ENVE: A novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients

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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

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Varadan, V., Singh, S., Nosrati, A., Ravi, L., Lutterbaugh, J., Barnholtz-Sloan, J. S., … Guda, K. (2015). ENVE: A novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients. Genome Medicine, 7(1). https://doi.org/10.1186/s13073-015-0192-9

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