ArticleNature communications2016
RUBIC identifies driver genes by detecting recurrent DNA copy number breaks.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative oncogenomics identifies combinations of driver genes and drug targets in BRCA1-mutated breast cancer.Nature communications · 2019Pooled it
- CRESCENT: a deep learning framework with multi-scale attention for detecting recurrent copy number alterations.Briefings in bioinformatics · 2026Article
- CNAScope: pan-cancer copy number aberration database with functional annotation and interactive visualization.Nucleic acids research · 2026Article
- Functional screening of amplification outlier oncogenes in organoid models of early tumorigenesis.Cell reports · 2023Article
- Multi-omics analysis reveals distinct non-reversion mechanisms of PARPi resistance in BRCA1- versus BRCA2-deficient mammary tumors.Cell reports · 2023Article
- Article
- Molecular characterization of breast and lung tumors by integration of multiple data types with functional sparse-factor analysis.PLoS computational biology · 2018Article
- Identification of driver copy number alterations in diverse cancer types and application in drug repositioning.Molecular oncology · 2017Article
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Authors and funding
5 authors.
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Abstract
The frequent recurrence of copy number aberrations across tumour samples is a reliable hallmark of certain cancer driver genes. However, state-of-the-art algorithms for detecting recurrent aberrations fail to detect several known drivers. In this study, we propose RUBIC, an approach that detects recurrent copy number breaks, rather than recurrently amplified or deleted regions. This change of perspective allows for a simplified approach as recursive peak splitting procedures and repeated re-estimation of the background model are avoided. Furthermore, we control the false discovery rate on the level of called regions, rather than at the probe level, as in competing algorithms. We benchmark RUBIC against GISTIC2 (a state-of-the-art approach) and RAIG (a recently proposed approach) on simulated copy number data and on three SNP6 and NGS copy number data sets from TCGA. We show that RUBIC calls more focal recurrent regions and identifies a much larger fraction of known cancer genes.
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