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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "deepSNV" in publications use:'
type: software
license: GPL-3.0-only
title: 'deepSNV: Detection of subclonal SNVs in deep sequencing data.'
version: 1.51.0
abstract: This package provides provides quantitative variant callers for detecting
  subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The
  deepSNV algorithm is used for a comparative setup with a control experiment of the
  same loci and uses a beta-binomial model and a likelihood ratio test to discriminate
  sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes
  classifier based on a beta-binomial model for variant calling with multiple samples
  for precisely estimating model parameters - such as local error rates and dispersion
  - and prior knowledge, e.g. from variation data bases such as COSMIC.
authors:
- family-names: Gerstung
  given-names: Moritz
  email: moritz.gerstung@ebi.ac.uk
preferred-citation:
  type: article
  title: Reliable detection of subclonal single-nucleotide variants in tumor cell
    populations
  authors:
  - family-names: Gerstung
    given-names: Moritz
    email: moritz.gerstung@ebi.ac.uk
  - family-names: Beisel
    given-names: Christian
  - family-names: Rechsteiner
    given-names: Markus
  - family-names: Wild
    given-names: Peter
  - family-names: Schraml
    given-names: Peter
  - family-names: Moch
    given-names: Holger
  - family-names: Beerenwinkel
    given-names: Niko
  abstract: According to the clonal evolution model, tumor growth is driven by competing
    subclones in somatically evolving cancer cell populations, which gives rise to
    genomically heterogeneous tumors. We present a comparative sequencing approach
    combined with a customized statistical algorithm for detecting and quantifying
    subclonal single-nucleotide variants in mixed populations. We rigorously assess
    this method experimentally and show that it is capable of detecting variants with
    frequencies as low as 1/10,000 alleles. In selected genomic loci of the TP53 and
    VHL genes isolated from matched tumor and normal samples of four renal cell carcinoma
    patients, we detect 24 variants at allele frequencies ranging from 0.0002 to 0.34.
    Our findings demonstrate that genomic diversity is common in renal cell carcinomas
    and provide quantitative evidence for the clonal evolution model.
  journal: Nat Commun
  volume: '3'
  year: '2012'
  start: '811'
repository: https://bioc.r-universe.dev
contact:
- family-names: Gerstung
  given-names: Moritz
  email: moritz.gerstung@ebi.ac.uk
references:
- type: article
  title: Subclonal variant calling with multiple samples and prior knowledge
  authors:
  - family-names: Gerstung
    given-names: Moritz
  - family-names: Papaemmanuil
    given-names: Elli
  - family-names: Campbell
    given-names: Peter J
  journal: Bioinformatics
  volume: '30'
  year: '2014'
  start: 1198-1204