https://github.com/lima1/purecn
Copy number calling and variant classification using targeted short read sequencing
https://github.com/lima1/purecn
bioconductor-package cell-free-dna copy-number loh tumor-heterogeneity tumor-mutational-burden tumor-purity
Last synced: 5 months ago
JSON representation
Copy number calling and variant classification using targeted short read sequencing
- Host: GitHub
- URL: https://github.com/lima1/purecn
- Owner: lima1
- License: artistic-2.0
- Created: 2017-08-04T17:49:35.000Z (almost 9 years ago)
- Default Branch: devel
- Last Pushed: 2025-08-28T23:00:21.000Z (11 months ago)
- Last Synced: 2025-10-20T14:06:51.642Z (9 months ago)
- Topics: bioconductor-package, cell-free-dna, copy-number, loh, tumor-heterogeneity, tumor-mutational-burden, tumor-purity
- Language: R
- Homepage: https://bioconductor.org/packages/devel/bioc/html/PureCN.html
- Size: 19 MB
- Stars: 140
- Watchers: 7
- Forks: 34
- Open Issues: 49
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
- License: LICENSE
Awesome Lists containing this project
README
[](https://github.com/lima1/PureCN/actions/workflows/check-bioc.yml)
[](https://bioconductor.org/checkResults/release/bioc-LATEST/PureCN)
[](https://www.bioconductor.org/packages/release/bioc/html/PureCN.html#archives)
[](https://codecov.io/gh/lima1/PureCN)
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# 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[outdated](https://bioconductor.org/about/release-announcements/), this will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For outdated R/Bioconductor versions, you can try backporting the latest stable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_21")
```
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=2.12.0
```
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).