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Extreme Value Distribution Based Somatic Copy-Number Variation Estimation

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ENVE is a novel and robust methodology for detecting somatic copy-number alterations in massively parallel DNA sequencing data derived from stromal-admixed clinical samples. ENVE models inherent noise in whole-exome sequencing data using non-malignant tissue samples, and utilizes the learned model for robust estimation of sCNAs in tumors.

Salient features and merits of ENVE methodology

(May 2016) : Fixed bug in PreENVE module's parsing of sample_info file.


Varadan, Vinay, Salendra Singh, Arman Nosrati, Lakshmeswari Ravi, James Lutterbaugh, Jill S. Barnholtz-Sloan, Sanford D. Markowitz, Joseph E. Willis, and Kishore Guda. "ENVE: a novel computational framework characterizes copy-number mutational landscapes in African American colon cancers." Genome Medicine 7, no. 1 (2015): 69.

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