| 书目名称 | Copy Number Variants | | 副标题 | Methods and Protocol | | 编辑 | Derek M. Bickhart | | 视频video | http://file.papertrans.cn/239/238187/238187.mp4 | | 概述 | Includes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts | | 丛书名称 | Methods in Molecular Biology | | 图书封面 |  | | 描述 | .This volume offers detailed step-by-step instructions to allow beginners and experts alike to run appropriate copy number variants (CNV) detection software on a dataset of choice and discern between false positive noise and true positive CNV signals. Chapters guide readers through single nucleotide polymorphism (SNP) chips, optical mapping assembly techniques, and current open-source programs specializing in CNV detection. Written in the highly successful .Methods in Molecular Biology .series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls..Authoritative and cutting-edge, .Copy Number Variants: Methods and Protocols .aims to provide guidance to Bioinformaticians and Molecular Biologists who are interested in identifying copy number variants (CNV) with a wide variety of experimental media | | 出版日期 | Book 2018 | | 关键词 | PBHoney; DeAnnCNV; MELT; PINDEL; aCGH | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4939-8666-8 | | isbn_softcover | 978-1-4939-9359-8 | | isbn_ebook | 978-1-4939-8666-8Series ISSN 1064-3745 Series E-ISSN 1940-6029 | | issn_series | 1064-3745 | | copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Front Matter |
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Abstract
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,Identification of Copy Number Variants from SNP Arrays Using PennCNV, |
Li Fang,Kai Wang |
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Abstract
High-resolution single-nucleotide polymorphism (SNP) genotyping arrays offer a sensitive and affordable method for genome-wide detection of copy number variants (CNVs). PennCNV is a hidden Markov model (HMM)-based CNV caller for SNP arrays, first released 10 years ago. A typical CNV calling procedure using PennCNV includes preparation of input files, CNV calling, filtering CNV calls, CNV annotation, and CNV visualization. Here we describe several protocols for CNV calling using PennCNV, together with descriptions on several recent improvements to the software tool.
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,Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Ne |
Zhongyang Zhang,Ke Hao |
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Abstract
Somatic copy number alterations (SCNAs) are profound in cancer genomes at different stages: oncogenesis, progression, and metastasis. Accurate detection and characterization of SCNA landscape at genome-wide scale are of great importance. Next-generation sequencing and SNP array are current technology of choice for SCNA analysis. They are able to quantify SCNA with high resolution and meanwhile raise great challenges in data analysis. To this end, we have developed an R package . for SCNA analysis using (1) whole-genome sequencing (WGS), (2) whole-exome sequencing (WES) or (3) whole-genome SNP array data. In this chapter, we provide the features of the package and step-by-step instructions in detail.
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,Statistical Detection of Genome Differences Based on CNV Segments, |
Yang Zhou,Derek M. Bickhart,George E. Liu |
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Abstract
Population analysis using copy number variation (CNV) is far more complex than analysis using SNPs because of the diverse copy number and inconsistent boundaries of CNVs in different individuals that causes changes in frequency. Multiple studies have reported CNV regions associated with diseases or body traits based on a CNV segmentation strategy that condenses calls from multiple different sources into a genotype state. Here, we provide a guideline of how to generate CNV segments from known CNV results, and how to detect genome differences based on CNV segments.
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,Whole-Genome Shotgun Sequence CNV Detection Using Read Depth, |
Fatma Kahveci,Can Alkan |
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Abstract
With the developments in high-throughput sequencing (HTS) technologies, researchers have gained a powerful tool to identify structural variants (SVs) in genomes with substantially less cost than before. SVs can be broadly classified into two main categories: balanced rearrangements and copy number variations (CNVs). Many algorithms have been developed to characterize CNVs using HTS data, with focus on different types and size range of variants using different read signatures. Read depth (RD) based tools are more common in characterizing large (>10 kb) CNVs since RD strategy does not rely on the fragment size and read length, which are limiting factors in read pair and split read analysis. Here we provide a guideline for a user friendly tool for detecting large segmental duplications and deletions that can also predict integer copy numbers for duplicated genes.
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,Read Depth Analysis to Identify CNV in Bacteria Using CNOGpro, |
Ola Brynildsrud |
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Abstract
Whole-genome sequencing with short-read technologies is well suited for calling single nucleotide polymorphisms, but has major problems with the detection of structural variants larger than the read length. One such type of variation is copy number variation (CNV), which entails deletion or duplication of genomic regions, and the expansion or contraction of repeated elements. Duplicated and deleted regions will typically be collapsed during de novo assembly of sequence data, or ignored when mapping reads toward a reference. However, signatures of the copy number variation can be detected in the resultant read depth at each position in the genome. We here provide instructions on how to analyze this read depth signal with the R package CNOGpro, allowing for estimation of copy numbers with uncertainty for each feature in a genome.
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,Using HaMMLET for Bayesian Segmentation of WGS Read-Depth Data, |
John Wiedenhoeft,Alexander Schliep |
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Abstract
CNV detection requires a high-quality segmentation of genomic data. In many WGS experiments, sample and control are sequenced together in a multiplexed fashion using DNA barcoding for economic reasons. Using the differential read depth of these two conditions cancels out systematic additive errors. Due to this detrending, the resulting data is appropriate for inference using a hidden Markov model (HMM), arguably one of the principal models for labeled segmentation. However, while the usual frequentist approaches such as Baum-Welch are problematic for several reasons, they are often preferred to Bayesian HMM inference, which normally requires prohibitively long running times and exceeds a typical user’s computational resources on a genome scale data. HaMMLET solves this problem using a dynamic wavelet compression scheme, which makes Bayesian segmentation of WGS data feasible on standard consumer hardware.
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,Split-Read Indel and Structural Variant Calling Using ,, |
Kai Ye,Li Guo,Xiaofei Yang,Eric-Wubbo Lamijer,Keiran Raine,Zemin Ning |
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Abstract
Genetic variations are important evolutionary forces in all forms of life in nature. Accurate and efficient detection of various forms of genetic variants is crucial for understanding cell function, evolution and diseases in living organisms. In this chapter, we describe a detailed protocol that uses ., a split-read algorithm, to discover indels and structural variants in a given genome, from Illumina short-read sequencing data produced from biological samples.
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,Detecting Small Inversions Using SRinversion, |
Ruoyan Chen,Yu Lung Lau,Wanling Yang |
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Abstract
Rapid development of next generation sequencing (NGS) technology has substantially improved our ability to detect genomic variations. However, unlike other variations, such as point mutations, insertions, and deletions, which can be identified in high sensitivities and specificities based on NGS reads, most of inversions, especially those shorter than 1 kb, remain difficult to detect. Here we introduce a new framework, SRinversion, which was developed specifically for detection of inversions shorter than 1 kb by splitting and realigning poorly mapped or unmapped reads of the NGS data.
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,Detection of CNVs in NGS Data Using VS-CNV, |
Nathan Fortier,Gabe Rudy,Andreas Scherer |
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Abstract
Copy number variations have been linked to numerous genetic diseases including cancer, Parkinson’s disease, pancreatitis, and lupus. While current best practices for CNV detection often require using microarrays for detecting large CNVs or multiplex ligation-dependent probe amplification (MLPA) for gene-sized CNVs, new methods have been developed with the goal of replacing both of these specialized assays with bioinformatic analysis applied to next-generation sequencing (NGS) data. Because NGS is already used by clinical labs to detect small coding variants, this approach reduces associated costs, resources, and analysis time. This chapter provides an overview of the various approaches to CNV detection via NGS data, and examines VS-CNV, a commercial tool developed by Golden Helix, which provides robust CNV calling capabilities for both gene panel and exome data.
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,Structural Variant Breakpoint Detection with novoBreak, |
Zechen Chong,Ken Chen |
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Abstract
Structural variations (SVs) are an important type of genomic variants and always play a critical role for cancer development and progression. In the cancer genomics era, detecting structural variations from short sequencing data is still challenging. We developed a novel algorithm, novoBreak (Chong et al. Nat Methods 14:65–67, 2017), which achieved the highest balanced accuracy (mean of sensitivity and precision) in the ICGC-TCGA DREAM 8.5 Somatic Mutation Calling Challenge. Here we describe detailed instructions of applying novoBreak (.), an open-source software, for somatic SVs detection. We also briefly introduce how to detect germline SVs using novoBreak pipeline and how to use the Workflow (.) of novoBreak on the Seven Bridges Cancer Genomics Cloud.
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,Use of RAPTR-SV to Identify SVs from Read Pairing and Split Read Signatures, |
Derek M. Bickhart |
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Abstract
High-throughput short read sequencing technologies are still the leading cost-effective means of assessing variation in individual samples. Unfortunately, while such technologies are eminently capable of detecting single nucleotide polymorphisms (SNP) and small insertions and deletions, the detection of large copy number variants (CNV) with these technologies is prone to numerous false positives. CNV detection tools that incorporate multiple variant signals and exclude regions of systemic bias in the genome tend to reduce the probability of false positive calls and therefore represent the best means of ascertaining true CNV regions. To this end, we provide instructions and details on the use of the RAPTR-SV CNV detection pipeline, which is a tool that incorporates read-pair and split-read signals to identify high confidence CNV regions in a sequenced sample. By combining two different structural variant (SV) signals in variant calling, RAPTR-SV enables the easy filtration of artifact CNV calls from large datasets.
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,Versatile Identification of Copy Number Variants with Canvas, |
Sergii Ivakhno,Eric Roller |
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Abstract
Versatile and efficient variant calling tools are needed to analyze large-scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy, polyclonality in cancer samples, or frequency of de novo variation in germline genomes of pedigrees. The frequent need of core sequencing facilities to process samples from both normal and tumor sources favors multipurpose variant calling tools with functionality to process these diverse sets within a single software framework. This not only simplifies the overall bioinformatics workflow but also streamlines maintenance by shortening the software update cycle and requiring only limited staff training. Here we introduce Canvas, a tool for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor–normal, small pedigree, and single-sample normal resequencing, as well as whole-exome matched and unmatched tumor–normal studies. In addition to variant calling, Canvas infers genome-wide parameters s
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,A Randomized Iterative Approach for SV Discovery with SVelter, |
Xuefang Zhao |
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Abstract
Genomic structural variants (SVs) are major sources of genome diversity, and numerous studies over the past few decades have shown the impact this class of genetic variation has had on human health and disease. In spite of the recent advances in sequencing technology and discovery methodology, there are still considerable amount of variants in the genome that are partially or completely misinterpreted. The computational tool introduced in this chapter, SVelter, is specifically designed to detect and resolve genomic SVs in all different formats, including the canonical as well as the complex.
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,Analysis of Population-Genetic Properties of Copy Number Variations, |
Lingyang Xu,Liu Yang,Derek M. Bickhart,JunYa Li,George E. Liu |
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Abstract
While single nucleotide polymorphisms (SNPs) are typically the variant of choice for population genetics, copy number variations (CNVs) which comprise insertions, deletions and duplications of genomic sequences, is also an informative type of genetic variation. CNVs have been shown to be both common in mammals and important for understanding the relationship between genotype and phenotype. Moreover, population-specific CNVs are candidate regions under selection and are potentially responsible for diverse phenotypes.
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,Validation of Genomic Structural Variants Through Long Sequencing Technologies, |
Xuefang Zhao |
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Abstract
Although numerous algorithms have been developed to identify large chromosomal rearrangements (i.e., genomic structural variants, SVs), there remains a dearth of approaches to evaluate their results. This is significant, as the accurate identification of SVs is still an outstanding problem whereby no single algorithm has been shown to be able to achieve high sensitivity and specificity across different classes of SVs. The method introduced in this chapter, VaPoR, is specifically designed to evaluate the accuracy of SV predictions using third-generation long sequences. This method uses a recurrence approach and collects direct evidence from raw reads thus avoiding computationally costly whole genome assembly. This chapter would describe in detail as how to apply this tool onto different data types.
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,Structural Variation Detection and Analysis Using Bionano Optical Mapping, |
Saki Chan,Ernest Lam,Michael Saghbini,Sven Bocklandt,Alex Hastie,Han Cao,Erik Holmlin,Mark Borodkin |
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Abstract
The need to accurately identify the complete structural variation profile of genomes is becoming increasingly evident. In contrast to reference-based methods like sequencing or comparative methods like aCGH, optical mapping is a de novo assembly-based method that enables better realization of true genomic structure. It allows for independently detecting balanced and unbalanced structural variants (SVs) from separate alleles and for discovering de novo events. Here we show how Bionano Genome Mapping creates de novo assemblies from native and intact, megabase-scale DNA molecules and uses those assemblies to detect a wide range of structural variants.
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,Correction to: Analysis of Population-Genetic Properties of Copy Number Variations, |
Lingyang Xu,Liu Yang,Derek M. Bickhart,JunYa Li,George E. Liu |
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Abstract
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Back Matter |
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