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
- Accounts for and models both technical- and sample-associated variability in whole-exome sequencing data using non-tumor diploid normal samples
- Reliably detects copy-number alterations among clinical samples with varying stromal admixture without the need for estimating tumor ploidy or content
- Performs robust somatic copy-number estimation using either matched or unmatched tumor-normal samples in an unbiased manner
- By use of these learned model parameters, to evaluate somatic copy-number alterations in tumor samples.
- Requires no a priori parameters or user-intervention.
- Is applicable across deep sequencing platforms and tissue types.
- Is implemented to be easily portable, requiring lightweight computational resources ____________________________________________________________________________________ #### Bugs Fixed
(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.