Evidence map›Paper›PMID 27999612›Full record

ArticleSource code for biology and medicine2016

PureCN: copy number calling and SNV classification using targeted short read sequencing.

Markus Riester, Angad P Singh, A Rose Brannon, Kun Yu, Catarina D Campbell, Derek Y Chiang, Michael P Morrissey

Abstract read
In one paragraph

Article in Source code for biology and medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

110 citing papers in PubMed.

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  3. Genomic and Epigenomic ctDNA Profiling in Liquid Biopsies from Heavily Pretreated Patients with DNA Damage Response-Deficient Tumors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025
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  17. Mapping the genetic landscape of hereditary diffuse-type gastric cancer progression.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
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  18. Evaluation of homologous recombination testing in ovarian carcinoma.Virchows Archiv : an international journal of pathology · 2026
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50 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Markus RiesterNovartis Institutes for BioMedical Research, Cambridge, MA USA.ORCID 0000-0002-4759-8332
Angad P SinghNovartis Institutes for BioMedical Research, Cambridge, MA USA.
A Rose BrannonNovartis Institutes for BioMedical Research, Cambridge, MA USA.
Kun YuNovartis Institutes for BioMedical Research, Cambridge, MA USA.
Catarina D CampbellNovartis Institutes for BioMedical Research, Cambridge, MA USA.
Derek Y ChiangNovartis Institutes for BioMedical Research, Cambridge, MA USA.
Michael P MorrisseyNovartis Institutes for BioMedical Research, Cambridge, MA USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMatched sequencing of both tumor and normal tissue is routinely used to classify variants of uncertain significance (VUS) into somatic vs. germline. However, assays used in molecular diagnostics focus on known somatic alterations in cancer genes and often only sequence tumors. Therefore, an algorithm that reliably classifies variants would be helpful for retrospective exploratory analyses. Contamination of tumor samples with normal cells results in differences in expected allelic fractions of germline and somatic variants, which can be exploited to accurately infer genotypes after adjusting for local copy number. However, existing algorithms for determining tumor purity, ploidy and copy number are not designed for unmatched short read sequencing data.

resultsWe describe a methodology and corresponding open source software for estimating tumor purity, copy number, loss of heterozygosity (LOH), and contamination, and for classification of single nucleotide variants (SNVs) by somatic status and clonality. This R package, PureCN, is optimized for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for matched and unmatched tumor samples. Accuracy is demonstrated on simulated data and on real whole exome sequencing data.

conclusionsOur algorithm provides accurate estimates of tumor purity and ploidy, even if matched normal samples are not available. This in turn allows accurate classification of SNVs. The software is provided as open source (Artistic License 2.0) R/Bioconductor package PureCN (http://bioconductor.org/packages/PureCN/).

Indexed as

Cell linesCopy numberHeterogeneityHybrid captureLoss of heterozygosityPloidyPurityWhole exome sequencing

Identifiers

PMID27999612
PMCPMC5157099

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.