Summary: Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have accumulated a variety of high throughput sequencing and molecular profiling data, but it is still challenging to identify potentially causal genetic mutations in cancer as well as in other diseases in an automated fashion. We developed the NetBoxR package written in the R programming language, that makes use of the NetBox algorithm to identify candidate

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... cer-related processes. The algorithm makes use of a network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of functionally curated gene sets. A key aspect of this approach is its ability to combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. Availability and implementation: The NetBoxR package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html Contact: lium2@mskcc.org; aluna@jimmy.harvard.edu; sander.research@gmail.com Supplementary information: None