMC bioinformatics2017
CLOVE: classification of genomic fusions into structural variation events.
Article in BMC bioinformatics, 2017. 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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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Profound cell wall remodeling inMicrobiology spectrum · 2026Article
- PerSVade: personalized structural variant detection in any species of interest.Genome biology · 2022Article
- Using genomics to understand the mechanisms of virulence and drug resistance in fungal pathogens.Biochemical Society transactions · 2022Review
- Narrow mutational signatures drive acquisition of multidrug resistance in the fungal pathogen Candida glabrata.Current biology : CB · 2021Article
- A pipeline for complete characterization of complex germline rearrangements from long DNA reads.Genome medicine · 2020Article
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software.Nature communications · 2019Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion).
resultsWe present CLOVE, an algorithm for integrating the results of multiple breakpoint or SV callers and classifying the results as a particular SV. CLOVE is based on a graph data structure that is created from the breakpoint information. The algorithm looks for patterns in the graph that are characteristic of more complex rearrangement types. CLOVE is able to integrate the results of multiple callers, producing a consensus call.
conclusionsWe demonstrate using simulated and real data that re-classified SV calls produced by CLOVE improve on the raw call set of existing SV algorithms, particularly in terms of accuracy. CLOVE is freely available from http://www.github.com/PapenfussLab .
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Registered trials
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