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A Streamlined Method for Detecting Structural Variants in Cancer Genomes by Short Read Paired-End Sequencing

Overview of attention for article published in PLOS ONE, October 2012
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Title
A Streamlined Method for Detecting Structural Variants in Cancer Genomes by Short Read Paired-End Sequencing
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0048314
Pubmed ID
Authors

Martina Mijušković, Stuart M. Brown, Zuojian Tang, Cory R. Lindsay, Efstratios Efstathiadis, Ludovic Deriano, David B. Roth

Abstract

Defining the architecture of a specific cancer genome, including its structural variants, is essential for understanding tumor biology, mechanisms of oncogenesis, and for designing effective personalized therapies. Short read paired-end sequencing is currently the most sensitive method for detecting somatic mutations that arise during tumor development. However, mapping structural variants using this method leads to a large number of false positive calls, mostly due to the repetitive nature of the genome and the difficulty of assigning correct mapping positions to short reads. This study describes a method to efficiently identify large tumor-specific deletions, inversions, duplications and translocations from low coverage data using SVDetect or BreakDancer software and a set of novel filtering procedures designed to reduce false positive calls. Applying our method to a spontaneous T cell lymphoma arising in a core RAG2/p53-deficient mouse, we identified 40 validated tumor-specific structural rearrangements supported by as few as 2 independent read pairs.

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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 %
Spain 1 2%
Sweden 1 2%
France 1 2%
Brazil 1 2%
Unknown 57 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 39%
Student > Ph. D. Student 17 28%
Professor 5 8%
Student > Bachelor 4 7%
Other 3 5%
Other 7 11%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 49%
Biochemistry, Genetics and Molecular Biology 10 16%
Computer Science 7 11%
Medicine and Dentistry 5 8%
Engineering 3 5%
Other 3 5%
Unknown 3 5%