↓ Skip to main content

PLOS

Chapter 6: Structural Variation and Medical Genomics

Overview of attention for article published in PLoS Computational Biology, December 2012
Altmetric Badge

Mentioned by

twitter
7 X users
facebook
1 Facebook page

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
235 Mendeley
citeulike
7 CiteULike
Title
Chapter 6: Structural Variation and Medical Genomics
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002821
Pubmed ID
Authors

Benjamin J. Raphael

Abstract

Differences between individual human genomes, or between human and cancer genomes, range in scale from single nucleotide variants (SNVs) through intermediate and large-scale duplications, deletions, and rearrangements of genomic segments. The latter class, called structural variants (SVs), have received considerable attention in the past several years as they are a previously under appreciated source of variation in human genomes. Much of this recent attention is the result of the availability of higher-resolution technologies for measuring these variants, including both microarray-based techniques, and more recently, high-throughput DNA sequencing. We describe the genomic technologies and computational techniques currently used to measure SVs, focusing on applications in human and cancer genomics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users 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 235 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
United Kingdom 5 2%
Brazil 4 2%
Spain 3 1%
Germany 2 <1%
Italy 2 <1%
Korea, Republic of 1 <1%
Sweden 1 <1%
India 1 <1%
Other 5 2%
Unknown 204 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 59 25%
Researcher 58 25%
Student > Master 22 9%
Student > Bachelor 22 9%
Professor > Associate Professor 15 6%
Other 39 17%
Unknown 20 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 95 40%
Biochemistry, Genetics and Molecular Biology 46 20%
Computer Science 23 10%
Medicine and Dentistry 20 9%
Engineering 6 3%
Other 18 8%
Unknown 27 11%