1
|
Identification of Copy Number Alterations from Next-Generation Sequencing Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:55-74. [DOI: 10.1007/978-3-030-91836-1_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
2
|
Yuan X, Li J, Bai J, Xi J. A Local Outlier Factor-Based Detection of Copy Number Variations From NGS Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1811-1820. [PMID: 31880558 DOI: 10.1109/tcbb.2019.2961886] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Copy number variation (CNV) is a major type of genomic structural variations that play an important role in human disorders. Next generation sequencing (NGS) has fueled the advancement in algorithm design to detect CNVs at base-pair resolution. However, accurate detection of CNVs of low amplitudes remains a challenging task. This paper proposes a new computational method, CNV-LOF, to identify CNVs of full-range amplitudes from NGS data. CNV-LOF is distinctly different from traditional methods, which mainly consider aberrations from a global perspective and rely on some assumed distribution of NGS read depths. In contrast, CNV-LOF takes a local view on the read depths and assigns an outlier factor to each genome segment. With the outlier factor profile, CNV-LOF uses a boxplot procedure to declare CNVs without the reliance of any distribution assumptions. Simulation experiments indicate that CNV-LOF outperforms five existing methods with respect to F1-measure, sensitivity, and precision. CNV-LOF is further validated on real sequencing samples, yielding highly consistent results with peer methods. CNV-LOF is able to detect CNVs of low and moderate amplitudes where the other existing methods fail, and it is expected to become a routine approach for the discovery of novel CNVs on whole sequencing genome.
Collapse
|
3
|
Zhao HY, Li Q, Tian Y, Chen YH, Alvi HAK, Yuan XG. CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data. BIOLOGY 2021; 10:biology10070584. [PMID: 34202028 PMCID: PMC8301091 DOI: 10.3390/biology10070584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/10/2021] [Accepted: 06/21/2021] [Indexed: 12/29/2022]
Abstract
Simple Summary In this study, we propose a copy number variation (CNV) detection method called CIRCNV, which is based on a circular profile of the read depth from sequencing data. The proposed method is an extended version of our previously developed method CNV-LOF. The main difference of CIRCNV from CNV-LOF lies in its two new features: (1) it transfers the read depth profile from a line shape to a circular shape via a polar coordinate transformation to generate a meaningful two-dimensional dataset for CNV analysis and promote fairness between the ends and middle part of the genome, and (2) it performs two rounds of CNV declaration via estimating tumor purity and recovering the truth circular RD profile. We test and evaluate the performance of CIRCNV via conducting simulation studies and real sequencing tumor sample applications. The experimental results show that CIRCNV outperforms peer methods with respect to sensitivity, precision, and the F1-score. The experiments prove that the proposed method is a reliable and effective tool in the field of variation analysis of tumor genomes. Abstract Copy number variation (CNV) is a common type of structural variation in the human genome. Accurate detection of CNVs from tumor genomes can provide crucial information for the study of tumor genesis and cancer precision diagnosis. However, the contamination of normal genomes in tumor genomes and the crude profiles of the read depth make such a task difficult. In this paper, we propose an alternative approach, called CIRCNV, for the detection of CNVs from sequencing data. CIRCNV is an extension of our previously developed method CNV-LOF, which uses local outlier factors to predict CNVs. Comparatively, CIRCNV can be performed on individual tumor samples and has the following two new features: (1) it transfers the read depth profile from a line shape to a circular shape via a polar coordinate transformation, in order to improve the efficiency of the read depth (RD) profile for the detection of CNVs; and (2) it performs a second round of CNV declaration based on the truth circular RD profile, which is recovered by estimating tumor purity. We test and validate the performance of CIRCNV based on simulation and real sequencing data and perform comparisons with several peer methods. The results demonstrate that CIRCNV can obtain superior performance in terms of sensitivity and precision. We expect that our proposed method will be a supplement to existing methods and become a routine tool in the field of variation analysis of tumor genomes.
Collapse
Affiliation(s)
- Hai-Yong Zhao
- School of Computer Science and Technology, Liaocheng University, Liaocheng 252000, China;
| | - Qi Li
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China; (Q.L.); (Y.T.); (H.A.K.A.)
| | - Ye Tian
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China; (Q.L.); (Y.T.); (H.A.K.A.)
| | - Yue-Hui Chen
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Ji’nan 250022, China;
| | - Haque A. K. Alvi
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China; (Q.L.); (Y.T.); (H.A.K.A.)
| | - Xi-Guo Yuan
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China; (Q.L.); (Y.T.); (H.A.K.A.)
- Correspondence:
| |
Collapse
|
4
|
Statistical Considerations on NGS Data for Inferring Copy Number Variations. Methods Mol Biol 2021; 2243:27-58. [PMID: 33606251 DOI: 10.1007/978-1-0716-1103-6_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The next-generation sequencing (NGS) technology has revolutionized research in genetics and genomics, resulting in massive NGS data and opening more fronts to answer unresolved issues in genetics. NGS data are usually stored at three levels: image files, sequence tags, and alignment reads. The sizes of these types of data usually range from several hundreds of gigabytes to several terabytes. Biostatisticians and bioinformaticians are typically working with the aligned NGS read count data (hence the last level of NGS data) for data modeling and interpretation.To horn in on the use of NGS technology, researchers utilize it to profile the whole genome to study DNA copy number variations (CNVs) for an individual subject (or patient) as well as groups of subjects (or patients). The resulting aligned NGS read count data are then modeled by proper mathematical and statistical approaches so that the loci of CNVs can be accurately detected. In this book chapter, a summary of most popularly used statistical methods for detecting CNVs using NGS data is given. The goal is to provide readers with a comprehensive resource of available statistical approaches for inferring DNA copy number variations using NGS data.
Collapse
|
5
|
Whitford W, Lehnert K, Snell RG, Jacobsen JC. RBV: Read balance validator, a tool for prioritising copy number variations in germline conditions. Sci Rep 2019; 9:16934. [PMID: 31729446 PMCID: PMC6858463 DOI: 10.1038/s41598-019-53181-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 10/25/2019] [Indexed: 11/11/2022] Open
Abstract
The popularisation and decreased cost of genome resequencing has resulted in an increased use in molecular diagnostics. While there are a number of established and high quality bioinfomatic tools for identifying small genetic variants including single nucleotide variants and indels, currently there is no established standard for the detection of copy number variants (CNVs) from sequence data. The requirement for CNV detection from high throughput sequencing has resulted in the development of a large number of software packages. These tools typically utilise the sequence data characteristics: read depth, split reads, read pairs, and assembly-based techniques. However, the additional source of information from read balance (defined as relative proportion of reads of each allele at each position) has been underutilised in the existing applications. Here we present Read Balance Validator (RBV), a bioinformatic tool that uses read balance for prioritisation and validation of putative CNVs. The software simultaneously interrogates nominated regions for the presence of deletions or multiplications, and can differentiate larger CNVs from diploid regions. Additionally, the utility of RBV to test for inheritance of CNVs is demonstrated in this report. RBV is a CNV validation and prioritisation bioinformatic tool for both genome and exome sequencing available as a python package from https://github.com/whitneywhitford/RBV.
Collapse
Affiliation(s)
- Whitney Whitford
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand. .,Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
| | - Klaus Lehnert
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand.,Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Russell G Snell
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand.,Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Jessie C Jacobsen
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand.,Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| |
Collapse
|
6
|
Ji T, Chen J. Statistical models for DNA copy number variation detection using read-depth data from next generation sequencing experiments. AUST NZ J STAT 2016. [DOI: 10.1111/anzs.12175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Tieming Ji
- Department of Statistics; University of Missouri at Columbia; Columbia MI 65211 USA
| | - Jie Chen
- Department of Biostatistics and Epidemiology; Medical College of Georgia, Augusta University; Augusta GA 30912 USA
| |
Collapse
|
7
|
Clinical Applications of Next-Generation Sequencing in Cancer Diagnosis. Pathol Oncol Res 2016; 23:225-234. [PMID: 27722982 DOI: 10.1007/s12253-016-0124-z] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 10/04/2016] [Indexed: 12/22/2022]
Abstract
With the advancement and improvement of new sequencing technology, next-generation sequencing (NGS) has been applied increasingly in cancer genomics research fields. More recently, NGS has been adopted in clinical oncology to advance personalized treatment of cancer. NGS is utilized to novel diagnostic and rare cancer mutations, detection of translocations, inversions, insertions and deletions, detection of copy number variants, detect familial cancer mutation carriers, provide the molecular rationale for appropriate targeted, therapeutic and prognostic. NGS holds many advantages, such as the ability to fully sequence all types of mutations for a large number of genes (hundreds to thousands) and the sensitivity, speed in a single test at a relatively low cost compared to be other sequencing modalities. Here we described the technology, methods and applications that can be immediately considered and some of the challenges that lie ahead.
Collapse
|
8
|
Lih CJ, Si H, Das B, Harrington RD, Harper KN, Sims DJ, McGregor PM, Camalier CE, Kayserian AY, Williams PM, He HJ, Almeida JL, Lund SP, Choquette S, Cole KD. Certified DNA Reference Materials to Compare HER2 Gene Amplification Measurements Using Next-Generation Sequencing Methods. J Mol Diagn 2016; 18:753-761. [PMID: 27455875 PMCID: PMC5397679 DOI: 10.1016/j.jmoldx.2016.05.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 05/12/2016] [Accepted: 05/27/2016] [Indexed: 01/29/2023] Open
Abstract
The National Institute of Standards and Technology (NIST) Standard Reference Materials 2373 is a set of genomic DNA samples prepared from five breast cancer cell lines with certified values for the ratio of the HER2 gene copy number to the copy numbers of reference genes determined by real-time quantitative PCR and digital PCR. Targeted-amplicon, whole-exome, and whole-genome sequencing measurements were used with the reference material to compare the performance of both the laboratory steps and the bioinformatic approaches of the different methods using a range of amplification ratios. Although good reproducibility was observed in each next-generation sequencing method, slightly different HER2 copy numbers associated with platform-specific biases were obtained. This study clearly demonstrates the value of Standard Reference Materials 2373 as reference material and as a calibrator for evaluating assay performance as well as for increasing confidence in reporting HER2 amplification for clinical applications.
Collapse
Affiliation(s)
- Chih-Jian Lih
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Han Si
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Biswajit Das
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Robin D Harrington
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Kneshay N Harper
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - David J Sims
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Paul M McGregor
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Corinne E Camalier
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Andrew Y Kayserian
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - P Mickey Williams
- Molecular Characterization and Clinical Assay Development Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Hua-Jun He
- Division of Biosystems and Biomaterials, National Institute of Standards and Technology, Gaithersburg, Maryland
| | - Jamie L Almeida
- Division of Biosystems and Biomaterials, National Institute of Standards and Technology, Gaithersburg, Maryland
| | - Steve P Lund
- Division of Statistical Engineering, National Institute of Standards and Technology, Gaithersburg, Maryland
| | - Steve Choquette
- Division of Biosystems and Biomaterials, National Institute of Standards and Technology, Gaithersburg, Maryland
| | - Kenneth D Cole
- Division of Biosystems and Biomaterials, National Institute of Standards and Technology, Gaithersburg, Maryland.
| |
Collapse
|
9
|
Li Y, Zhou S, Schwartz DC, Ma J. Allele-Specific Quantification of Structural Variations in Cancer Genomes. Cell Syst 2016; 3:21-34. [PMID: 27453446 PMCID: PMC4965314 DOI: 10.1016/j.cels.2016.05.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 05/13/2016] [Accepted: 05/24/2016] [Indexed: 12/21/2022]
Abstract
Aneuploidy and structural variations (SVs) generate cancer genomes containing a mixture of rearranged genomic segments with extensive somatic copy number alterations. However, existing methods can identify either SVs or allele-specific copy number alterations, but not both simultaneously, which provides a limited view of cancer genome structure. Here we introduce Weaver, an algorithm for the quantification and analysis of allele-specific copy numbers of SVs. Weaver uses a Markov Random Field to estimate joint probabilities of allele-specific copy number of SVs and their inter-connectivity based on paired-end whole-genome sequencing data. Weaver also predicts the timing of SVs relative to chromosome amplifications. We demonstrate the accuracy of Weaver using simulations and findings from whole-genome Optical Mapping. We apply Weaver to generate allele-specific copy numbers of SVs for MCF-7 and HeLa cell lines, and identify recurrent SV patterns in 44 TCGA ovarian cancer whole-genome sequencing datasets. Our approach provides a more complete assessment of the complex genomic architectures inherent to many cancer genomes.
Collapse
Affiliation(s)
- Yang Li
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Shiguo Zhou
- Laboratory for Molecular and Computational Genomics, Department of Chemistry, Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - David C Schwartz
- Laboratory for Molecular and Computational Genomics, Department of Chemistry, Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jian Ma
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| |
Collapse
|
10
|
Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. PLoS Comput Biol 2016; 12:e1004871. [PMID: 27177143 PMCID: PMC4866742 DOI: 10.1371/journal.pcbi.1004871] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 03/14/2016] [Indexed: 11/22/2022] Open
Abstract
By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.
Collapse
|
11
|
Yang S, Fang Z. Beta approximation of ratio distribution and its application to next generation sequencing read counts. J Appl Stat 2016; 44:57-70. [PMID: 29456282 DOI: 10.1080/02664763.2016.1158798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Paired sequencing data are commonly collected in genomic studies to control biological variation. However, existing data processing strategies suffer at low coverage regions, which are unavoidable due to the limitation of current sequencing technology. Furthermore, information contained in the absolute values of the read counts is commonly ignored. We propose a read count ratio processing/modification method, to not only incorporate information contained in the absolute values of paired counts into one variable, but also mitigate the discrete artifact, especially when both counts are small. Simulation shows that the processed variable fits well with a Beta distribution, thus providing an easy tool for down-stream inference analysis.
Collapse
Affiliation(s)
- Shengping Yang
- Department of Pathology, School of Medicine, Texas Tech University Health Science Center, Lubbock, Texas, USA
| | - Zhide Fang
- Biostatistics Program, School of Public Health, LSU Health Sciences Center, New Orleans, Louisiana, USA
| |
Collapse
|
12
|
Chien J, Sicotte H, Fan JB, Humphray S, Cunningham JM, Kalli KR, Oberg AL, Hart SN, Li Y, Davila JI, Baheti S, Wang C, Dietmann S, Atkinson EJ, Asmann YW, Bell DA, Ota T, Tarabishy Y, Kuang R, Bibikova M, Cheetham RK, Grocock RJ, Swisher EM, Peden J, Bentley D, Kocher JPA, Kaufmann SH, Hartmann LC, Shridhar V, Goode EL. TP53 mutations, tetraploidy and homologous recombination repair defects in early stage high-grade serous ovarian cancer. Nucleic Acids Res 2015; 43:6945-58. [PMID: 25916844 PMCID: PMC4538798 DOI: 10.1093/nar/gkv111] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Revised: 01/23/2015] [Accepted: 02/02/2015] [Indexed: 12/30/2022] Open
Abstract
To determine early somatic changes in high-grade serous ovarian cancer (HGSOC), we performed whole genome sequencing on a rare collection of 16 low stage HGSOCs. The majority showed extensive structural alterations (one had an ultramutated profile), exhibited high levels of p53 immunoreactivity, and harboured a TP53 mutation, deletion or inactivation. BRCA1 and BRCA2 mutations were observed in two tumors, with nine showing evidence of a homologous recombination (HR) defect. Combined Analysis with The Cancer Genome Atlas (TCGA) indicated that low and late stage HGSOCs have similar mutation and copy number profiles. We also found evidence that deleterious TP53 mutations are the earliest events, followed by deletions or loss of heterozygosity (LOH) of chromosomes carrying TP53, BRCA1 or BRCA2. Inactivation of HR appears to be an early event, as 62.5% of tumours showed a LOH pattern suggestive of HR defects. Three tumours with the highest ploidy had little genome-wide LOH, yet one of these had a homozygous somatic frame-shift BRCA2 mutation, suggesting that some carcinomas begin as tetraploid then descend into diploidy accompanied by genome-wide LOH. Lastly, we found evidence that structural variants (SV) cluster in HGSOC, but are absent in one ultramutated tumor, providing insights into the pathogenesis of low stage HGSOC.
Collapse
Affiliation(s)
- Jeremy Chien
- Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Sean Humphray
- Illumina Cambridge Ltd, Little Chesterford, Essex CB10 1, UK
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Ann L Oberg
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Steven N Hart
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Ying Li
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Jaime I Davila
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Saurabh Baheti
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Chen Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Sabine Dietmann
- Wellcome Trust, Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge CB2 1TN, UK
| | | | - Yan W Asmann
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Debra A Bell
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Takayo Ota
- Department of Internal Medicine, Rinku General Medical Center, Izumi-sano, 598-8577, Japan
| | - Yaman Tarabishy
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Rui Kuang
- Department of Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN 55414, USA
| | | | | | | | - Elizabeth M Swisher
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA 98109, USA
| | - John Peden
- Illumina Cambridge Ltd, Little Chesterford, Essex CB10 1, UK
| | - David Bentley
- Illumina Cambridge Ltd, Little Chesterford, Essex CB10 1, UK
| | | | | | - Lynn C Hartmann
- Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Viji Shridhar
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen L Goode
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
13
|
Wang W, Wang W, Sun W, Crowley JJ, Szatkiewicz JP. Allele-specific copy-number discovery from whole-genome and whole-exome sequencing. Nucleic Acids Res 2015; 43:e90. [PMID: 25883151 PMCID: PMC4538801 DOI: 10.1093/nar/gkv319] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Accepted: 03/27/2015] [Indexed: 11/14/2022] Open
Abstract
Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/.
Collapse
Affiliation(s)
- WeiBo Wang
- Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599-3175, USA
| | - Wei Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095, USA
| | - Wei Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599-7400, USA
| | - James J Crowley
- Department of Genetics, University of North Carolina at Chapel Hill, NC 27599-7264, USA
| | - Jin P Szatkiewicz
- Department of Genetics, University of North Carolina at Chapel Hill, NC 27599-7264, USA
| |
Collapse
|
14
|
Kuilman T, Velds A, Kemper K, Ranzani M, Bombardelli L, Hoogstraat M, Nevedomskaya E, Xu G, de Ruiter J, Lolkema MP, Ylstra B, Jonkers J, Rottenberg S, Wessels LF, Adams DJ, Peeper DS, Krijgsman O. CopywriteR: DNA copy number detection from off-target sequence data. Genome Biol 2015; 16:49. [PMID: 25887352 PMCID: PMC4396974 DOI: 10.1186/s13059-015-0617-1] [Citation(s) in RCA: 152] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2015] [Accepted: 02/20/2015] [Indexed: 12/13/2022] Open
Abstract
Current methods for detection of copy number variants (CNV) and aberrations (CNA) from targeted sequencing data are based on the depth of coverage of captured exons. Accurate CNA determination is complicated by uneven genomic distribution and non-uniform capture efficiency of targeted exons. Here we present CopywriteR, which eludes these problems by exploiting 'off-target' sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. CopywriteR outperforms existing methods and constitutes a widely applicable alternative to available tools.
Collapse
Affiliation(s)
- Thomas Kuilman
- Division of Molecular Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| | - Arno Velds
- Central Genomic Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Kristel Kemper
- Division of Molecular Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| | - Marco Ranzani
- Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.
| | - Lorenzo Bombardelli
- Division of Molecular Genetics, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Marlous Hoogstraat
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Ekaterina Nevedomskaya
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Guotai Xu
- Division of Molecular Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| | - Julian de Ruiter
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Martijn P Lolkema
- Center for Personalized Cancer Treatment, Amsterdam, The Netherlands.
| | - Bauke Ylstra
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.
| | - Jos Jonkers
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Sven Rottenberg
- Division of Molecular Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Vetsuisse Faculty, Institute of Animal Pathology, University of Bern, Bern, Switzerland.
| | - Lodewyk F Wessels
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - David J Adams
- Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.
| | - Daniel S Peeper
- Division of Molecular Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| | - Oscar Krijgsman
- Division of Molecular Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| |
Collapse
|