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COPS: A Sensitive and Accurate Tool for Detecting Somatic Copy Number Alterations Using Short-Read Sequence Data from Paired Samples

Overview of attention for article published in PLOS ONE, October 2012
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Title
COPS: A Sensitive and Accurate Tool for Detecting Somatic Copy Number Alterations Using Short-Read Sequence Data from Paired Samples
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0047812
Pubmed ID
Authors

Neeraja M. Krishnan, Prakhar Gaur, Rakshit Chaudhary, Arjun A. Rao, Binay Panda

Abstract

Copy Number Alterations (CNAs) such as deletions and duplications; compose a larger percentage of genetic variations than single nucleotide polymorphisms or other structural variations in cancer genomes that undergo major chromosomal re-arrangements. It is, therefore, imperative to identify cancer-specific somatic copy number alterations (SCNAs), with respect to matched normal tissue, in order to understand their association with the disease. We have devised an accurate, sensitive, and easy-to-use tool, COPS, COpy number using Paired Samples, for detecting SCNAs. We rigorously tested the performance of COPS using short sequence simulated reads at various sizes and coverage of SCNAs, read depths, read lengths and also with real tumor:normal paired samples. We found COPS to perform better in comparison to other known SCNA detection tools for all evaluated parameters, namely, sensitivity (detection of true positives), specificity (detection of false positives) and size accuracy. COPS performed well for sequencing reads of all lengths when used with most upstream read alignment tools. Additionally, by incorporating a downstream boundary segmentation detection tool, the accuracy of SCNA boundaries was further improved. Here, we report an accurate, sensitive and easy to use tool in detecting cancer-specific SCNAs using short-read sequence data. In addition to cancer, COPS can be used for any disease as long as sequence reads from both disease and normal samples from the same individual are available. An added boundary segmentation detection module makes COPS detected SCNA boundaries more specific for the samples studied. COPS is available at ftp://115.119.160.213 with username "cops" and password "cops".

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Geographical breakdown

Country Count As %
Brazil 2 6%
Spain 1 3%
Sweden 1 3%
United States 1 3%
Unknown 29 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 32%
Student > Ph. D. Student 8 24%
Professor > Associate Professor 3 9%
Student > Master 3 9%
Student > Bachelor 2 6%
Other 6 18%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 53%
Biochemistry, Genetics and Molecular Biology 7 21%
Engineering 2 6%
Computer Science 2 6%
Unspecified 1 3%
Other 2 6%
Unknown 2 6%