A Comprehensive Workflow for Read Depth-Based Identification of Copy-Number Variation from Whole-Genome Sequence Data

A remaining hurdle to whole-genome sequencing (WGS) becoming a first-tier genetic test has been accurate detection of copy-number variations (CNVs). Here, we used several datasets to empirically develop a detailed workflow for identifying germline CNVs>1 kb from  short-read WGS data using read depth-based algorithms. Our workflow is comprehensive in that it addresses all stages of the CNV-detection process, including DNA library preparation, sequencing, quality control, reference mapping, and computational CNV identification.

Source: The American Journal of Human Genetics - January 4, 2018 Category: Genetics & Stem Cells Authors: Brett Trost, Susan Walker, Zhuozhi Wang, Bhooma Thiruvahindrapuram, Jeffrey R. MacDonald, Wilson W.L. Sung, Sergio L. Pereira, Joe Whitney, Ada J.S. Chan, Giovanna Pellecchia, Miriam S. Reuter, Si Lok, Ryan K.C. Yuen, Christian R. Marshall, Daniele Merico Tags: Article Source Type: research