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README.md
[![R-CMD-check-bioc](https://github.com/lima1/PureCN/actions/workflows/check-bioc.yml/badge.svg)](https://github.com/lima1/PureCN/actions/workflows/check-bioc.yml) [![BioC status](http://www.bioconductor.org/shields/build/release/bioc/PureCN.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/PureCN) [![Platforms](http://www.bioconductor.org/shields/availability/release/PureCN.svg)](https://www.bioconductor.org/packages/release/bioc/html/PureCN.html#archives) [![Coverage](https://img.shields.io/codecov/c/github/lima1/PureCN.svg)](https://codecov.io/gh/lima1/PureCN) [![License: Artistic-2.0](https://img.shields.io/badge/License-Artistic%202.0-0298c3.svg)](https://opensource.org/licenses/Artistic-2.0) # PureCN A tool developed for tumor-only diagnostic sequencing using hybrid-capture protocols. It provides copy number adjusted for purity and ploidy and can classify mutations by somatic status and clonality. It requires a pool of process-matched normals for coverage normalization and artifact filtering. PureCN was parameterized using large collections of diverse samples, ranging from low coverage whole-exome to ultra-deep sequenced plasma gene-panels. ## Installation To install this package, start R and enter: ``` if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("PureCN") ``` If your R/Bioconductor version is [outdated](https://bioconductor.org/about/release-announcements/), this will install an old and unsupported version. For outdated R/Bioconductor versions, you can try backporting the latest stable version (this should work fine for Bioconductor 3.3 and later): ``` BiocManager::install("lima1/PureCN", ref = "RELEASE_3_18") ``` If you want the latest and greatest from the developer branch: ``` BiocManager::install("lima1/PureCN") ``` To get the lastest stable version from [Conda](https://anaconda.org/bioconda/bioconductor-purecn) (unstable is currently only available from GitHub directly): ``` conda install -c bioconda bioconductor-purecn=2.8.1 ``` A [Dockerhub](https://hub.docker.com/r/markusriester/purecn) image of the latest stable version with recommended dependencies such as [GenomicsDB](https://github.com/GenomicsDB/GenomicsDB) and [GATK 4](https://github.com/broadinstitute/gatk) pre-installed: ``` docker pull markusriester/purecn:latest ``` ## Tutorials To get started: ``` vignette("Quick", package = "PureCN") ``` For the R package and more detailed information: ``` vignette("PureCN", package = "PureCN") ``` These tutorials are also available on the Bioconductor project page ([devel](https://bioconductor.org/packages/devel/bioc/html/PureCN.html), [stable](https://doi.org/doi:10.18129/B9.bioc.PureCN)). ## Bugs Before [posting](https://github.com/lima1/PureCN/issues) a bug report: * update to the latest version * confirm with sessionInfo() that the latest version is used * if this is a first PureCN attempt, closely follow the Quick vignette ([devel](https://bioconductor.org/packages/devel/bioc/vignettes/PureCN/inst/doc/Quick.html), [stable](https://bioconductor.org/packages/release/bioc/vignettes/PureCN/inst/doc/Quick.html)) * make sure that the issue is not covered in the Support section of the main vignette ## Papers * Main paper describing the likelihood model: Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D and Morrissey M (2016). “PureCN: Copy number calling and SNV classification using targeted short read sequencing.” _Source Code for Biology and Medicine_, **11**, pp. 13. doi: [10.1186/s13029-016-0060-z](https://doi.org/10.1186/s13029-016-0060-z). * Validation paper, including description of novel additions, such as off-target support, tangent normalization and tweaks to the likelihood model: Oh S, Geistlinger L, Ramos M, Morgan M, Waldron L, Riester M (2020). Reliable analysis of clinical tumor-only whole exome sequencing data. _JCO Clinical Cancer Informatics_. doi: [10.1200/CCI.19.00130](https://doi.org/10.1200/CCI.19.00130); _bioRxiv_. doi: [10.1101/552711](https://doi.org/10.1101/552711) ## Selected citations Pereira et al. (2021). "Cell-free DNA captures tumor heterogeneity and driver alterations in rapid autopsies with pre-treated metastatic cancer". _Nature Communications_. doi: [10.1038/s41467-021-23394-4](https://doi.org/10.1038/s41467-021-23394-4). Dummer et al. (2020). "Combined PD-1, BRAF and MEK inhibition in advanced BRAF-mutant melanoma: safety run-in and biomarker cohorts of COMBI-i". _Nature Medicine_. doi: [10.1038/s41591-020-1082-2](https://doi.org/10.1038/s41591-020-1082-2). Bertucci et al. (2019). "Genomic characterization of metastatic breast cancers". _Nature_. doi: [10.1038/s41586-019-1056-z](https://doi.org/10.1038/s41586-019-1056-z). Dagogo-Jack et al. (2018). "Tracking the evolution of resistance to ALK tyrosine kinase inhibitors through longitudinal analysis of circulating tumor DNA". _JCO Precision Oncology_. doi: [10.1200/PO.17.00160](https://doi.org/10.1200/PO.17.00160). Orlando et al. (2018). "Genetic mechanisms of target antigen loss in CAR19 therapy of acute lymphoblastic leukemia". _Nature Medicine_. doi: [10.1038/s41591-018-0146-z](https://doi.org/10.1038/s41591-018-0146-z). Pal et al. (2018). "Efficacy of BGJ398, a fibroblast growth factor receptor 1-3 inhibitor, in patients with previously treated advanced urothelial carcinoma with FGFR3 alterations". _Cancer Discovery_. doi: [10.1158/2159-8290.CD-18-0229](https://doi.org/10.1158/2159-8290.CD-18-0229). Pitt et al. (2018). "Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features". _Nature Communications_. doi: [10.1038/s41467-018-06616-0](https://doi.org/10.1038/s41467-018-06616-0).