Title |
ENVE: a novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients
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Published in |
Genome Medicine, July 2015
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DOI | 10.1186/s13073-015-0192-9 |
Pubmed ID | |
Authors |
Vinay Varadan, Salendra Singh, Arman Nosrati, Lakshmeswari Ravi, James Lutterbaugh, Jill S. Barnholtz-Sloan, Sanford D. Markowitz, Joseph E. Willis, Kishore Guda |
Abstract |
Reliable detection of somatic copy-number alterations (sCNAs) in tumors using whole-exome sequencing (WES) remains challenging owing to technical (inherent noise) and sample-associated variability in WES data. We present a novel computational framework, ENVE, which models inherent noise in any WES dataset, enabling robust detection of sCNAs across WES platforms. ENVE achieved high concordance with orthogonal sCNA assessments across two colorectal cancer (CRC) WES datasets, and consistently outperformed a best-in-class algorithm, Control-FREEC. We subsequently used ENVE to characterize global sCNA landscapes in African American CRCs, identifying genomic aberrations potentially associated with CRC pathogenesis in this population. ENVE is downloadable at https://github.com/ENVE-Tools/ENVE. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 17% |
United States | 1 | 17% |
Germany | 1 | 17% |
Unknown | 3 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 50% |
Scientists | 3 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Canada | 1 | 3% |
Unknown | 29 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 10 | 33% |
Student > Bachelor | 5 | 17% |
Student > Ph. D. Student | 3 | 10% |
Other | 2 | 7% |
Professor | 2 | 7% |
Other | 6 | 20% |
Unknown | 2 | 7% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 10 | 33% |
Agricultural and Biological Sciences | 6 | 20% |
Engineering | 4 | 13% |
Computer Science | 3 | 10% |
Medicine and Dentistry | 2 | 7% |
Other | 3 | 10% |
Unknown | 2 | 7% |