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Copy Number Variants

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Cover of 'Copy Number Variants'

Table of Contents

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    Book Overview
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    Chapter 1 Identification of Copy Number Variants from SNP Arrays Using PennCNV
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    Chapter 2 Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Next Generation Sequencing and SNP Array Data
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    Chapter 3 Statistical Detection of Genome Differences Based on CNV Segments
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    Chapter 4 Whole-Genome Shotgun Sequence CNV Detection Using Read Depth
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    Chapter 5 Read Depth Analysis to Identify CNV in Bacteria Using CNOGpro
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    Chapter 6 Using HaMMLET for Bayesian Segmentation of WGS Read-Depth Data
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    Chapter 7 Split-Read Indel and Structural Variant Calling Using PINDEL
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    Chapter 8 Detecting Small Inversions Using SRinversion
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    Chapter 9 Detection of CNVs in NGS Data Using VS-CNV
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    Chapter 10 Structural Variant Breakpoint Detection with novoBreak
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    Chapter 11 Use of RAPTR-SV to Identify SVs from Read Pairing and Split Read Signatures
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    Chapter 12 Versatile Identification of Copy Number Variants with Canvas
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    Chapter 13 A Randomized Iterative Approach for SV Discovery with SVelter
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    Chapter 14 Analysis of Population-Genetic Properties of Copy Number Variations
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    Chapter 15 Validation of Genomic Structural Variants Through Long Sequencing Technologies
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    Chapter 16 Structural Variation Detection and Analysis Using Bionano Optical Mapping
Attention for Chapter 11: Use of RAPTR-SV to Identify SVs from Read Pairing and Split Read Signatures
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Chapter title
Use of RAPTR-SV to Identify SVs from Read Pairing and Split Read Signatures
Chapter number 11
Book title
Copy Number Variants
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8666-8_11
Pubmed ID
Book ISBNs
978-1-4939-8665-1, 978-1-4939-8666-8
Authors

Derek M. Bickhart, Bickhart, Derek M.

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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Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 100%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 100%