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Genomic Structural Variants

Overview of attention for book
Cover of 'Genomic Structural Variants'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 What Have Studies of Genomic Disorders Taught Us About Our Genome?
  3. Altmetric Badge
    Chapter 2 Microdeletion and microduplication syndromes.
  4. Altmetric Badge
    Chapter 3 Structural Genomic Variation in Intellectual Disability
  5. Altmetric Badge
    Chapter 4 Copy Number Variation and Psychiatric Disease Risk
  6. Altmetric Badge
    Chapter 5 Detection and characterization of copy number variation in autism spectrum disorder.
  7. Altmetric Badge
    Chapter 6 Structural Variation in Subtelomeres
  8. Altmetric Badge
    Chapter 7 Array-Based Approaches in Prenatal Diagnosis
  9. Altmetric Badge
    Chapter 8 Structural Variation and Its Effect on Expression
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    Chapter 9 The Challenges of Studying Complex and Dynamic Regions of the Human Genome
  11. Altmetric Badge
    Chapter 10 Population Genetic Nature of Copy Number Variation
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    Chapter 11 Detection and interpretation of genomic structural variation in mammals.
  13. Altmetric Badge
    Chapter 12 Structural Genetic Variation in the Context of Somatic Mosaicism
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    Chapter 13 Online resources for genomic structural variation.
  15. Altmetric Badge
    Chapter 14 Algorithm Implementation for CNV Discovery Using Affymetrix and Illumina SNP Array Data
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    Chapter 15 Targeted Screening and Validation of Copy Number Variations
  17. Altmetric Badge
    Chapter 16 High-Resolution Copy Number Profiling by Array CGH Using DNA Isolated from Formalin-Fixed, Paraffin-Embedded Tissues
  18. Altmetric Badge
    Chapter 17 Characterizing and Interpreting Genetic Variation from Personal Genome Sequencing
  19. Altmetric Badge
    Chapter 18 Massively Parallel Sequencing Approaches for Characterization of Structural Variation
Attention for Chapter 11: Detection and interpretation of genomic structural variation in mammals.
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

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Chapter title
Detection and interpretation of genomic structural variation in mammals.
Chapter number 11
Book title
Genomic Structural Variants
Published in
Methods in molecular biology, January 2012
DOI 10.1007/978-1-61779-507-7_11
Pubmed ID
Book ISBNs
978-1-61779-506-0, 978-1-61779-507-7

Hall IM, Quinlan AR, Ira M. Hall, Aaron R. Quinlan, Hall, Ira M., Quinlan, Aaron R.


Lars Feuk


Structural variation (SV) encompasses diverse types of genomic variants including deletions, duplications, inversions, transpositions, translocations, and complex rearrangements, and is now recognized to be an abundant class of genetic variation in mammals. Different individuals, or strains, of a given species can differ by thousands of variants. However, despite a large number of studies over the past decade and impressive progress on many fronts, there remain significant gaps in our knowledge, particularly in species other than human. Arguably the most relevant among these are genetically tractable models such as mouse, rat, and dog. The emergence of efficient and affordable DNA sequencing technologies presents an opportunity to make rapid progress toward understanding the nature, origin, and function of SV in these, and other, domesticated species. Here, we summarize the current state of knowledge of SV in mammals, with a focus on the similarities and differences between domesticated species and human. We then present methods to identify SV breakpoints from next-generation sequence (NGS) data by paired-end mapping, split-read mapping, and local assembly, and discuss challenges that arise when interpreting these data in lineages with complex breeding histories and incomplete reference genomes. We further describe technical modifications that allow for identification of variants involving repetitive DNA elements such as transposons and segmental duplications. Finally, we explore a few of the key biological insights that can be gained by applying NGS methods to model organisms.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Canada 2 3%
Italy 1 2%
Brazil 1 2%
France 1 2%
Germany 1 2%
Sweden 1 2%
China 1 2%
Poland 1 2%
Other 0 0%
Unknown 50 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 23%
Student > Ph. D. Student 11 18%
Other 5 8%
Professor 4 7%
Student > Bachelor 3 5%
Other 10 16%
Unknown 14 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 43%
Biochemistry, Genetics and Molecular Biology 10 16%
Medicine and Dentistry 7 11%
Computer Science 3 5%
Social Sciences 1 2%
Other 0 0%
Unknown 14 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 06 June 2012.
All research outputs
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Outputs from Methods in molecular biology
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Outputs of similar age
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Outputs of similar age from Methods in molecular biology
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Altmetric has tracked 22,094,926 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,660 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done particularly well, scoring higher than 96% 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 144,936 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.