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Copy Number Variants

Overview of attention for book
Cover of 'Copy Number Variants'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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
  4. Altmetric Badge
    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
  17. Altmetric Badge
    Chapter 16 Structural Variation Detection and Analysis Using Bionano Optical Mapping
Attention for Chapter 15: Validation of Genomic Structural Variants Through Long Sequencing Technologies
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Chapter title
Validation of Genomic Structural Variants Through Long Sequencing Technologies
Chapter number 15
Book title
Copy Number Variants
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8666-8_15
Pubmed ID
30039374
Book ISBNs
978-1-4939-8665-1, 978-1-4939-8666-8
Authors

Xuefang Zhao, Zhao, Xuefang

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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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 50%
Other 1 25%
Researcher 1 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 50%
Computer Science 1 25%
Agricultural and Biological Sciences 1 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 July 2018.
All research outputs
#20,527,576
of 23,096,849 outputs
Outputs from Methods in molecular biology
#9,977
of 13,208 outputs
Outputs of similar age
#378,510
of 442,670 outputs
Outputs of similar age from Methods in molecular biology
#1,194
of 1,499 outputs
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So far Altmetric has tracked 13,208 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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