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PLoS One. 2017 Dec 19;12(12):e0189334. doi: 10.1371/journal.pone.0189334. eCollection 2017.

aCNViewer: Comprehensive genome-wide visualization of absolute copy number and copy neutral variations.

Author information

Laboratory for Bioinformatics, Fondation Jean Dausset-CEPH, Paris, France.
Laboratory of Excellence GenMed, Paris, France.
Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA, Evry, France.
Inserm, UMR-1162, Génomique fonctionnelle des tumeurs solides, Institut Universitaire d'Hématologie (IUH), Paris, France.
Laboratory for Genomics, Fondation Jean Dausset-CEPH, Paris, France.



Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information.


To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B).


aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub and Docker


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