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CNAReporter: a GenePattern pipeline for the generation of clinical reports of genomic alterations.

Overview of attention for article published in BMC Medical Genomics, January 2010
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CNAReporter: a GenePattern pipeline for the generation of clinical reports of genomic alterations.
Published in
BMC Medical Genomics, January 2010
DOI 10.1186/1755-8794-3-11
Pubmed ID

Kotliarov, Yuri, Bozdag, Serdar, Cheng, Hangjiong, Wuchty, Stefan, Zenklusen, Jean-Claude, Fine, Howard A


Genomic copy number alterations are widely associated with a broad range of human tumors and offer the potential to be used as a diagnostic tool. Especially in the emerging era of personalized medicine medical informatics tools that allow the fast visualization and analysis of genomic alterations of a patient's genomic profile for diagnostic and potential treatment purposes increasingly gain importance. We developed CNAReporter, a software tool that allows users to visualize SNP-specific data obtained from Affymetrix arrays and generate PDF-reports as output. We combined standard algorithms for the analysis of chromosomal alterations, utilizing the widely applied GenePattern framework. As an example, we show genome analyses of two patients with distinctly different CNA profiles using the tool. Glioma subtypes, characterized by different genomic alterations, are often treated differently but can be difficult to differentiate pathologically. CNAReporter offers a user-friendly way to visualize and analyse genomic changes of any given tumor genomic profile, thereby leading to an accurate diagnosis and patient-specific treatment.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 3%
Brazil 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 20%
Professor 5 17%
Student > Bachelor 4 13%
Student > Ph. D. Student 4 13%
Professor > Associate Professor 3 10%
Other 6 20%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 37%
Medicine and Dentistry 6 20%
Computer Science 5 17%
Biochemistry, Genetics and Molecular Biology 2 7%
Engineering 2 7%
Other 1 3%
Unknown 3 10%