Patchwork: allele-specific copy number analysis of whole-genome sequenced tumor tissue

Mayrhofer, Markus; DiLorenzo, Sebastian; Isaksson, Anders

doi:10.1186/gb-2013-14-3-r24

Cited by 68 publications

(75 citation statements)

References 33 publications

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“…For example, tumor samples are often infiltrated with normal stroma, resulting in inevitable contamination of normal DNA and dilution of somatic aberration signals (Boeva et al , 2011, 2012; Gusnanto et al , 2012; Ha et al , 2012; Mayrhofer et al , 2013). Impurity of tumor sample can significantly alter WGS data; and therefore, complicates genomic aberration detection, especially when normal cells dominate in tumor samples.…”

Section: Introductionmentioning

confidence: 99%

CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data

Liu

Shen

et al. 2014

Bioinformatics

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Motivation: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore, efficient computational methods are required to address these issues.Results: We introduce CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors), a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B-allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples.Availability and implementation: The CLImAT software package can be freely downloaded at http://bioinformatics.ustc.edu.cn/CLImAT/.Contact: aoli@ustc.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data

Liu

Shen

et al. 2014

Bioinformatics

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“…Compared to conventional copy number analysis, which usually estimates the total copy number for a given genomic window, allele-specific copy number analysis (ASCN) is becoming increasingly popular due to its promise in clonality analysis [23–25]. ASCN methods require read counts and allele frequency at each single nucleotide as input data to infer high-resolution allele-specific copy number and accurate tumor purity/ploidy.…”

Section: Discussionmentioning

confidence: 99%

SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling

et al. 2017

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Changes in the quantity of genetic material, known as somatic copy number alterations (CNAs), can drive tumorigenesis. Many methods exist for assessing CNAs using microarrays, but considerable technical issues limit current CNA calling based upon DNA sequencing. We present SynthEx, a novel tool for detecting CNAs from whole exome and genome sequencing. SynthEx utilizes a “synthetic-normal” strategy to overcome technical and financial issues. In terms of accuracy and precision, SynthEx is highly comparable to array-based methods and outperforms sequencing-based CNA detection tools. SynthEx robustly identifies CNAs using sequencing data without the additional costs associated with matched normal specimens.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1193-3) contains supplementary material, which is available to authorized users.

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“…Furthermore, for SNV classification, not only total copy number is needed, but also the maternal and paternal copy numbers are important and only few published algorithms for sequencing data provide allele-specific copy numbers (e.g. [3, 8]). A reliable algorithm for classifying private variants would make sequencing of matched normal samples less important, especially in settings such as diagnostics where variants of uncertain significance (VUS) are typically ignored, but where secondary, exploratory analyses are common.…”

Section: Introductionmentioning

confidence: 99%

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

Riester

Singh

Brannon

et al. 2016

Source Code Biol Med

125

113

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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/).Electronic supplementary materialThe online version of this article (doi:10.1186/s13029-016-0060-z) contains supplementary material, which is available to authorized users.

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Patchwork: allele-specific copy number analysis of whole-genome sequenced tumor tissue

Cited by 68 publications

References 33 publications

CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data

CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data

SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling

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

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