WaveCNV: Allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing

Carson Holt, Bojan Losic, Deepa Pai, Zhen Zhao, Quang Trinh, Sujata Syam, Niloofar Arshadi, Gun Ho Jang, Johar Ali, Tim Beck, John Douglas Mcpherson, Lakshmi B. Muthuswamy

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Motivation: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.Results: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.

Original languageEnglish (US)
Pages (from-to)768-774
Number of pages7
JournalBioinformatics
Volume30
Issue number6
DOIs
StatePublished - Mar 2014
Externally publishedYes

Fingerprint

Heterografts
Sequencing
Tumors
Tumor
Genes
Alleles
Genome
Neoplasms
Microarrays
Cancer
Model
Technology
Wavelet Analysis
Microarray
Correlation coefficient
Polymerase chain reaction
Discrete wavelet transforms
Aneuploidy
Assign
Diploidy

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

WaveCNV : Allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing. / Holt, Carson; Losic, Bojan; Pai, Deepa; Zhao, Zhen; Trinh, Quang; Syam, Sujata; Arshadi, Niloofar; Jang, Gun Ho; Ali, Johar; Beck, Tim; Mcpherson, John Douglas; Muthuswamy, Lakshmi B.

In: Bioinformatics, Vol. 30, No. 6, 03.2014, p. 768-774.

Research output: Contribution to journalArticle

Holt, C, Losic, B, Pai, D, Zhao, Z, Trinh, Q, Syam, S, Arshadi, N, Jang, GH, Ali, J, Beck, T, Mcpherson, JD & Muthuswamy, LB 2014, 'WaveCNV: Allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing', Bioinformatics, vol. 30, no. 6, pp. 768-774. https://doi.org/10.1093/bioinformatics/btt611
Holt, Carson ; Losic, Bojan ; Pai, Deepa ; Zhao, Zhen ; Trinh, Quang ; Syam, Sujata ; Arshadi, Niloofar ; Jang, Gun Ho ; Ali, Johar ; Beck, Tim ; Mcpherson, John Douglas ; Muthuswamy, Lakshmi B. / WaveCNV : Allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing. In: Bioinformatics. 2014 ; Vol. 30, No. 6. pp. 768-774.
@article{440e1bd3a44846ad9329866cb95ace41,
title = "WaveCNV: Allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing",
abstract = "Motivation: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.Results: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.",
author = "Carson Holt and Bojan Losic and Deepa Pai and Zhen Zhao and Quang Trinh and Sujata Syam and Niloofar Arshadi and Jang, {Gun Ho} and Johar Ali and Tim Beck and Mcpherson, {John Douglas} and Muthuswamy, {Lakshmi B.}",
year = "2014",
month = "3",
doi = "10.1093/bioinformatics/btt611",
language = "English (US)",
volume = "30",
pages = "768--774",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "6",

}

TY - JOUR

T1 - WaveCNV

T2 - Allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing

AU - Holt, Carson

AU - Losic, Bojan

AU - Pai, Deepa

AU - Zhao, Zhen

AU - Trinh, Quang

AU - Syam, Sujata

AU - Arshadi, Niloofar

AU - Jang, Gun Ho

AU - Ali, Johar

AU - Beck, Tim

AU - Mcpherson, John Douglas

AU - Muthuswamy, Lakshmi B.

PY - 2014/3

Y1 - 2014/3

N2 - Motivation: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.Results: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.

AB - Motivation: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.Results: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.

UR - http://www.scopus.com/inward/record.url?scp=84897853363&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897853363&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btt611

DO - 10.1093/bioinformatics/btt611

M3 - Article

C2 - 24192544

AN - SCOPUS:84897853363

VL - 30

SP - 768

EP - 774

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 6

ER -