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Copy Number Variants

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

Table of Contents

  1. Altmetric Badge
    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
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    Chapter 16 Structural Variation Detection and Analysis Using Bionano Optical Mapping
Attention for Chapter 16: Structural Variation Detection and Analysis Using Bionano Optical Mapping
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

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60 Mendeley
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Chapter title
Structural Variation Detection and Analysis Using Bionano Optical Mapping
Chapter number 16
Book title
Copy Number Variants
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8666-8_16
Pubmed ID
30039375
Book ISBNs
978-1-4939-8665-1, 978-1-4939-8666-8
Authors

Saki Chan, Ernest Lam, Michael Saghbini, Sven Bocklandt, Alex Hastie, Han Cao, Erik Holmlin, Mark Borodkin, Chan, Saki, Lam, Ernest, Saghbini, Michael, Bocklandt, Sven, Hastie, Alex, Cao, Han, Holmlin, Erik, Borodkin, Mark

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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 17%
Student > Ph. D. Student 9 15%
Student > Master 8 13%
Student > Bachelor 5 8%
Student > Doctoral Student 4 7%
Other 6 10%
Unknown 18 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 28%
Agricultural and Biological Sciences 7 12%
Medicine and Dentistry 6 10%
Computer Science 3 5%
Engineering 3 5%
Other 5 8%
Unknown 19 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 February 2022.
All research outputs
#13,113,968
of 23,114,117 outputs
Outputs from Methods in molecular biology
#3,335
of 13,227 outputs
Outputs of similar age
#207,642
of 442,765 outputs
Outputs of similar age from Methods in molecular biology
#283
of 1,499 outputs
Altmetric has tracked 23,114,117 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,227 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,765 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
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 has done well, scoring higher than 79% of its contemporaries.

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