Niknafs / PureCN

Copy number calling and variant classification using targeted short read sequencing

Home Page:https://bioconductor.org/packages/devel/bioc/html/PureCN.html

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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If your R/Bioconductor version is outdated, this will install an old and unsupported version.

For outdated R/Bioconductor versions, you can try backporting the latest stable version (this should work fine for Bioconductor 3.3 and later):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_8")

If you want the latest and greatest from the developer branch:

BiocManager::install("lima1/PureCN")

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, stable).

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.

  • 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 (2019). Reliable analysis of clinical tumor-only whole exome sequencing data. bioRxiv. doi: 10.1101/552711

Selected citations

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.

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.

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.

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.

About

Copy number calling and variant classification using targeted short read sequencing

https://bioconductor.org/packages/devel/bioc/html/PureCN.html

License:Artistic License 2.0


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