Bioconductor Developer Survey 2026 Now Open!

Uniquorn

Identification of cancer cell lines based on their weighted mutational/ variational fingerprint

Bioconductor version: 3.24 · Package version: 2.33.0

Other Bioconductor versions

devel is the development version; release is the current stable one.

3.24 (devel), 3.23 (release)

'Uniquorn' enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file).

DOI: 10.18129/B9.bioc.Uniquorn

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("Uniquorn")

Details

MaintainerRaik Otto <raik.otto@hu-berlin.de>
AuthorRaik Otto
LicenseArtistic-2.0
Source branchdevel
Build report Bioconductor build system, r-universe
biocViewsExomeSeq, ImmunoOncology, Software, StatisticalMethod, WholeGenome
Package Short Url https://bioconductor.org/packages/Uniquorn/

Citation

From within R, enter citation("Uniquorn"):

Raik Otto. Uniquorn: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint. doi:10.18129/B9.bioc.Uniquorn, R package version 2.33.0, https://bioconductor.org/packages/Uniquorn.

Generated from the package metadata; it may differ from the package's own citation.

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageUniquorn_2.33.0.tar.gz
Windows binary (x86_64)Uniquorn_2.33.0.zip
macOS binary (arm64)Uniquorn_2.33.0.tgz
macOS binary (x86_64)Uniquorn_2.33.0.tgz
Dependencies

Depends: R (>= 3.5)

Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation, data.table

Suggests: testthat, knitr, rmarkdown, BiocGenerics